From bb79bb4bac225e609cafce57d72efdfce498ce09 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Wed, 3 Jun 2026 17:03:43 +0200 Subject: [PATCH 01/74] Add sensitivity-analysis pipeline + per-episode recording Adds a policy-sensitivity analysis stack under isaaclab_arena/analysis/ sensitivity/: a SensitivityDataset loader (factors.yaml + episode JSONL), NPE / MNPE / KDE / empirical analyzers (sbi-backed), continuous + categorical factor support with LogUniform priors, and an interactive Plotly HTML report. eval_runner gains an opt-in --episode_summary flag that appends one JSONL row per recorded episode (full arena_env_args dict + task outcomes); the analyzer decides which arena_env_args keys are factors via factors.yaml, so eval needs no knowledge of "factors". Job now carries arena_env_args_dict so the writer logs typed values. Adds sbi to dev deps. Driver scripts: analyze_sensitivity.py (single factor/outcome) and generate_sensitivity_report.py (full multi-factor HTML deliverable). Signed-off-by: Clemens Volk --- isaaclab_arena/analysis/__init__.py | 4 + .../analysis/sensitivity/__init__.py | 4 + .../analysis/sensitivity/analyzer.py | 501 ++++++++++++ .../analysis/sensitivity/dataset.py | 417 ++++++++++ .../analysis/sensitivity/episode_writer.py | 110 +++ .../analysis/sensitivity/plotting.py | 238 ++++++ isaaclab_arena/analysis/sensitivity/report.py | 736 ++++++++++++++++++ .../sensitivity/synthetic_data_categorical.py | 146 ++++ .../sensitivity/synthetic_data_continuous.py | 182 +++++ isaaclab_arena/evaluation/eval_runner.py | 19 + isaaclab_arena/evaluation/eval_runner_cli.py | 9 + isaaclab_arena/evaluation/job_manager.py | 6 + isaaclab_arena/scripts/analyze_sensitivity.py | 106 +++ .../scripts/generate_sensitivity_report.py | 56 ++ setup.py | 1 + 15 files changed, 2535 insertions(+) create mode 100644 isaaclab_arena/analysis/__init__.py create mode 100644 isaaclab_arena/analysis/sensitivity/__init__.py create mode 100644 isaaclab_arena/analysis/sensitivity/analyzer.py create mode 100644 isaaclab_arena/analysis/sensitivity/dataset.py create mode 100644 isaaclab_arena/analysis/sensitivity/episode_writer.py create mode 100644 isaaclab_arena/analysis/sensitivity/plotting.py create mode 100644 isaaclab_arena/analysis/sensitivity/report.py create mode 100644 isaaclab_arena/analysis/sensitivity/synthetic_data_categorical.py create mode 100644 isaaclab_arena/analysis/sensitivity/synthetic_data_continuous.py create mode 100644 isaaclab_arena/scripts/analyze_sensitivity.py create mode 100644 isaaclab_arena/scripts/generate_sensitivity_report.py diff --git a/isaaclab_arena/analysis/__init__.py b/isaaclab_arena/analysis/__init__.py new file mode 100644 index 0000000000..fee3a6a9f6 --- /dev/null +++ b/isaaclab_arena/analysis/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 diff --git a/isaaclab_arena/analysis/sensitivity/__init__.py b/isaaclab_arena/analysis/sensitivity/__init__.py new file mode 100644 index 0000000000..fee3a6a9f6 --- /dev/null +++ b/isaaclab_arena/analysis/sensitivity/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 diff --git a/isaaclab_arena/analysis/sensitivity/analyzer.py b/isaaclab_arena/analysis/sensitivity/analyzer.py new file mode 100644 index 0000000000..99bf25dbd2 --- /dev/null +++ b/isaaclab_arena/analysis/sensitivity/analyzer.py @@ -0,0 +1,501 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Inference-only analyzers for v0.3 sensitivity analysis. + +What this module does in plain English +-------------------------------------- +Given a dataset of (factor values, outcome values) pairs from a policy evaluation, the +analyzer learns the *conditional* distribution of factor values given a chosen outcome +value (e.g. "given the episode succeeded, which factor values were most consistent?"). +This is the **posterior** ``P(theta | outcome=success)``. Under v0.3's uniform prior, +this posterior's peak is also the operating point ``argmax P(success | theta)`` — so +plotting the marginal posterior over one factor identifies the values that maximize +success rate. + +The three concrete analyzers cover the three relevant factor-mix cases: + + - ``NPEAnalyzer`` — **N**eural **P**osterior **E**stimation. Used when *all* + declared factors are continuous. Trains a normalizing-flow density estimator on + ``(theta, x)`` pairs and exposes ``posterior.sample`` / ``posterior.log_prob``. + Limitation: with a binary outcome and a 1D theta, sbi falls back to a Gaussian + density and the recovered peak reflects the *mean* of successful theta values + rather than the true *mode* — a known caveat we surface as a [WARN] at fit time. + - ``MNPEAnalyzer`` — **M**ixed **N**eural **P**osterior **E**stimation. Used when + the schema has *both* continuous and categorical factors. sbi's MixedDensityEstimator + routes continuous columns through the same kind of flow NPE uses while routing + discrete columns through a categorical mass estimator. + - ``EmpiricalAnalyzer`` — Pure-categorical schemas. Skip the neural fit entirely: under + a uniform prior the posterior ``P(category | success)`` is *exactly* the normalized + per-category empirical success rate. No smoothing improves on that, and sbi MNPE + in version 0.26 also refuses to train without at least one continuous theta column. + +``make_analyzer(dataset, outcome_name)`` is the factory: callers don't need to know about +the hierarchy, they just hand it a dataset and outcome name. + +How rendering fits in +--------------------- +This module is *inference-only*. The sibling ``plotting`` module reads the analyzer's +public queries (``continuous_marginal_density``, ``categorical_marginal_probs``) and +renders matplotlib figures. Decoupling the two means new plot types don't require +analyzer changes, and analyzer changes don't risk breaking the plot. + +Public posterior-query surface used by ``plotting.py``: + - ``BaseAnalyzer.categorical_marginal_probs(factor_name, outcome_value, num_samples)`` + - ``PosteriorAnalyzer.continuous_marginal_density(factor_name, outcome_value, num_grid_points)`` + (NOT defined on ``EmpiricalAnalyzer`` — that analyzer rejects continuous factors at init time) +""" + +from __future__ import annotations + +import numpy as np +import torch +from abc import ABC, abstractmethod + +from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec, SensitivityDataset + + +class BaseAnalyzer(ABC): + """Abstract base — owns state validation and the abstract posterior-query surface. + + Subclasses must implement: + - ``fit`` — train (or no-op) so queries can be called afterwards. + - ``categorical_marginal_probs`` — return ``P(category | outcome)`` for a categorical factor. + Continuous-factor queries (``continuous_marginal_density``) live on ``PosteriorAnalyzer`` + only — the empirical analyzer never needs them by construction. + """ + + def __init__(self, dataset: SensitivityDataset, outcome_name: str): + self.dataset = dataset + self.outcome_name = outcome_name + assert ( + outcome_name in dataset.outcome_columns + ), f"Outcome {outcome_name!r} not found in schema; available: {list(dataset.outcome_columns)}" + assert len(dataset.schema.factors) > 0, "Schema declares no factors" + + @abstractmethod + def fit(self, training_batch_size: int = 50) -> None: + """Train the posterior (or no-op for empirical) so queries can be called afterwards. + + For NPE/MNPE this trains a neural density estimator on ``(theta, x_selected)``, + where ``x_selected`` is the single outcome column named by ``outcome_name``. For + the empirical analyzer this is a no-op — the categorical posterior is computed + directly from the data at query time. + """ + + @abstractmethod + def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: + """Return ``P(category | outcome=outcome_value)`` for one categorical factor. + + Output is a 1D numpy array of length ``len(factor.choices)`` whose entries sum to 1. + For posterior analyzers this is computed by sampling the trained posterior and + counting category frequencies; for the empirical analyzer it's the normalized + per-category empirical success rate. + """ + + def _factor_spec(self, factor_name: str) -> FactorSpec: + """Return the ``FactorSpec`` for ``factor_name``, asserting it exists in the schema.""" + assert ( + factor_name in self.dataset.factor_columns + ), f"Factor {factor_name!r} not in schema; available: {list(self.dataset.factor_columns)}" + return next(factor for factor in self.dataset.schema.factors if factor.name == factor_name) + + +class PosteriorAnalyzer(BaseAnalyzer): + """Common base for the sbi-driven analyzers (NPE and MNPE). + + NPE and MNPE differ only in *which* sbi inference class they instantiate; everything + else (training loop, posterior storage, density and sample queries) is identical. + Subclasses override ``_make_inference`` to choose the class, and the + binary-outcome WARN hook to surface any method-specific caveats. + + After ``fit()`` returns, ``self.posterior`` is an sbi posterior object that supports + ``posterior.sample(shape, x=...)`` and (for NPE) ``posterior.log_prob(theta, x=...)``. + """ + + def __init__(self, dataset: SensitivityDataset, outcome_name: str): + super().__init__(dataset, outcome_name) + self.posterior = None + + def _make_inference(self): + """Return the sbi inference object to train with. + + Subclass-specific: ``NPEAnalyzer`` returns ``sbi.inference.NPE(...)``, + ``MNPEAnalyzer`` returns ``sbi.inference.MNPE(...)``. The lazy import of sbi + lives in the subclass so callers don't pay the (heavy) sbi import cost until + they actually fit. + """ + raise NotImplementedError("PosteriorAnalyzer subclasses must implement _make_inference") + + def fit(self, training_batch_size: int = 50) -> None: + """Train the chosen sbi estimator on ``(theta, x_selected)`` and stash the posterior. + + Steps: + 1. Slice ``self.dataset.x`` to the single outcome column named by ``outcome_name``. + 2. Surface any method-specific caveats about the outcome (e.g. NPE's + 1D-theta Gaussian fallback) via ``_maybe_warn_binary_outcome``. + 3. Instantiate the sbi inference object (NPE or MNPE) via ``_make_inference``. + 4. Append the simulations and train. + 5. Build a posterior object from the trained estimator and store it on ``self``. + """ + outcome_column_index = self.dataset.outcome_columns[self.outcome_name] + selected_outcome_column = self.dataset.x[:, outcome_column_index : outcome_column_index + 1] + self._maybe_warn_binary_outcome(selected_outcome_column) + + print( + f"[INFO] {type(self).__name__}: fitting on {self.dataset.theta.shape[0]} samples" + f" (theta dim={self.dataset.theta.shape[1]}," + f" x dim={selected_outcome_column.shape[1]})." + ) + inference = self._make_inference() + inference.append_simulations(self.dataset.theta, selected_outcome_column) + density_estimator = inference.train(training_batch_size=training_batch_size) + self.posterior = inference.build_posterior(density_estimator) + + def _maybe_warn_binary_outcome(self, selected_outcome_column: torch.Tensor) -> None: + """Optional hook for subclass-specific caveats about the outcome. Default: no-op. + + ``NPEAnalyzer`` overrides this to warn that sbi falls back to a Gaussian density + when *theta* is 1D, biasing the recovered peak toward the mean of successful + theta values rather than the true mode. The fallback fires regardless of how + many outcome columns are logged — it's a property of single-factor analysis. + For 1-continuous-factor + binary-outcome workloads, prefer ``KDEAnalyzer``. + """ + + def continuous_marginal_density( + self, factor_name: str, outcome_value: float, num_grid_points: int + ) -> tuple[np.ndarray, np.ndarray]: + """Evaluate ``P(factor_value | outcome=outcome_value)`` over the factor's prior range. + + Returns ``(grid, density)`` as numpy arrays of length ``num_grid_points``, suitable + for plotting as a smooth curve. + + Two evaluation paths depending on whether other factors are present: + - **1D theta** (the only declared factor is this one): evaluate + ``posterior.log_prob`` directly on a regular grid — exact, no sampling. + - **Multi-dim theta**: sample the posterior at the given outcome value, extract + this factor's column, and histogram-then-interpolate to a grid. This + marginalizes over the other factor dims implicitly. + """ + assert self.posterior is not None, "Call fit() before querying the posterior" + factor_spec = self._factor_spec(factor_name) + assert ( + factor_spec.type == "continuous" + ), f"continuous_marginal_density expects a continuous factor; {factor_name!r} is {factor_spec.type!r}" + assert ( + factor_spec.range is not None and len(factor_spec.range) == 1 + ), "Continuous-factor marginal expects a populated 1D range" + + factor_column_slice = self.dataset.factor_columns[factor_name] + observed_outcome = torch.tensor([outcome_value], dtype=torch.float32) + # ``factor_spec.range`` is always in linear (user-readable) units. For log_uniform + # factors the analyzer's parameter space is log10(theta), so we generate the grid + # in log10 space here and convert it back to linear before returning so the caller + # plots intensities directly on a log-scale x-axis. + is_log_uniform = factor_spec.distribution == "log_uniform" + range_low, range_high = factor_spec.range[0] + analyzer_low = np.log10(range_low) if is_log_uniform else range_low + analyzer_high = np.log10(range_high) if is_log_uniform else range_high + + if self.dataset.theta.shape[1] == 1: + grid_tensor = torch.linspace(analyzer_low, analyzer_high, num_grid_points, dtype=torch.float32).unsqueeze(1) + with torch.no_grad(): + log_probabilities = self.posterior.log_prob(grid_tensor, x=observed_outcome) + density_numpy = torch.exp(log_probabilities).cpu().numpy() + grid_numpy = grid_tensor.squeeze(-1).cpu().numpy() + else: + with torch.no_grad(): + posterior_samples = self.posterior.sample((10_000,), x=observed_outcome) + factor_column_samples = posterior_samples[:, factor_column_slice].squeeze(-1).cpu().numpy() + grid_numpy = np.linspace(analyzer_low, analyzer_high, num_grid_points) + histogram_density, bin_edges = np.histogram( + factor_column_samples, bins=40, range=(analyzer_low, analyzer_high), density=True + ) + density_numpy = np.interp(grid_numpy, 0.5 * (bin_edges[:-1] + bin_edges[1:]), histogram_density) + + if is_log_uniform: + grid_numpy = np.power(10.0, grid_numpy) # log10 → linear for display + return grid_numpy, density_numpy + + def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: + """Estimate ``P(category | outcome)`` by sampling the trained posterior. + + Draws ``num_samples`` from ``posterior(theta | x=outcome_value)``, extracts the + factor's column (which sbi returns as floats over the BoxUniform support), rounds + to the nearest integer in ``[0, num_choices - 1]``, and tallies frequencies. + Result is a length-``num_choices`` numpy array that sums to 1. + """ + assert self.posterior is not None, "Call fit() before querying the posterior" + factor_spec = self._factor_spec(factor_name) + assert factor_spec.type == "categorical" + assert factor_spec.choices is not None + factor_column_slice = self.dataset.factor_columns[factor_name] + num_choices = len(factor_spec.choices) + + observed_outcome = torch.tensor([outcome_value], dtype=torch.float32) + with torch.no_grad(): + posterior_samples = self.posterior.sample((num_samples,), x=observed_outcome) + factor_column_samples = posterior_samples[:, factor_column_slice].squeeze(-1).cpu().numpy() + clipped_codes = np.clip(np.round(factor_column_samples), 0, num_choices - 1).astype(int) + return np.bincount(clipped_codes, minlength=num_choices) / num_samples + + +class NPEAnalyzer(PosteriorAnalyzer): + """Neural Posterior Estimation analyzer for continuous-only factor schemas. + + Use this when every declared factor is continuous (no categoricals). Internally + trains ``sbi.inference.NPE``, which fits a normalizing-flow density over + ``(theta, x_selected)`` and exposes both ``sample`` and ``log_prob`` on the result. + + **Caveat for binary outcomes (1D x):** sbi's flow code falls back to a Gaussian + density when the output space is 1D, which biases the recovered posterior peak + toward the *mean* of successful theta values rather than the true *mode* of the + success curve. We surface a [WARN] at fit time so users see this in plain text + rather than buried in sbi's UserWarning stream. + """ + + def _make_inference(self): + """Construct ``sbi.inference.NPE`` configured with the dataset's uniform prior.""" + from sbi.inference import NPE + + return NPE(prior=self.dataset.prior) + + def _maybe_warn_binary_outcome(self, selected_outcome_column: torch.Tensor) -> None: + """Warn if theta is 1D and the outcome is binary — the configuration that triggers + the sbi Gaussian fallback. Multi-factor theta (dim ≥ 2) escapes the fallback. + """ + if self.dataset.theta.shape[1] > 1: + return + unique_values = set(selected_outcome_column.flatten().tolist()) + if unique_values.issubset({0.0, 1.0}): + print( + f"[WARN] Theta is 1D ({self.dataset.schema.factors[0].name!r}) and outcome" + f" {self.outcome_name!r} is binary. sbi NPE falls back to a Gaussian density" + " in 1D theta space, so the recovered posterior peak reflects the *mean* of" + " successful theta values rather than the true *mode* of the success curve." + " For this configuration prefer KDEAnalyzer (uniform prior + binary outcome" + " → KDE on successful-theta samples is the correct posterior)." + ) + + +class MNPEAnalyzer(PosteriorAnalyzer): + """Mixed Neural Posterior Estimation analyzer for schemas with at least one of each type. + + Use this when the schema mixes continuous and categorical factors. Internally trains + ``sbi.inference.MNPE``, whose mixed density estimator routes continuous theta columns + through a normalizing flow while routing categorical columns through a categorical + mass estimator. The continuous-first / categorical-after column ordering in + ``factor_columns`` matches MNPE's expected layout exactly. + + sbi MNPE 0.26 requires at least one continuous theta column. For pure-categorical + schemas use ``EmpiricalAnalyzer`` instead — ``make_analyzer`` dispatches correctly. + """ + + def _make_inference(self): + """Construct ``sbi.inference.MNPE`` configured with the dataset's uniform prior.""" + from sbi.inference import MNPE + + return MNPE(prior=self.dataset.prior) + + +class EmpiricalAnalyzer(BaseAnalyzer): + """Frequency-table analyzer for pure-categorical factor schemas — no neural fit. + + Use this when every declared factor is categorical. Under v0.3's uniform prior, + Bayes' rule simplifies ``P(category | success) ∝ P(success | category) · P(category)`` + to ``P(category | success) ∝ P(success | category)`` — i.e. the posterior is *exactly* + the per-category empirical success rate, normalized to sum to 1. No neural network + can do better than this with a uniform prior; smoothing only hurts. + + Also covers a sbi limitation: MNPE 0.26 refuses to train if theta has zero continuous + columns. The empirical path sidesteps that entirely. + + Rejects continuous factors at construction time — ``make_analyzer`` shouldn't even + dispatch here for mixed schemas, but the explicit guard makes the constraint clear. + """ + + def __init__(self, dataset: SensitivityDataset, outcome_name: str): + super().__init__(dataset, outcome_name) + has_continuous_factor = any(factor.type == "continuous" for factor in dataset.schema.factors) + assert not has_continuous_factor, ( + "EmpiricalAnalyzer is only valid for all-categorical schemas. For mixed" + " continuous + categorical factors, use MNPEAnalyzer." + ) + + def fit(self, training_batch_size: int = 50) -> None: + """No-op — the posterior is computed directly from the data at query time.""" + print(f"[INFO] {type(self).__name__}: no neural fit needed for pure-categorical schema.") + + def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: + """Return ``P(category | outcome) = per_category_success_rate / sum(per_category_success_rate)``. + + For each category, computes the fraction of rows assigned to it whose outcome + column is ``>= 0.5`` (treating outcome as binary). Then normalizes across + categories so the result sums to 1. ``outcome_value`` and ``num_samples`` are + accepted for interface compatibility with ``PosteriorAnalyzer`` but not used — + empirical analysis treats outcome as binary (success vs not-success). + """ + factor_spec = self._factor_spec(factor_name) + assert factor_spec.type == "categorical" + assert factor_spec.choices is not None + factor_column_slice = self.dataset.factor_columns[factor_name] + num_choices = len(factor_spec.choices) + outcome_column_index = self.dataset.outcome_columns[self.outcome_name] + + empirical_theta_codes = self.dataset.theta[:, factor_column_slice].squeeze(-1).long().cpu().numpy() + empirical_outcomes = self.dataset.x[:, outcome_column_index].cpu().numpy() + empirical_rates = np.zeros(num_choices) + for code in range(num_choices): + category_mask = empirical_theta_codes == code + if category_mask.any(): + empirical_rates[code] = float((empirical_outcomes[category_mask] >= 0.5).mean()) + total_rate = float(empirical_rates.sum()) + if total_rate > 0: + return empirical_rates / total_rate + return np.full(num_choices, 1.0 / num_choices) + + +class KDEAnalyzer(BaseAnalyzer): + """KDE-based analyzer for the 1-continuous-factor + binary-outcome case. + + Under v0.3's uniform prior and a binary outcome, Bayes' rule reduces to + ``P(theta | success=1) ∝ P(success=1 | theta) · P(theta) = P(success=1 | theta) · const``. + The empirical density of *successful*-theta samples (i.e. rows where the chosen outcome + is 1) is directly proportional to ``P(success=1 | theta)``, and a Gaussian KDE over + those samples gives a smoothed estimate of that conditional density. No neural fit, + no Gaussian-shape constraint. + + This is the right primitive for our MVP-1 case (1 continuous factor, binary outcome): + sbi NPE forces a Gaussian shape when theta is 1D — biasing the recovered peak toward + the mean of successful-theta values rather than the true mode of the success curve. + KDE has no such constraint and recovers multi-modal / plateau / skewed shapes faithfully. + + Conceptual sibling of :class:`EmpiricalAnalyzer` (which does the same trick for purely + categorical theta via frequency counts). For multi-factor or non-binary-outcome + workloads, :func:`make_analyzer` dispatches to NPE/MNPE instead. + + Caveats: + - Bandwidth is scipy's Scott rule default; haven't tuned for non-uniform sample + distributions or sparse data. May over-smooth the empirical mode. + - Only ``continuous_marginal_density`` is implemented and only for + ``outcome_value >= 0.5`` (i.e. success conditioning). Failure conditioning would + require fitting a second KDE over failed-theta samples; left out for v0.3 simplicity. + """ + + def __init__(self, dataset: SensitivityDataset, outcome_name: str): + super().__init__(dataset, outcome_name) + num_continuous = sum(1 for factor in dataset.schema.factors if factor.type == "continuous") + num_categorical = sum(1 for factor in dataset.schema.factors if factor.type == "categorical") + assert num_continuous == 1 and num_categorical == 0, ( + f"KDEAnalyzer requires exactly one continuous factor and no categoricals; got {num_continuous} continuous," + f" {num_categorical} categorical." + ) + self._kde = None + self._num_successful_samples = 0 + self._num_total_samples = 0 + + def fit(self, training_batch_size: int = 50) -> None: + """Fit a Gaussian KDE on the successful-theta samples (no neural network involved).""" + from scipy.stats import gaussian_kde + + outcome_column_index = self.dataset.outcome_columns[self.outcome_name] + outcome_values = self.dataset.x[:, outcome_column_index].cpu().numpy() + theta_values = self.dataset.theta[:, 0].cpu().numpy() + success_mask = outcome_values >= 0.5 + self._num_total_samples = int(len(theta_values)) + self._num_successful_samples = int(success_mask.sum()) + + if self._num_successful_samples < 2: + print( + f"[WARN] KDEAnalyzer: only {self._num_successful_samples} successful samples" + f" / {self._num_total_samples} total — KDE undefined, marginal will be uniform." + ) + return + + successful_theta = theta_values[success_mask] + if float(np.std(successful_theta)) < 1e-9: + print( + "[WARN] KDEAnalyzer: all successful theta values are identical — KDE bandwidth" + " degenerate, marginal will be uniform." + ) + return + + self._kde = gaussian_kde(successful_theta) + print( + f"[INFO] KDEAnalyzer: fit Gaussian KDE on {self._num_successful_samples} successful" + f" theta samples / {self._num_total_samples} total." + ) + + def continuous_marginal_density( + self, factor_name: str, outcome_value: float, num_grid_points: int + ) -> tuple[np.ndarray, np.ndarray]: + """Evaluate the KDE-based posterior over the factor's prior range. + + ``outcome_value >= 0.5`` is treated as "success conditioning" (the only case + currently supported); the KDE is evaluated on a uniform grid spanning the + declared factor range. ``outcome_value < 0.5`` (failure conditioning) returns + a uniform density as a placeholder — extend by fitting a second KDE on failed + samples if/when that case is needed. + """ + factor_spec = self._factor_spec(factor_name) + assert factor_spec.type == "continuous", "KDEAnalyzer only handles continuous factors" + assert ( + factor_spec.range is not None and len(factor_spec.range) == 1 + ), "Continuous-factor marginal expects a populated 1D range" + # For log_uniform factors the analyzer's parameter space is log10(theta) (see + # ``dataset._build_factor_tensor``), so the KDE was fit on log10 values and the + # grid we evaluate on must also be in log space. We convert the linear grid back + # for display at the very end so the caller plots intensities directly on a + # log-scale x-axis. + is_log_uniform = factor_spec.distribution == "log_uniform" + range_low, range_high = factor_spec.range[0] + analyzer_low = np.log10(range_low) if is_log_uniform else range_low + analyzer_high = np.log10(range_high) if is_log_uniform else range_high + analyzer_grid = np.linspace(analyzer_low, analyzer_high, num_grid_points) + linear_grid = np.power(10.0, analyzer_grid) if is_log_uniform else analyzer_grid + + if outcome_value < 0.5 or self._kde is None: + uniform_density = 1.0 / max(analyzer_high - analyzer_low, 1e-9) + return linear_grid, np.full_like(analyzer_grid, uniform_density) + return linear_grid, self._kde(analyzer_grid) + + def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: + raise NotImplementedError( + "KDEAnalyzer is for continuous factors only. Categorical schemas dispatch to" + " EmpiricalAnalyzer via make_analyzer." + ) + + +def make_analyzer(dataset: SensitivityDataset, outcome_name: str) -> BaseAnalyzer: + """Construct the right analyzer for the dataset's factor mix and outcome shape. + + Dispatch table (checked top-to-bottom): + - 1 continuous + 0 categorical AND the outcome is binary → :class:`KDEAnalyzer` + (avoids sbi NPE's 1D-theta Gaussian-shape constraint; exact under uniform prior) + - any continuous + any categorical → :class:`MNPEAnalyzer` + - all categorical (zero continuous) → :class:`EmpiricalAnalyzer` + - all continuous (zero categorical) → :class:`NPEAnalyzer` + (the multi-continuous-factor case; theta is multi-D so no Gaussian fallback) + + The KDE branch checks the outcome column's binary-ness on the dataset itself rather + than the schema, since outcome ``type: float`` in factors.yaml covers both continuous + durations and binary 0/1 success rates. + """ + num_continuous_factors = sum(1 for factor in dataset.schema.factors if factor.type == "continuous") + num_categorical_factors = sum(1 for factor in dataset.schema.factors if factor.type == "categorical") + assert num_continuous_factors + num_categorical_factors > 0, "Schema declares no factors" + + if num_continuous_factors == 1 and num_categorical_factors == 0: + outcome_column_index = dataset.outcome_columns[outcome_name] + outcome_values = dataset.x[:, outcome_column_index] + unique_outcome_values = set(outcome_values.flatten().tolist()) + if unique_outcome_values.issubset({0.0, 1.0}): + return KDEAnalyzer(dataset, outcome_name) + + if num_continuous_factors > 0 and num_categorical_factors > 0: + return MNPEAnalyzer(dataset, outcome_name) + if num_categorical_factors > 0: + return EmpiricalAnalyzer(dataset, outcome_name) + return NPEAnalyzer(dataset, outcome_name) diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py new file mode 100644 index 0000000000..d25cb4928f --- /dev/null +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -0,0 +1,417 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Schema parser and dataset loader for sensitivity analysis. + +Combines a hand-authored ``factors.yaml`` (the declared schema + priors) with an +``episode_summary.jsonl`` (per-episode factor draws + outcome values, written by +``episode_writer``) into the tensors that ``sbi`` consumes for posterior inference. + +Vocabulary refresher (for readers new to simulation-based inference / SBI): + - **theta** — the *factor* values per episode. The "inputs" we vary in the eval (e.g. + ``light_intensity``, ``pick_up_object``). Shape ``(num_episodes, total_factor_dim)``, + continuous factors come first then categoricals. + - **x** — the *outcome* values per episode. The "outputs" the policy produced (e.g. + ``success_rate``, ``object_moved_rate``). Shape ``(num_episodes, num_outcomes)``. + - **prior** — the assumed distribution over theta *before* seeing data. v0.3 ships + uniform priors only, encoded as ``sbi.utils.BoxUniform``. + - **factor_columns** — map from factor name to its column slice in theta, so + downstream code can extract a marginal by name without knowing the layout. + +MVP-2 supports continuous-1D and categorical factors. Vector continuous (``dim > 1``) +factors still raise ``NotImplementedError`` so adding them later is a fill-in. +""" + +from __future__ import annotations + +import json +import math +import torch +import yaml +from dataclasses import dataclass +from pathlib import Path +from typing import Literal + + +@dataclass +class FactorSpec: + """One factor's schema as declared in ``factors.yaml``. + + Continuous factors carry a ``range`` (one ``[low, high]`` pair per dim); categorical + factors carry ``choices`` (a list of string labels, integer-encoded by index in theta). + """ + + name: str + type: Literal["continuous", "categorical"] + dim: int = 1 + range: list[list[float]] | None = None # one [low, high] pair per dim, continuous only + choices: list[str] | None = None # categorical only + # Sampling distribution this factor was drawn from (must match the assumed prior). + # Default is linear uniform on ``range``. ``log_uniform`` declares the factor was drawn + # uniformly in log10 space — required when the sweep spans multiple decades to keep + # samples balanced per decade. Only valid for continuous factors with strictly positive + # ``range``. The dataset loader log10-transforms theta values for log_uniform factors + # so the analyzer trains in the same space the prior covers. + distribution: Literal["uniform", "log_uniform"] = "uniform" + + +@dataclass +class OutcomeSpec: + """One outcome's schema (just a name and a type hint; the loader treats all as float).""" + + name: str + type: str # "bool", "float", "int" — informational; loader treats all as float + + +@dataclass +class SliceSpec: + """The ``(policy, task, embodiment)`` tuple a dataset comes from. + + MNPE/NPE assume a single data-generating source per analysis, so all rows in a + dataset must belong to the same slice — enforced by the loader. + """ + + policy: str + task: str + embodiment: str + + +@dataclass +class FactorSchema: + """Parsed ``factors.yaml`` — slice + factor list + outcome list.""" + + slice: SliceSpec + factors: list[FactorSpec] + outcomes: list[OutcomeSpec] + + @classmethod + def from_yaml(cls, path: str | Path) -> FactorSchema: + """Load a ``factors.yaml`` from disk into a typed ``FactorSchema``. + + The YAML must have three top-level blocks: ``slice`` (policy/task/embodiment), + ``factors`` (one entry per varied input), and ``outcomes`` (one entry per + measured output). Each factor's ``type`` must be ``continuous`` or ``categorical``. + """ + with open(path, encoding="utf-8") as yaml_file: + yaml_data = yaml.safe_load(yaml_file) + assert isinstance(yaml_data, dict), f"factors.yaml at {path} must be a mapping at top level" + for required_key in ("slice", "factors", "outcomes"): + assert required_key in yaml_data, f"factors.yaml at {path} is missing top-level `{required_key}:` block" + + slice_block = yaml_data["slice"] + for required_key in ("policy", "task", "embodiment"): + assert ( + required_key in slice_block + ), f"factors.yaml at {path} `slice:` block is missing `{required_key}` (need policy/task/embodiment)" + slice_spec = SliceSpec( + policy=slice_block["policy"], + task=slice_block["task"], + embodiment=slice_block["embodiment"], + ) + + factors: list[FactorSpec] = [] + for factor_name, factor_block in yaml_data["factors"].items(): + assert "type" in factor_block, ( + f"factors.yaml at {path} factor {factor_name!r} is missing required `type:` field" + " (expected 'continuous' or 'categorical')" + ) + factor_type = factor_block["type"] + assert factor_type in ("continuous", "categorical"), ( + f"factors.yaml at {path} factor {factor_name!r} has unknown type {factor_type!r};" + " expected 'continuous' or 'categorical'" + ) + distribution = factor_block.get("distribution", "uniform") + assert distribution in ("uniform", "log_uniform"), ( + f"factors.yaml at {path} factor {factor_name!r} has unknown distribution" + f" {distribution!r}; expected 'uniform' or 'log_uniform'" + ) + if distribution == "log_uniform": + assert factor_type == "continuous", ( + f"factors.yaml at {path} factor {factor_name!r}: distribution 'log_uniform'" + " is only valid for continuous factors" + ) + factor_range = factor_block.get("range") + assert factor_range is not None, ( + f"factors.yaml at {path} factor {factor_name!r}: distribution 'log_uniform'" + " requires an explicit `range:` (cannot be inferred at load time)" + ) + for dim_index, (dim_low, dim_high) in enumerate(factor_range): + assert float(dim_low) > 0, ( + f"factors.yaml at {path} factor {factor_name!r} dim {dim_index}:" + " log_uniform requires strictly positive range bounds;" + f" got low={dim_low}" + ) + factors.append( + FactorSpec( + name=factor_name, + type=factor_type, + dim=factor_block.get("dim", 1), + range=factor_block.get("range"), + choices=factor_block.get("choices"), + distribution=distribution, + ) + ) + + outcomes = [ + OutcomeSpec(name=outcome_name, type=outcome_block.get("type", "float")) + for outcome_name, outcome_block in yaml_data["outcomes"].items() + ] + + return cls(slice=slice_spec, factors=factors, outcomes=outcomes) + + @property + def total_factor_dim(self) -> int: + """Total width of theta — sum of ``dim`` over continuous factors plus 1 per categorical.""" + return sum(factor.dim if factor.type == "continuous" else 1 for factor in self.factors) + + @property + def factor_columns(self) -> dict[str, slice]: + """Map factor name → column slice in theta. + + Continuous factors occupy the leading columns (their ``dim`` columns each), then + each categorical factor occupies one trailing column. This continuous-first + ordering matches sbi's MNPE convention so the same theta layout works for both + NPE (all-continuous) and MNPE (mixed). + """ + continuous_factors = [factor for factor in self.factors if factor.type == "continuous"] + categorical_factors = [factor for factor in self.factors if factor.type == "categorical"] + column_slices: dict[str, slice] = {} + column_index = 0 + for factor in continuous_factors + categorical_factors: + column_width = factor.dim if factor.type == "continuous" else 1 + column_slices[factor.name] = slice(column_index, column_index + column_width) + column_index += column_width + return column_slices + + +class SensitivityDataset: + """Combines a ``factors.yaml`` schema with an ``episode_summary.jsonl`` data file. + + On construction: + 1. Parses the schema (factors + outcomes + slice metadata). + 2. Loads the JSONL rows (one row per episode). + 3. Validates that every row contains all declared factor and outcome keys. + 4. Fills any missing continuous ranges by inferring from observed min/max so the + analyzer can always trust ``schema.factors[i].range`` to be populated. + 5. Builds the ``theta`` and ``x`` tensors that sbi (or the empirical analyzer) + will consume. + + The four public attributes used by the analyzer (``theta``, ``x``, ``prior``, + ``factor_columns``) are properties — recomputed lazily where appropriate. + """ + + def __init__(self, factors_yaml: str | Path, jsonl_path: str | Path): + self.schema = FactorSchema.from_yaml(factors_yaml) + + jsonl_text = Path(jsonl_path).read_text(encoding="utf-8") + self.rows = [json.loads(line) for line in jsonl_text.splitlines() if line.strip()] + assert len(self.rows) > 0, f"Empty episode_summary.jsonl at {jsonl_path}" + + self._validate_rows(jsonl_path) + self._infer_missing_factor_ranges() + + self._theta = self._build_factor_tensor() + self._x = self._build_outcome_tensor() + + def _validate_rows(self, jsonl_path: str | Path) -> None: + """Assert every JSONL row carries the keys declared in the schema. + + The writer logs the *entire* arena_env_args dict per row, so the loader only + requires that the schema's declared factor names are a *subset* of what's in + ``row["arena_env_args"]`` — extra keys (other arena_env_args we don't analyze) + are fine and ignored. Same superset-not-equality check for outcomes. + + Catches the most common authoring mistake: a factor declared in factors.yaml + that the eval didn't actually vary or log. Surfaces a clear error pointing at + the first offending row. + """ + expected_factor_names = {factor.name for factor in self.schema.factors} + expected_outcome_names = {outcome.name for outcome in self.schema.outcomes} + for row_index, row in enumerate(self.rows): + assert ( + "arena_env_args" in row and "outcomes" in row + ), f"Row {row_index} of {jsonl_path} missing arena_env_args/outcomes block" + missing_factor_names = expected_factor_names - set(row["arena_env_args"].keys()) + assert not missing_factor_names, ( + f"Row {row_index} of {jsonl_path} is missing factor(s) " + f"{sorted(missing_factor_names)} from its arena_env_args block; " + f"factors.yaml declares: {sorted(expected_factor_names)}" + ) + missing_outcome_names = expected_outcome_names - set(row["outcomes"].keys()) + assert ( + not missing_outcome_names + ), f"Row {row_index} of {jsonl_path} missing outcomes {sorted(missing_outcome_names)}" + + def _infer_missing_factor_ranges(self) -> None: + """For any continuous factor without a declared ``range``, fill it from observed data. + + The prior bounds default to ``[min(values), max(values)]`` over the JSONL. Users + who want a principled prior (e.g. matching the variation system's declared + ``Uniform(low, high)``) should hand-author ``range`` in factors.yaml; that value + takes precedence and this method skips them. + """ + for factor in self.schema.factors: + if factor.type != "continuous" or factor.range is not None: + continue + if factor.dim != 1: + raise NotImplementedError( + "Range inference for vector factors (dim > 1) is not implemented;" + f" factor {factor.name!r} has dim={factor.dim}" + ) + observed_values = [float(row["arena_env_args"][factor.name]) for row in self.rows] + factor.range = [[min(observed_values), max(observed_values)]] + + def _build_factor_tensor(self) -> torch.Tensor: + """Assemble the per-episode factor matrix ``theta``. + + Layout: continuous factors fill the leading columns (one column per dim), then + each categorical factor fills one trailing column. Categorical values are + encoded as ``float32`` integers ``0..num_choices-1`` per the index in + ``FactorSpec.choices`` — sbi's MNPE expects exactly this layout (continuous-first, + discrete columns as floats, the density estimator handles them as discrete). + """ + continuous_factors = [factor for factor in self.schema.factors if factor.type == "continuous"] + categorical_factors = [factor for factor in self.schema.factors if factor.type == "categorical"] + + factor_columns: list[torch.Tensor] = [] + + # Continuous columns come first (sbi MNPE convention). + for factor in continuous_factors: + if factor.dim != 1: + raise NotImplementedError( + "Vector continuous factors (dim > 1) are not yet supported;" + f" factor {factor.name!r} has dim={factor.dim}" + ) + raw_values = [float(row["arena_env_args"][factor.name]) for row in self.rows] + # For log_uniform factors, theta is stored in log10 space so the prior built + # below (BoxUniform on log10(range)) matches the parameter space the analyzer + # trains and queries in. The factor's declared ``range`` stays in linear units + # for human readability — log-transform happens here, not in the schema. + if factor.distribution == "log_uniform": + for row_index, value in enumerate(raw_values): + assert value > 0, ( + f"Row {row_index} factor {factor.name!r} has value {value!r}" + " ≤ 0; log_uniform factors require strictly positive samples" + ) + raw_values = [math.log10(v) for v in raw_values] + factor_column = torch.tensor(raw_values, dtype=torch.float32).unsqueeze(1) + factor_columns.append(factor_column) + + # Categorical columns: integer-code each string value as its index in FactorSpec.choices. + for factor in categorical_factors: + assert ( + factor.choices is not None and len(factor.choices) > 0 + ), f"Categorical factor {factor.name!r} has no `choices:` block in factors.yaml" + choice_to_code = {choice: code for code, choice in enumerate(factor.choices)} + category_codes: list[int] = [] + for row_index, row in enumerate(self.rows): + value = row["arena_env_args"][factor.name] + assert value in choice_to_code, ( + f"Row {row_index} factor {factor.name!r} has value {value!r}" + f" not in declared choices {factor.choices}" + ) + category_codes.append(choice_to_code[value]) + factor_column = torch.tensor(category_codes, dtype=torch.float32).unsqueeze(1) + factor_columns.append(factor_column) + + if factor_columns: + return torch.cat(factor_columns, dim=1) + return torch.zeros((len(self.rows), 0), dtype=torch.float32) + + def _build_outcome_tensor(self) -> torch.Tensor: + """Assemble the per-episode outcome matrix ``x`` (one column per declared outcome). + + Each outcome value is cast to float; bool outcomes become 0.0/1.0. The analyzer + usually selects a single outcome column at fit time and conditions queries on it. + """ + outcome_column_tensors = [ + torch.tensor([float(row["outcomes"][outcome.name]) for row in self.rows], dtype=torch.float32).unsqueeze(1) + for outcome in self.schema.outcomes + ] + return torch.cat(outcome_column_tensors, dim=1) + + @property + def theta(self) -> torch.Tensor: + """``(num_episodes, total_factor_dim)`` matrix of factor values, one row per episode. + + This is the "input" sbi infers a posterior over. Column layout is given by + ``factor_columns`` — continuous factors first, then categoricals (integer-coded). + """ + return self._theta + + @property + def x(self) -> torch.Tensor: + """``(num_episodes, num_outcomes)`` matrix of outcome values, one row per episode. + + This is what the analyzer conditions queries on. The analyzer typically selects a + single outcome column at fit time (e.g. ``success_rate``) and asks + "what theta values were consistent with observing this outcome?" + """ + return self._x + + @property + def factor_columns(self) -> dict[str, slice]: + """Map factor name → its column slice in theta. Same as ``schema.factor_columns``.""" + return self.schema.factor_columns + + @property + def outcome_columns(self) -> dict[str, int]: + """Map outcome name → its column index in x.""" + return {outcome.name: index for index, outcome in enumerate(self.schema.outcomes)} + + @property + def has_categorical_factors(self) -> bool: + """True iff the schema declares at least one categorical factor.""" + return any(factor.type == "categorical" for factor in self.schema.factors) + + @property + def prior(self): + """The uniform prior over all factor dims that the analyzer assumes. + + Built as a single ``sbi.utils.BoxUniform`` over the concatenated bounds in + continuous-first / categorical-after order: + - Continuous factor → uses the declared (or inferred) ``[low, high]`` per dim. + - Categorical factor → uses ``[0, num_choices - 1]`` (the integer codes from + ``_build_factor_tensor``); sbi MNPE's mixed density estimator treats them as + discrete from there. + + sbi is imported lazily so loading the dataset doesn't pay the sbi import cost + unless the analyzer actually runs. + """ + from sbi.utils import BoxUniform + + low_bounds: list[float] = [] + high_bounds: list[float] = [] + + # Continuous factor bounds (one [low, high] pair per dim). For log_uniform factors + # the prior is built in log10 space — the corresponding theta column is also + # log10-transformed in ``_build_factor_tensor``, so the analyzer sees a consistent + # (log-space-θ, log-space-prior) pair without any further transformation downstream. + for factor in self.schema.factors: + if factor.type != "continuous": + continue + assert factor.range is not None, f"Factor {factor.name!r} has no range and was not inferred" + for dim_low, dim_high in factor.range: + if factor.distribution == "log_uniform": + low_bounds.append(math.log10(float(dim_low))) + high_bounds.append(math.log10(float(dim_high))) + else: + low_bounds.append(float(dim_low)) + high_bounds.append(float(dim_high)) + + # Categorical factor bounds: [0, num_choices - 1] per factor (one column). + for factor in self.schema.factors: + if factor.type != "categorical": + continue + assert ( + factor.choices is not None and len(factor.choices) > 0 + ), f"Categorical factor {factor.name!r} has no `choices:` block" + low_bounds.append(0.0) + high_bounds.append(float(len(factor.choices) - 1)) + + return BoxUniform( + low=torch.tensor(low_bounds, dtype=torch.float32), + high=torch.tensor(high_bounds, dtype=torch.float32), + ) diff --git a/isaaclab_arena/analysis/sensitivity/episode_writer.py b/isaaclab_arena/analysis/sensitivity/episode_writer.py new file mode 100644 index 0000000000..199c3479b9 --- /dev/null +++ b/isaaclab_arena/analysis/sensitivity/episode_writer.py @@ -0,0 +1,110 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Per-episode summary writer for sensitivity analysis. + +``write_episode_summaries`` appends one JSONL row per recorded demo for a just-completed +job. Each row carries: + + - ``job_name`` and ``episode_idx`` for traceability, + - ``arena_env_args`` — the *entire* job.arena_env_args_dict, i.e. every value that + parameterized the env for this episode, + - ``outcomes`` — per-episode outcome values from the task's registered metrics, extracted + from the recorded hdf5 demos via each metric's ``compute_metric_from_recording``. + +The eval-side writer is intentionally analysis-agnostic: it logs all env state, and the +analyzer's ``factors.yaml`` decides which subset of those keys to treat as factors. This +keeps the writer free of any "what counts as a factor?" knowledge. + +Import-order note: this module legitimately touches pxr at import time via +``isaaclab_arena.metrics.metrics`` (which imports ``isaaclab.envs.manager_based_rl_env``). +Like ``metrics`` itself, callers must defer importing this module until *after* +``SimulationAppContext`` is active — see ``policy_runner.py`` (which uses the same pattern +for ``compute_metrics``) and ``eval_runner.py``'s per-job try block for examples. +""" + +from __future__ import annotations + +import h5py +import json +from pathlib import Path +from typing import TYPE_CHECKING + +from isaaclab_arena.metrics.metrics import get_metric_recorder_dataset_path +from isaaclab_arena.metrics.metrics_logger import metrics_to_plain_python_types + +if TYPE_CHECKING: + from isaaclab_arena.evaluation.job_manager import Job + + +def write_episode_summaries(env, job: Job, output_path: str | Path) -> int: + """Append one JSONL row per recorded demo for the just-completed job. + + Each row has shape:: + + { + "job_name": "", + "episode_idx": , + "arena_env_args": , + "outcomes": + } + + Args: + env: The (possibly gym-wrapped) Arena env that just finished its rollout. The hdf5 + path and registered metrics are read from ``env.unwrapped.cfg``. + job: The Job that ran. Its ``arena_env_args_dict`` is logged verbatim under + ``arena_env_args``. + output_path: JSONL file to append to. Created (with parent dirs) if absent. + + Returns: + Number of rows written. + """ + unwrapped_env = env.unwrapped + if not hasattr(unwrapped_env.cfg, "metrics") or unwrapped_env.cfg.metrics is None: + return 0 + + arena_env_args_snapshot = dict(job.arena_env_args_dict) + + hdf5_dataset_path = get_metric_recorder_dataset_path(unwrapped_env) + registered_metrics = unwrapped_env.cfg.metrics + output_path = Path(output_path) + output_path.parent.mkdir(parents=True, exist_ok=True) + + rows_written = 0 + env_step_dt = float(unwrapped_env.step_dt) + with h5py.File(hdf5_dataset_path, "r") as hdf5_file: + recorded_demos = hdf5_file["data"] + with open(output_path, "a", encoding="utf-8") as jsonl_output: + for demo_index, demo_name in enumerate(recorded_demos): + demo_group = recorded_demos[demo_name] + raw_outcome_values = {} + # Find the demo's actual step count by taking the max length across all + # recorded metric arrays. Per-step time-series recorders (e.g. the + # ObjectVelocityRecorder for `object_moved_rate`) produce a (T, …) array + # whose first dim is the step count. Per-episode scalar recorders (e.g. + # the SuccessRecorder) produce a (1,) array regardless of episode length — + # using `len()` on the wrong one collapses task_duration to a single step. + demo_step_count = 0 + for metric in registered_metrics: + recorded_metric_data = demo_group[metric.recorder_term_name][:] + raw_outcome_values[metric.name] = metric.compute_metric_from_recording([recorded_metric_data]) + demo_step_count = max(demo_step_count, len(recorded_metric_data)) + # task_duration: wall-clock-equivalent seconds spent on this episode before + # termination. Short for fast successes / early failures, max_episode_length + # for timeouts. Provides a continuous outcome that carries information beyond + # binary success metrics. + if demo_step_count > 0: + raw_outcome_values["task_duration"] = float(demo_step_count) * env_step_dt + outcome_values = metrics_to_plain_python_types(raw_outcome_values) + summary_row = { + "job_name": job.name, + "episode_idx": demo_index, + "arena_env_args": arena_env_args_snapshot, + "outcomes": outcome_values, + } + jsonl_output.write(json.dumps(summary_row) + "\n") + rows_written += 1 + + return rows_written diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py new file mode 100644 index 0000000000..9fc3274da3 --- /dev/null +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -0,0 +1,238 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Plot renderers for sensitivity analysis. + +Pure-visualization module. Calls into the analyzer's public posterior queries +(``continuous_marginal_density`` and ``categorical_marginal_probs``) and renders matplotlib +figures. Decoupled from the analyzer hierarchy so new plot types can be added without +touching inference code, and so existing plot code can be tested with mock posteriors. + +The single entry point is ``plot_marginal(analyzer, factor_name, output_path, ...)``, +which dispatches by factor type to the right renderer. +""" + +from __future__ import annotations + +import numpy as np +from pathlib import Path +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from isaaclab_arena.analysis.sensitivity.analyzer import BaseAnalyzer + from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec + + +def plot_marginal( + analyzer: BaseAnalyzer, + factor_name: str, + output_path, + outcome_value: float = 1.0, + num_samples: int = 10_000, + num_grid_points: int = 200, +) -> None: + """Render the marginal posterior for ``factor_name``, dispatching by factor type. + + For continuous factors, the analyzer must expose ``continuous_marginal_density`` + (only ``PosteriorAnalyzer`` does — ``EmpiricalAnalyzer`` rejects continuous factors at + construction time, so this branch isn't reachable through ``make_analyzer``). + """ + factor_spec = analyzer._factor_spec(factor_name) + if factor_spec.type == "continuous": + if not hasattr(analyzer, "continuous_marginal_density"): + raise NotImplementedError( + f"{type(analyzer).__name__} cannot plot continuous factors; expected a PosteriorAnalyzer (NPE/MNPE)." + ) + _plot_continuous_marginal(analyzer, factor_spec, output_path, outcome_value, num_grid_points) + elif factor_spec.type == "categorical": + _plot_categorical_marginal(analyzer, factor_spec, output_path, outcome_value, num_samples) + else: + raise NotImplementedError(f"Unsupported factor type {factor_spec.type!r}") + + +def _plot_continuous_marginal( + analyzer: BaseAnalyzer, + factor_spec: FactorSpec, + output_path, + outcome_value: float, + num_grid_points: int, +) -> None: + """Render a continuous factor's marginal posterior as a density curve. + + The blue curve shows ``P(factor_value | outcome=outcome_value)`` from the analyzer. + Below the x-axis is an empirical "rug" — small vertical ticks at the actual recorded + theta values, coloured green for episodes where the outcome was achieved (``≥ 0.5``) + and red for episodes where it was not. The rug lets a human eyeball whether the + smooth posterior actually agrees with where the successful episodes lived. + """ + import matplotlib.pyplot as plt + + grid, density = analyzer.continuous_marginal_density(factor_spec.name, outcome_value, num_grid_points) + factor_column_slice = analyzer.dataset.factor_columns[factor_spec.name] + outcome_column_index = analyzer.dataset.outcome_columns[analyzer.outcome_name] + empirical_theta_values = analyzer.dataset.theta[:, factor_column_slice].squeeze(-1).cpu().numpy() + # For log_uniform factors the dataset stores log10(theta); the rug ticks need to be at + # the actual intensity values to align with the linear-scale grid returned above. + if factor_spec.distribution == "log_uniform": + empirical_theta_values = np.power(10.0, empirical_theta_values) + empirical_outcomes = analyzer.dataset.x[:, outcome_column_index].cpu().numpy() + + figure, axes = plt.subplots(figsize=(8, 5)) + axes.plot( + grid, + density, + color="steelblue", + linewidth=2, + label=f"P({factor_spec.name} | {analyzer.outcome_name}={outcome_value:g})", + ) + axes.fill_between(grid, 0, density, color="steelblue", alpha=0.2) + + # Rug coloring depends on outcome shape. For binary outcomes (only 0/1 observed) the + # green/red ≥/<0.5 split gives a meaningful "successes vs failures" picture. For + # continuous outcomes (e.g. task_duration) the same threshold is nonsensical (every + # sample is ≥ 0.5), so we drop the split and just show all samples as one neutral rug. + is_binary_outcome = set(empirical_outcomes.flatten().tolist()).issubset({0.0, 1.0}) + if is_binary_outcome: + success_mask = empirical_outcomes >= 0.5 + axes.scatter( + empirical_theta_values[success_mask], + np.full(success_mask.sum(), -0.05 * density.max()), + marker="|", + color="seagreen", + s=80, + label=f"{analyzer.outcome_name} ≥ 0.5 (n={success_mask.sum()})", + ) + axes.scatter( + empirical_theta_values[~success_mask], + np.full((~success_mask).sum(), -0.1 * density.max()), + marker="|", + color="firebrick", + s=80, + label=f"{analyzer.outcome_name} < 0.5 (n={(~success_mask).sum()})", + ) + else: + axes.scatter( + empirical_theta_values, + np.full(len(empirical_theta_values), -0.05 * density.max()), + marker="|", + color="slategray", + s=80, + label=f"observed samples (n={len(empirical_theta_values)})", + ) + axes.set_xlabel(factor_spec.name) + axes.set_ylabel("posterior density") + axes.set_title(_plot_title(analyzer, factor_spec.name)) + if factor_spec.distribution == "log_uniform": + axes.set_xscale("log") + axes.legend(loc="best", fontsize=9) + axes.grid(alpha=0.3) + figure.tight_layout() + _save_figure(figure, output_path) + + +def _plot_categorical_marginal( + analyzer: BaseAnalyzer, + factor_spec: FactorSpec, + output_path, + outcome_value: float, + num_samples: int, +) -> None: + """Render a categorical factor's marginal as side-by-side bars per category. + + The blue bar (left of each category) is the analyzer's ``P(category | outcome)``. + The green bar (right of each category) is the *empirical* per-category outcome rate + — independent of the analyzer's posterior, computed directly from the raw data. + For the ``EmpiricalAnalyzer`` the two will agree exactly (up to normalization); for + a posterior-based analyzer they may differ slightly if the model smooths. + + Each green bar is annotated with the sample count ``n`` for that category, so the + user can see how trustworthy each bar is. + """ + import matplotlib.pyplot as plt + + assert factor_spec.choices is not None + choices = factor_spec.choices + num_choices = len(choices) + factor_column_slice = analyzer.dataset.factor_columns[factor_spec.name] + outcome_column_index = analyzer.dataset.outcome_columns[analyzer.outcome_name] + + # Posterior probs come from the analyzer; empirical rate and counts are raw data, + # rendered alongside as a sanity reference. + posterior_probabilities = analyzer.categorical_marginal_probs(factor_spec.name, outcome_value, num_samples) + + empirical_theta_codes = analyzer.dataset.theta[:, factor_column_slice].squeeze(-1).long().cpu().numpy() + empirical_outcomes = analyzer.dataset.x[:, outcome_column_index].cpu().numpy() + empirical_rates = np.zeros(num_choices) + empirical_counts = np.zeros(num_choices, dtype=int) + for code in range(num_choices): + category_mask = empirical_theta_codes == code + empirical_counts[code] = int(category_mask.sum()) + if category_mask.any(): + empirical_rates[code] = float((empirical_outcomes[category_mask] >= 0.5).mean()) + + figure, axes = plt.subplots(figsize=(max(8, 1.0 * num_choices), 5)) + bar_x_positions = np.arange(num_choices) + bar_width = 0.4 + axes.bar( + bar_x_positions - bar_width / 2, + posterior_probabilities, + bar_width, + color="steelblue", + alpha=0.8, + label=f"P(category | {analyzer.outcome_name}={outcome_value:g})", + ) + axes.bar( + bar_x_positions + bar_width / 2, + empirical_rates, + bar_width, + color="seagreen", + alpha=0.7, + label=f"empirical {analyzer.outcome_name} rate per category", + ) + for category_index, count in enumerate(empirical_counts): + axes.text( + category_index + bar_width / 2, + empirical_rates[category_index] + 0.02, + f"n={count}", + ha="center", + fontsize=8, + ) + + axes.set_xticks(bar_x_positions) + axes.set_xticklabels(choices, rotation=30, ha="right") + axes.set_ylabel("probability") + axes.set_ylim(0, 1.05) + axes.set_title(_plot_title(analyzer, factor_spec.name)) + axes.legend(loc="best", fontsize=9) + axes.grid(alpha=0.3, axis="y") + figure.tight_layout() + _save_figure(figure, output_path) + + +def _plot_title(analyzer: BaseAnalyzer, factor_name: str) -> str: + """Format the plot title as ``"Sensitivity of to " / slice block``.""" + return ( + f"Sensitivity of {analyzer.outcome_name} to {factor_name}\n" + f"slice: {analyzer.dataset.schema.slice.policy} / " + f"{analyzer.dataset.schema.slice.task} / {analyzer.dataset.schema.slice.embodiment}" + ) + + +def _save_figure(figure, destination) -> None: + """Save a matplotlib figure to ``destination`` (a path or a writable file-like object). + + Accepts either a filesystem path (``str`` / ``Path``) or any seekable file-like buffer + (e.g. ``io.BytesIO``). Paths get parent-dir creation; buffers are written to directly. + The figure is closed after save regardless of destination type. + """ + import matplotlib.pyplot as plt + + if isinstance(destination, (str, Path)): + path = Path(destination) + path.parent.mkdir(parents=True, exist_ok=True) + figure.savefig(path, dpi=150) + else: + figure.savefig(destination, dpi=150, format="png") + plt.close(figure) diff --git a/isaaclab_arena/analysis/sensitivity/report.py b/isaaclab_arena/analysis/sensitivity/report.py new file mode 100644 index 0000000000..7bb6a0dd10 --- /dev/null +++ b/isaaclab_arena/analysis/sensitivity/report.py @@ -0,0 +1,736 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Interactive HTML sensitivity report generator. + +Single function entry point: :func:`generate_report` reads a (factors.yaml, JSONL) pair, +runs the analyzer pipeline for every declared (outcome, factor) combination, and emits a +self-contained HTML file with interactive Plotly plots embedded inline. Bootstrap CSS via +CDN provides the visual chrome (tabs, cards, accordion). The deliverable is a single .html +file that opens in any modern browser — no server, no Python at view time. + +Why Plotly + Jinja2 + Bootstrap (vs the earlier static-PNG version): + + - Plotly plots support hover (exact (factor, density) readout), drag-to-zoom into + specific regions (critical for sweeps spanning multiple decades), legend-click to + hide/show traces. The static matplotlib version had none of this. + - Bootstrap nav-tabs let users switch between outcomes (success_rate vs task_duration + etc.) without scrolling — only the active outcome's section is visible at a time. + - Jinja2 templating keeps the HTML structure separate from the Python data, which + makes the template editable without touching plot generation. + +The generator produces *one* HTML file. Plotly.js is loaded from the CDN by default, +which keeps file size ~500 KB. For offline viewing, pass ``plotlyjs_mode="inline"`` +to embed the ~3.5 MB Plotly library directly in the HTML. + +The CLI wrapper is ``isaaclab_arena.scripts.generate_sensitivity_report``. +""" + +from __future__ import annotations + +import datetime +import html as html_module +import json +import numpy as np +from pathlib import Path +from typing import Any, Literal + +import plotly.graph_objects as go +from jinja2 import Template + +from isaaclab_arena.analysis.sensitivity.analyzer import make_analyzer +from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec, OutcomeSpec, SensitivityDataset + +_BOOTSTRAP_CSS_URL = "https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/css/bootstrap.min.css" +_BOOTSTRAP_JS_URL = "https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/js/bootstrap.bundle.min.js" + + +def generate_report( + factors_yaml_path: str | Path, + jsonl_path: str | Path, + output_html_path: str | Path, + plotlyjs_mode: Literal["cdn", "inline"] = "cdn", +) -> Path: + """Build a self-contained interactive HTML sensitivity report. + + Reads the schema from ``factors_yaml_path`` and the per-episode data from + ``jsonl_path``, fits one analyzer per declared outcome, and renders one Plotly + figure per (outcome, factor) pair. All figures end up inline in a single HTML file + arranged as Bootstrap nav-tabs (one tab per outcome) with one card per factor inside + each tab. + + Args: + factors_yaml_path: Schema file declaring factors and outcomes. + jsonl_path: episode_summary.jsonl from eval_runner. + output_html_path: Destination for the report file. + plotlyjs_mode: ``"cdn"`` for a small file that needs internet to load Plotly; + ``"inline"`` to embed Plotly.js (~3.5 MB) for offline viewing. + + Returns: + The resolved output path. + """ + factors_yaml_path = Path(factors_yaml_path) + jsonl_path = Path(jsonl_path) + output_html_path = Path(output_html_path) + + dataset = SensitivityDataset(factors_yaml_path, jsonl_path) + + print(f"[INFO] Generating report: {len(dataset.schema.outcomes)} outcomes × {len(dataset.schema.factors)} factors") + outcome_blocks = [] + for outcome in dataset.schema.outcomes: + analyzer = make_analyzer(dataset, outcome.name) + print(f"[INFO] Fitting analyzer for outcome={outcome.name!r} ({type(analyzer).__name__})") + analyzer.fit() + outcome_value = _default_outcome_value_for_analysis(dataset, outcome) + + sections = [] + for factor in dataset.schema.factors: + print(f"[INFO] Rendering ({outcome.name}, {factor.name}) @ outcome_value={outcome_value:g}") + plot_html = _render_marginal_to_plotly_html(analyzer, factor, outcome_value) + stats = _compute_summary_stats(dataset, factor, outcome, outcome_value) + sections.append({ + "factor_name": factor.name, + "plot_html": plot_html, + "stats": _format_stats_for_display(stats), + }) + outcome_blocks.append({ + "name": outcome.name, + "conditioning_value": _format_number(outcome_value), + "analyzer_name": type(analyzer).__name__, + "sections": sections, + }) + + factors_yaml_text = factors_yaml_path.read_text(encoding="utf-8") + raw_jsonl_text = _read_first_rows(jsonl_path, max_rows=10) + + html_text = _render_template( + slice_info=dataset.schema.slice, + num_episodes=len(dataset.rows), + num_factors=len(dataset.schema.factors), + num_outcomes=len(dataset.schema.outcomes), + outcome_blocks=outcome_blocks, + factors_yaml_text=factors_yaml_text, + raw_jsonl_text=raw_jsonl_text, + plotlyjs_mode=plotlyjs_mode, + ) + + output_html_path.parent.mkdir(parents=True, exist_ok=True) + output_html_path.write_text(html_text, encoding="utf-8") + print(f"[INFO] Wrote report → {output_html_path}") + return output_html_path + + +def _default_outcome_value_for_analysis(dataset: SensitivityDataset, outcome: OutcomeSpec) -> float: + """Pick a sensible value to condition the posterior on for this outcome. + + Binary outcomes (only ``{0, 1}`` observed) → ``1.0`` (the "success" branch). + Continuous outcomes → empirical median; a "typical case" value always inside the data range. + """ + outcome_column_index = dataset.outcome_columns[outcome.name] + values = dataset.x[:, outcome_column_index].cpu().numpy() + if set(values.flatten().tolist()).issubset({0.0, 1.0}): + return 1.0 + return float(np.median(values)) + + +def _render_marginal_to_plotly_html(analyzer, factor: FactorSpec, outcome_value: float) -> str: + """Build the Plotly figure for one (analyzer, factor) pair and return its HTML div. + + Dispatches by factor type. Continuous-factor plots get a built-in Plotly slider over + conditioning values when the outcome is continuous (more than 2 distinct values + observed) — the user can drag through "what if we condition on outcome=X instead?" + and watch the posterior curve update in-place. Binary outcomes (only 0/1 observed) + keep the static single-value plot since a 2-step slider has no value. + + Plotly.js itself is *not* embedded per-plot — the page loads it once globally via + CDN or inline (see :func:`_render_template`), so each plot here is just the div + + the constructor JS. + """ + if factor.type == "continuous": + if _outcome_is_continuous(analyzer): + figure = _build_continuous_figure_with_slider(analyzer, factor) + else: + figure = _build_continuous_figure(analyzer, factor, outcome_value) + elif factor.type == "categorical": + figure = _build_categorical_figure(analyzer, factor, outcome_value) + else: + raise NotImplementedError(f"Unsupported factor type {factor.type!r}") + return figure.to_html(include_plotlyjs=False, full_html=False, config={"displaylogo": False}) + + +def _outcome_is_continuous(analyzer) -> bool: + """Heuristic: an outcome is "continuous" (slider-worthy) if it has >2 distinct observed values.""" + outcome_column_index = analyzer.dataset.outcome_columns[analyzer.outcome_name] + values = analyzer.dataset.x[:, outcome_column_index].cpu().numpy().flatten() + unique_values = set(values.tolist()) + if unique_values.issubset({0.0, 1.0}): + return False + return len(unique_values) > 2 + + +def _build_continuous_figure(analyzer, factor: FactorSpec, outcome_value: float) -> go.Figure: + """Continuous-factor density curve + empirical rug. Hover, zoom, pan all native to Plotly.""" + grid, density = analyzer.continuous_marginal_density(factor.name, outcome_value, num_grid_points=200) + factor_column_slice = analyzer.dataset.factor_columns[factor.name] + outcome_column_index = analyzer.dataset.outcome_columns[analyzer.outcome_name] + empirical_theta = analyzer.dataset.theta[:, factor_column_slice].squeeze(-1).cpu().numpy() + # log_uniform factors store theta in log10 space; un-transform for display so rug ticks + # land at the actual intensity values that align with the (linear-scale) curve grid. + if factor.distribution == "log_uniform": + empirical_theta = np.power(10.0, empirical_theta) + empirical_outcomes = analyzer.dataset.x[:, outcome_column_index].cpu().numpy() + density_max = float(np.max(density)) if len(density) else 1.0 + rug_y_value = -0.05 * density_max + + figure = go.Figure() + figure.add_trace( + go.Scatter( + x=grid, + y=density, + mode="lines", + fill="tozeroy", + line={"color": "steelblue", "width": 2}, + fillcolor="rgba(70, 130, 180, 0.2)", + name=f"P({factor.name} | {analyzer.outcome_name}={outcome_value:g})", + hovertemplate=f"{factor.name}=%{{x:.4g}}
density=%{{y:.4g}}", + ) + ) + + is_binary_outcome = set(empirical_outcomes.flatten().tolist()).issubset({0.0, 1.0}) + if is_binary_outcome: + success_mask = empirical_outcomes >= 0.5 + _add_rug_trace( + figure, + empirical_theta[success_mask], + rug_y_value, + color="seagreen", + name=f"{analyzer.outcome_name} ≥ 0.5 (n={int(success_mask.sum())})", + ) + _add_rug_trace( + figure, + empirical_theta[~success_mask], + rug_y_value * 2, + color="firebrick", + name=f"{analyzer.outcome_name} < 0.5 (n={int((~success_mask).sum())})", + ) + else: + _add_rug_trace( + figure, + empirical_theta, + rug_y_value, + color="slategray", + name=f"observed (n={len(empirical_theta)})", + hover_values=empirical_outcomes, + hover_label=analyzer.outcome_name, + ) + + xaxis_kwargs = {"title": factor.name} + if factor.distribution == "log_uniform": + xaxis_kwargs["type"] = "log" + figure.update_layout( + title=_plot_title(analyzer, factor.name), + xaxis=xaxis_kwargs, + yaxis_title="posterior density", + template="plotly_white", + hovermode="closest", + height=480, + margin={"l": 60, "r": 30, "t": 70, "b": 50}, + legend={"orientation": "h", "yanchor": "bottom", "y": 1.02, "xanchor": "right", "x": 1}, + ) + return figure + + +def _build_continuous_figure_with_slider(analyzer, factor: FactorSpec) -> go.Figure: + """Continuous-factor density curve with a draggable slider over outcome conditioning values. + + Pre-computes the posterior density at ``num_slider_steps`` evenly-spaced conditioning + values across the empirical outcome range, packs them as Plotly frames keyed on the + outcome value, and binds a slider to navigate them. The rug (empirical samples) is + invariant across frames — same data, different conditional curve — so it's drawn once + and held static while only trace 0 (the density curve) updates per frame. + + Total fit cost stays one analyzer.fit() in the outer loop; this adds ~num_slider_steps + calls to ``continuous_marginal_density`` at report-gen time. Each call is a posterior + sample + histogram (~50 ms for KDE, ~100 ms for NPE), so ~1-2 s extra per plot. Trivial + at browse time — the user just drags the slider, no compute. + """ + num_slider_steps = 15 + + outcome_column_index = analyzer.dataset.outcome_columns[analyzer.outcome_name] + empirical_outcomes = analyzer.dataset.x[:, outcome_column_index].cpu().numpy().flatten() + factor_column_slice = analyzer.dataset.factor_columns[factor.name] + empirical_theta = analyzer.dataset.theta[:, factor_column_slice].squeeze(-1).cpu().numpy() + # log_uniform factors store theta in log10 space; un-transform for display so the rug + # aligns with the curve's linear-grid x-coordinates and Plotly's log-axis ticks read as + # actual intensity values. + if factor.distribution == "log_uniform": + empirical_theta = np.power(10.0, empirical_theta) + + min_outcome = float(np.min(empirical_outcomes)) + max_outcome = float(np.max(empirical_outcomes)) + if min_outcome == max_outcome: + # Degenerate (no spread) — fall back to the static plot at that one value. + return _build_continuous_figure(analyzer, factor, min_outcome) + + slider_outcome_values = np.linspace(min_outcome, max_outcome, num_slider_steps) + # Default-active slider step: the empirical median, snapped to the nearest slider value. + default_outcome_value = float(np.median(empirical_outcomes)) + active_step_index = int(np.argmin(np.abs(slider_outcome_values - default_outcome_value))) + + # Pre-compute density curves for each slider step. + density_grids = [] + density_values = [] + for outcome_value in slider_outcome_values: + grid, density = analyzer.continuous_marginal_density(factor.name, float(outcome_value), num_grid_points=200) + density_grids.append(grid) + density_values.append(density) + density_max = float(max(np.max(d) for d in density_values)) if density_values else 1.0 + rug_y_value = -0.05 * density_max + + # Initial figure: trace 0 = density at the default-active slider step, trace 1 = rug. + initial_density = density_values[active_step_index] + initial_grid = density_grids[active_step_index] + figure = go.Figure( + data=[ + go.Scatter( + x=initial_grid, + y=initial_density, + mode="lines", + fill="tozeroy", + line={"color": "steelblue", "width": 2}, + fillcolor="rgba(70, 130, 180, 0.2)", + name=f"P({factor.name} | {analyzer.outcome_name})", + hovertemplate=f"{factor.name}=%{{x:.4g}}
density=%{{y:.4g}}", + ), + go.Scatter( + x=empirical_theta, + y=np.full(len(empirical_theta), rug_y_value), + mode="markers", + marker={"symbol": "line-ns-open", "size": 14, "color": "slategray", "line": {"width": 2}}, + name=f"observed (n={len(empirical_theta)})", + customdata=empirical_outcomes, + hovertemplate=f"{factor.name}=%{{x:.4g}}
{analyzer.outcome_name}=%{{customdata:.4g}}", + ), + ], + ) + + # Frames update only trace[0] (density). traces=[0] keeps rug static at trace[1]. + figure.frames = [ + go.Frame( + data=[ + go.Scatter( + x=density_grids[step_index], + y=density_values[step_index], + mode="lines", + fill="tozeroy", + line={"color": "steelblue", "width": 2}, + fillcolor="rgba(70, 130, 180, 0.2)", + hovertemplate=f"{factor.name}=%{{x:.4g}}
density=%{{y:.4g}}", + ) + ], + name=f"{slider_outcome_values[step_index]:.3g}", + traces=[0], + ) + for step_index in range(num_slider_steps) + ] + + slider_steps = [ + { + "method": "animate", + "args": [ + [f"{slider_outcome_values[step_index]:.3g}"], + { + "mode": "immediate", + "frame": {"duration": 0, "redraw": True}, + "transition": {"duration": 0}, + }, + ], + "label": f"{slider_outcome_values[step_index]:.3g}", + } + for step_index in range(num_slider_steps) + ] + + xaxis_kwargs = {"title": factor.name} + if factor.distribution == "log_uniform": + xaxis_kwargs["type"] = "log" + figure.update_layout( + title=_plot_title(analyzer, factor.name), + xaxis=xaxis_kwargs, + yaxis_title="posterior density", + template="plotly_white", + hovermode="closest", + height=560, + margin={"l": 60, "r": 30, "t": 70, "b": 110}, + legend={"orientation": "h", "yanchor": "bottom", "y": 1.02, "xanchor": "right", "x": 1}, + sliders=[{ + "active": active_step_index, + "currentvalue": { + "prefix": f"Conditioning on {analyzer.outcome_name} = ", + "font": {"size": 14}, + }, + "steps": slider_steps, + "pad": {"t": 50, "b": 10}, + "len": 0.9, + "x": 0.05, + }], + ) + return figure + + +def _add_rug_trace( + figure: go.Figure, + x_values: np.ndarray, + y_value: float, + color: str, + name: str, + hover_values: np.ndarray | None = None, + hover_label: str | None = None, +) -> None: + """Add a single rug (vertical-tick scatter) trace to ``figure``. + + Uses ``line-ns-open`` marker symbol for the classic rug look. If ``hover_values`` is + supplied, hover reveals the per-sample outcome value alongside the factor value. + """ + customdata = None + if hover_values is not None and hover_label is not None: + customdata = hover_values + hovertemplate = f"%{{x:.4g}}
{hover_label}=%{{customdata:.4g}}" + else: + hovertemplate = f"%{{x:.4g}}{html_module.escape(name)}" + figure.add_trace( + go.Scatter( + x=x_values, + y=np.full(len(x_values), y_value), + mode="markers", + marker={"symbol": "line-ns-open", "size": 14, "color": color, "line": {"width": 2}}, + name=name, + customdata=customdata, + hovertemplate=hovertemplate, + ) + ) + + +def _build_categorical_figure(analyzer, factor: FactorSpec, outcome_value: float) -> go.Figure: + """Categorical-factor side-by-side bars: analyzer posterior vs empirical rate per category.""" + assert factor.choices is not None + choices = factor.choices + num_choices = len(choices) + factor_column_slice = analyzer.dataset.factor_columns[factor.name] + outcome_column_index = analyzer.dataset.outcome_columns[analyzer.outcome_name] + + posterior_probabilities = analyzer.categorical_marginal_probs(factor.name, outcome_value, num_samples=10_000) + empirical_theta_codes = analyzer.dataset.theta[:, factor_column_slice].squeeze(-1).long().cpu().numpy() + empirical_outcomes = analyzer.dataset.x[:, outcome_column_index].cpu().numpy() + empirical_rates = np.zeros(num_choices) + empirical_counts = np.zeros(num_choices, dtype=int) + for code in range(num_choices): + category_mask = empirical_theta_codes == code + empirical_counts[code] = int(category_mask.sum()) + if category_mask.any(): + empirical_rates[code] = float((empirical_outcomes[category_mask] >= 0.5).mean()) + + figure = go.Figure() + figure.add_trace( + go.Bar( + x=choices, + y=posterior_probabilities, + name=f"P(category | {analyzer.outcome_name}={outcome_value:g})", + marker_color="steelblue", + hovertemplate="%{x}
posterior=%{y:.4g}", + ) + ) + figure.add_trace( + go.Bar( + x=choices, + y=empirical_rates, + name=f"empirical {analyzer.outcome_name} rate", + marker_color="seagreen", + customdata=empirical_counts, + hovertemplate="%{x}
empirical=%{y:.4g}
n=%{customdata}", + ) + ) + figure.update_layout( + title=_plot_title(analyzer, factor.name), + barmode="group", + xaxis_title=factor.name, + yaxis_title="probability", + template="plotly_white", + yaxis={"range": [0, 1.05]}, + height=480, + margin={"l": 60, "r": 30, "t": 70, "b": 80}, + legend={"orientation": "h", "yanchor": "bottom", "y": 1.02, "xanchor": "right", "x": 1}, + ) + return figure + + +def _plot_title(analyzer, factor_name: str) -> str: + return ( + f"Sensitivity of {analyzer.outcome_name} to {factor_name}" + f" — {analyzer.dataset.schema.slice.policy}" + f" / {analyzer.dataset.schema.slice.task}" + f" / {analyzer.dataset.schema.slice.embodiment}" + ) + + +def _compute_summary_stats( + dataset: SensitivityDataset, factor: FactorSpec, outcome: OutcomeSpec, outcome_value: float +) -> dict: + """Empirical summary stats kept distinct from the analyzer's posterior for cross-checking.""" + outcome_column_index = dataset.outcome_columns[outcome.name] + outcome_values = dataset.x[:, outcome_column_index].cpu().numpy() + is_binary_outcome = set(outcome_values.flatten().tolist()).issubset({0.0, 1.0}) + + stats: dict[str, Any] = { + "num_episodes": int(len(dataset.rows)), + "is_binary_outcome": is_binary_outcome, + } + if is_binary_outcome: + success_count = int((outcome_values >= 0.5).sum()) + stats["success_count"] = success_count + stats["failure_count"] = int(len(outcome_values) - success_count) + stats["overall_success_rate"] = float(outcome_values.mean()) + else: + stats["outcome_min"] = float(outcome_values.min()) + stats["outcome_max"] = float(outcome_values.max()) + stats["outcome_median"] = float(np.median(outcome_values)) + stats["outcome_mean"] = float(outcome_values.mean()) + + factor_column_slice = dataset.factor_columns[factor.name] + factor_values = dataset.theta[:, factor_column_slice].squeeze(-1).cpu().numpy() + if factor.type == "continuous": + stats["factor_min_observed"] = float(factor_values.min()) + stats["factor_max_observed"] = float(factor_values.max()) + stats["factor_unique_count"] = int(np.unique(np.round(factor_values, 6)).size) + if factor.range is not None and len(factor.range) == 1: + range_low, range_high = factor.range[0] + stats["factor_range"] = [float(range_low), float(range_high)] + elif factor.type == "categorical" and factor.choices is not None: + per_category_counts = {} + for code, choice in enumerate(factor.choices): + per_category_counts[choice] = int((factor_values == code).sum()) + stats["per_category_counts"] = per_category_counts + return stats + + +def _format_stats_for_display(stats: dict) -> list[tuple[str, str]]: + """Flatten a stats dict into ordered (label, html_value) pairs for the template.""" + formatted: list[tuple[str, str]] = [] + for key, value in stats.items(): + formatted.append((key, _format_value(value))) + return formatted + + +def _format_value(value: Any) -> str: + """Render one stats value with light formatting (numbers compact, dicts as nested list).""" + if isinstance(value, dict): + items = "".join( + f"
  • {html_module.escape(str(k))}: {_format_value(v)}
    • " for k, v in value.items() + ) + return f"
      {items}
    " + if isinstance(value, list): + return html_module.escape( + ", ".join(_format_number(item) if isinstance(item, (int, float)) else str(item) for item in value) + ) + if isinstance(value, bool): + return "✓" if value else "✗" + if isinstance(value, (int, float)): + return _format_number(value) + return html_module.escape(str(value)) + + +def _format_number(value) -> str: + """Compact number formatting: int-ish values stay integer; floats use 4-significant-digit g-format.""" + if isinstance(value, bool): + return str(value) + if isinstance(value, int): + return str(value) + if isinstance(value, float): + if value == int(value): + return f"{int(value)}" + return f"{value:.4g}" + return str(value) + + +def _read_first_rows(jsonl_path: Path, max_rows: int) -> str: + """Return the first ``max_rows`` JSONL rows pretty-printed for the accordion snippet.""" + lines: list[str] = [] + with open(jsonl_path, encoding="utf-8") as jsonl_file: + for row_index, raw_line in enumerate(jsonl_file): + if row_index >= max_rows: + lines.append("…") + break + try: + lines.append(json.dumps(json.loads(raw_line), indent=2)) + except json.JSONDecodeError: + lines.append(raw_line.rstrip()) + return "\n".join(lines) + + +_HTML_TEMPLATE = """\ + + + + + + {{ title }} + + {{ plotlyjs_block|safe }} + + + + +

    {{ title }}

    + +
    +
    +
    +
    Policy: {{ slice.policy }}
    +
    Task: {{ slice.task }}
    +
    Embodiment: {{ slice.embodiment }}
    +
    Episodes: {{ num_episodes }}
    +
    Factors declared: {{ num_factors }}
    +
    Outcomes declared: {{ num_outcomes }}
    +
    Generated {{ timestamp }}
    +
    +
    +
    + + + +
    + {% for outcome in outcome_blocks %} +
    +
    +

    + Posterior conditioned on {{ outcome.name }} = {{ outcome.conditioning_value }} + (analyzer: {{ outcome.analyzer_name }}). +

    + {% for section in outcome.sections %} +
    +
    + Factor: {{ section.factor_name }} +
    +
    + {{ section.plot_html|safe }} +
    +
    Empirical summary
    + + + {% for key, value in section.stats %} + + {% endfor %} + +
    {{ key }}{{ value|safe }}
    +
    +
    + {% endfor %} +
    +
    + {% endfor %} +
    + +
    +
    +

    + +

    +
    + 
    {{ factors_yaml_text }}
    +
    +
    +
    +

    + +

    +
    + 
    {{ raw_jsonl_text }}
    +
    +
    +
    + +
    + Generated by isaaclab_arena.analysis.sensitivity.report. Plots are + interactive: hover for exact values, drag-rectangle to zoom, double-click to reset, + click legend entries to hide/show traces. +
    + + + + +""" + + +def _render_template( + slice_info, + num_episodes: int, + num_factors: int, + num_outcomes: int, + outcome_blocks: list[dict], + factors_yaml_text: str, + raw_jsonl_text: str, + plotlyjs_mode: Literal["cdn", "inline"], +) -> str: + """Apply the Jinja2 template to the assembled context.""" + title = f"Sensitivity report — {slice_info.policy} / {slice_info.task} / {slice_info.embodiment}" + if plotlyjs_mode == "inline": + from plotly.offline import get_plotlyjs + + plotlyjs_block = f"" + else: + plotlyjs_block = "" + + rendered = Template(_HTML_TEMPLATE).render( + title=title, + slice=slice_info, + num_episodes=num_episodes, + num_factors=num_factors, + num_outcomes=num_outcomes, + outcome_blocks=outcome_blocks, + factors_yaml_text=factors_yaml_text, + raw_jsonl_text=raw_jsonl_text, + raw_jsonl_max_rows=10, + timestamp=datetime.datetime.now().isoformat(timespec="seconds"), + bootstrap_css=_BOOTSTRAP_CSS_URL, + bootstrap_js=_BOOTSTRAP_JS_URL, + plotlyjs_block=plotlyjs_block, + ) + return rendered diff --git a/isaaclab_arena/analysis/sensitivity/synthetic_data_categorical.py b/isaaclab_arena/analysis/sensitivity/synthetic_data_categorical.py new file mode 100644 index 0000000000..550048b03c --- /dev/null +++ b/isaaclab_arena/analysis/sensitivity/synthetic_data_categorical.py @@ -0,0 +1,146 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Synthetic JSONL generator for the MVP-2 categorical-factor analyzer smoke test. + +Generates a fake ``episode_summary.jsonl`` where a single categorical factor +``pick_up_object`` drives the success probability. Half of the choices are "easy" +(high success rate), the other half are "hard" (low success rate). With enough samples +the analyzer's recovered ``P(category | success=1)`` should concentrate on the easy +choices, and the empirical per-category bar should match the configured rates within +binomial noise. + +Sampling is **uniform over the categorical choices** (matches the semantics of +``Choose(...)`` in Alex's variation system and the uniform prior the analyzer assumes). + +Pair with the auto-emitted factors.yaml. End-to-end smoke test: + + /isaac-sim/python.sh -m isaaclab_arena.analysis.sensitivity.synthetic_data_categorical \\ + --output /tmp/syn_cat.jsonl + /isaac-sim/python.sh -m isaaclab_arena.scripts.analyze_sensitivity \\ + --factors_yaml /tmp/factors.yaml \\ + --episode_summary /tmp/syn_cat.jsonl \\ + --figure_path /tmp/syn_cat_plot.png + +Expected output: a bar chart where the "easy" choices have ~3x more posterior mass and +empirical success rate than the "hard" choices. +""" + +from __future__ import annotations + +import argparse +import json +import random +from pathlib import Path + +# Five distinct objects, like the maple-table droid sweep. The first three are "easy" +# (high success), the last two are "hard" (low success) — a known signal the analyzer +# should recover. +DEFAULT_CHOICES = [ + "rubiks_cube_hot3d_robolab", + "wooden_bowl_hot3d_robolab", + "alphabet_soup_can_hope_robolab", + "mug_ycb_robolab", + "sugar_box_ycb_robolab", +] +DEFAULT_SUCCESS_PROBABILITIES = [0.90, 0.85, 0.75, 0.25, 0.15] + + +def _factors_yaml_text(choices: list[str]) -> str: + """Build the factors.yaml content matching the synthetic data.""" + choices_string = ", ".join(choices) + return ( + "# factors.yaml — synthetic categorical dataset for analyzer smoke-testing.\n" + "# Auto-emitted by synthetic_data_categorical alongside the JSONL.\n" + "\n" + "slice:\n" + " policy: synthetic_categorical\n" + " task: synthetic_pick_and_place\n" + " embodiment: synthetic\n" + "\n" + "factors:\n" + " pick_up_object:\n" + " type: categorical\n" + f" choices: [{choices_string}]\n" + "\n" + "outcomes:\n" + " success_rate:\n" + " type: float\n" + " object_moved_rate:\n" + " type: float\n" + ) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument( + "--output", + type=str, + default="/tmp/synthetic_categorical_episode_summary.jsonl", + help="Output JSONL path.", + ) + parser.add_argument( + "--factors-yaml-out", + type=str, + default=None, + help="Output factors.yaml path. Default: same directory as --output, named factors.yaml.", + ) + parser.add_argument( + "--num-episodes", + type=int, + default=200, + help="Total episodes (uniform draws across all choices). Default 200 → ~40 per category for 5 choices.", + ) + parser.add_argument("--seed", type=int, default=42, help="RNG seed for reproducibility.") + args = parser.parse_args() + + random_generator = random.Random(args.seed) + choices = DEFAULT_CHOICES + success_probabilities = DEFAULT_SUCCESS_PROBABILITIES + assert len(choices) == len( + success_probabilities + ), "DEFAULT_CHOICES and DEFAULT_SUCCESS_PROBABILITIES lengths must match" + num_choices = len(choices) + + summary_rows = [] + per_category_stats: dict[str, list[int]] = {choice: [0, 0] for choice in choices} # category → [successes, total] + for episode_index in range(args.num_episodes): + category_index = random_generator.randrange(num_choices) + chosen_category = choices[category_index] + was_success = 1.0 if random_generator.random() < success_probabilities[category_index] else 0.0 + per_category_stats[chosen_category][0] += int(was_success) + per_category_stats[chosen_category][1] += 1 + summary_rows.append({ + "job_name": "synth_categorical", + "episode_idx": episode_index, + "arena_env_args": {"pick_up_object": chosen_category}, + "outcomes": {"success_rate": was_success, "object_moved_rate": was_success}, + }) + + output_path = Path(args.output) + output_path.parent.mkdir(parents=True, exist_ok=True) + with open(output_path, "w", encoding="utf-8") as jsonl_file: + for summary_row in summary_rows: + jsonl_file.write(json.dumps(summary_row) + "\n") + + factors_yaml_path = Path(args.factors_yaml_out) if args.factors_yaml_out else output_path.parent / "factors.yaml" + factors_yaml_path.parent.mkdir(parents=True, exist_ok=True) + factors_yaml_path.write_text(_factors_yaml_text(choices), encoding="utf-8") + + print(f"[INFO] Wrote {len(summary_rows)} rows to {output_path}") + print(f"[INFO] Wrote factors schema → {factors_yaml_path}") + print("[INFO] Per-category success counts (analyzer should pull posterior mass toward easy cats):") + for choice, target_probability in zip(choices, success_probabilities): + successes, total = per_category_stats[choice] + empirical_percentage = 100 * successes / total if total else 0.0 + bar_string = "█" * int(round(empirical_percentage / 5)) + print( + f" {choice:<35s} target={target_probability:>4.0%}" + f" empirical={successes:>3d}/{total:<3d} ({empirical_percentage:>5.1f}%) {bar_string}" + ) + + +if __name__ == "__main__": + main() diff --git a/isaaclab_arena/analysis/sensitivity/synthetic_data_continuous.py b/isaaclab_arena/analysis/sensitivity/synthetic_data_continuous.py new file mode 100644 index 0000000000..0c353dbaf5 --- /dev/null +++ b/isaaclab_arena/analysis/sensitivity/synthetic_data_continuous.py @@ -0,0 +1,182 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Synthetic JSONL generator for smoke-testing the sensitivity analysis pipeline. + +Produces a fake ``episode_summary.jsonl`` with a known linear-Gaussian competence band: + + P(success | intensity) = exp(-(intensity - center)^2 / (2 * sigma^2)) + +i.e. a Gaussian directly in linear intensity space centered on a trained operating point. + +Sampling is **linear-uniform** over ``[10, 5000]`` (one intensity drawn independently per +episode). This matches the semantics of ``Uniform(10, 5000)`` in Alex's variation system +and matches the uniform prior declared in factors.yaml. With these choices the smoke +test should recover the posterior peak exactly at ``center``, because: + + 1. linear uniform sampling matches the declared uniform prior (no sampling bias), + 2. a linear-Gaussian likelihood is symmetric in linear theta-space, so its mode + equals its mean — and the NPE Gaussian fallback for 1D binary outcomes fits + the mean, recovering the true center. + +A more realistic competence band would be log-Gaussian (asymmetric: cameras blind fast +at low intensity, saturate gradually at high), but that introduces a peak-bias artifact +that masks pipeline-correctness signal. This smoke test deliberately matches the +structural assumptions the analyzer can recover exactly, so any mismatch in the output +points to a real bug rather than a known statistical limitation. + +Pair with the hand-authored ``light_intensity_sweep_factors.yaml`` so the analyzer +script can be smoke-tested end-to-end without running Isaac Sim: + + /isaac-sim/python.sh -m isaaclab_arena.analysis.sensitivity.synthetic_data_continuous \\ + --output /tmp/syn.jsonl + /isaac-sim/python.sh -m isaaclab_arena.scripts.analyze_sensitivity \\ + --factors_yaml isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_factors.yaml \\ + --episode_summary /tmp/syn.jsonl \\ + --figure_path /tmp/syn_plot.png + +Expected output: a posterior-density curve peaking at ``center`` (default 500), with +empirical rug markers showing successes clustered around the center and failures at +both extremes. +""" + +from __future__ import annotations + +import argparse +import json +import math +import random +from pathlib import Path + +INTENSITY_LOW = 10.0 +INTENSITY_HIGH = 5000.0 + +# A self-contained factors.yaml template for the synthetic dataset. Kept inline (rather +# than imported from episode_writer.py) so this module stays a pure-python dev tool — +# importing episode_writer would transitively load pxr via isaaclab_arena.metrics. +_SYNTHETIC_FACTORS_YAML = """\ +# factors.yaml — synthetic dataset for analyzer smoke-testing. +# Auto-emitted by isaaclab_arena.analysis.sensitivity.synthetic_data_continuous alongside the JSONL. + +slice: + policy: synthetic_linear_uniform + task: synthetic_pick_and_place + embodiment: synthetic + +factors: + light_intensity: + type: continuous + dim: 1 + +outcomes: + success_rate: + type: float + object_moved_rate: + type: float + task_duration: + type: float +""" + +# Synthetic task-duration model: at the competence-band center an episode finishes in ~5s; +# successes scale linearly with z-score (harder = slower); failures hit the episode timeout. +EPISODE_TIMEOUT_S = 20.0 +BASE_SUCCESS_DURATION_S = 5.0 +SUCCESS_DURATION_PER_SIGMA = 6.0 + + +def success_probability(intensity: float, center: float, sigma: float) -> float: + """Linear-Gaussian competence band: peaks at `center`, falls off symmetrically in linear space.""" + z_score = (intensity - center) / sigma + return math.exp(-0.5 * z_score * z_score) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--output", type=str, default="/tmp/synthetic_episode_summary.jsonl", help="Output JSONL path.") + parser.add_argument( + "--factors-yaml-out", + type=str, + default=None, + help="Output factors.yaml path. Default: same directory as --output, named factors.yaml.", + ) + parser.add_argument( + "--num-episodes", + type=int, + default=180, + help="Total number of episodes to generate. Each draws an intensity from Uniform(10, 5000).", + ) + parser.add_argument("--center", type=float, default=500.0, help="Intensity where success rate peaks. Default: 500.") + parser.add_argument( + "--sigma", + type=float, + default=400.0, + help=( + "Linear-space width of the competence band (1 sigma in intensity units). Default: 400," + " which gives ~95%% success in [100, 900] and near-zero success beyond ~1700." + ), + ) + parser.add_argument("--seed", type=int, default=42, help="RNG seed for reproducibility.") + args = parser.parse_args() + + random_generator = random.Random(args.seed) + + summary_rows = [] + for episode_index in range(args.num_episodes): + intensity = random_generator.uniform(INTENSITY_LOW, INTENSITY_HIGH) + probability_of_success = success_probability(intensity, args.center, args.sigma) + was_success = 1.0 if random_generator.random() < probability_of_success else 0.0 + z_score = abs(intensity - args.center) / args.sigma + if was_success: + task_duration = BASE_SUCCESS_DURATION_S + SUCCESS_DURATION_PER_SIGMA * z_score + task_duration += random_generator.gauss(0.0, 0.4) + task_duration = max(1.0, min(task_duration, EPISODE_TIMEOUT_S)) + else: + task_duration = EPISODE_TIMEOUT_S + summary_rows.append({ + "job_name": "synth_linear_uniform", + "episode_idx": episode_index, + "arena_env_args": {"light_intensity": intensity}, + "outcomes": { + "success_rate": was_success, + "object_moved_rate": was_success, + "task_duration": task_duration, + }, + }) + + output_path = Path(args.output) + output_path.parent.mkdir(parents=True, exist_ok=True) + with open(output_path, "w", encoding="utf-8") as jsonl_file: + for summary_row in summary_rows: + jsonl_file.write(json.dumps(summary_row) + "\n") + + # Emit a matching factors.yaml so the analyzer can be pointed at this synthetic dataset + # without any hand-authored schema. Inline string template — see _SYNTHETIC_FACTORS_YAML. + factors_yaml_path = Path(args.factors_yaml_out) if args.factors_yaml_out else output_path.parent / "factors.yaml" + factors_yaml_path.parent.mkdir(parents=True, exist_ok=True) + factors_yaml_path.write_text(_SYNTHETIC_FACTORS_YAML, encoding="utf-8") + + print(f"[INFO] Wrote {len(summary_rows)} rows to {output_path}") + print(f"[INFO] Wrote factors schema → {factors_yaml_path}") + print(f"[INFO] Linear-Gaussian competence band: center={args.center:g}, sigma={args.sigma:g}") + print("[INFO] Per-bin success rates (10 equal bins across the prior range):") + num_bins = 10 + bin_width = (INTENSITY_HIGH - INTENSITY_LOW) / num_bins + for bin_index in range(num_bins): + bin_low = INTENSITY_LOW + bin_index * bin_width + bin_high = bin_low + bin_width + rows_in_bin = [row for row in summary_rows if bin_low <= row["arena_env_args"]["light_intensity"] < bin_high] + if not rows_in_bin: + continue + successes_in_bin = sum(int(row["outcomes"]["success_rate"]) for row in rows_in_bin) + percentage = 100 * successes_in_bin / len(rows_in_bin) + bar_string = "█" * int(round(percentage / 5)) + print( + f" [{bin_low:>5g}, {bin_high:>5g}): {successes_in_bin:>3d}/{len(rows_in_bin):<3d}" + f" ({percentage:>5.1f}%) {bar_string}" + ) + + +if __name__ == "__main__": + main() diff --git a/isaaclab_arena/evaluation/eval_runner.py b/isaaclab_arena/evaluation/eval_runner.py index 2359a9ee8b..ca7162d713 100644 --- a/isaaclab_arena/evaluation/eval_runner.py +++ b/isaaclab_arena/evaluation/eval_runner.py @@ -200,6 +200,16 @@ def main(): # Check if any job requires cameras and enable them if needed before starting simulation enable_cameras_if_required(eval_jobs_config, args_cli) + # Per-episode summary recording is opt-in via --episode_summary. The writer logs the + # full arena_env_args dict per episode; the analyzer side decides which keys to treat + # as factors via factors.yaml. No eval-side knowledge of "factors" required. + episode_summary_enabled = args_cli.episode_summary is not None + if episode_summary_enabled: + print( + "[INFO] Episode summary recording enabled. Per-episode arena_env_args + outcomes" + f" → {args_cli.episode_summary}" + ) + with SimulationAppContext(args_cli): job_manager = JobManager(eval_jobs_config["jobs"]) metrics_logger = MetricsLogger() @@ -250,6 +260,15 @@ def main(): language_instruction=job.language_instruction, ) + if episode_summary_enabled: + # Deferred import — episode_writer transitively touches pxr via + # isaaclab_arena.metrics.metrics. Matches the policy_runner.py + # pattern for compute_metrics. + from isaaclab_arena.analysis.sensitivity.episode_writer import write_episode_summaries + + rows = write_episode_summaries(env, job, args_cli.episode_summary) + print(f"[INFO] Wrote {rows} episode summaries for job '{job.name}'") + job_manager.complete_job(job, metrics=metrics, status=Status.COMPLETED) # users may not specify metrics for a task, although it's not recommended diff --git a/isaaclab_arena/evaluation/eval_runner_cli.py b/isaaclab_arena/evaluation/eval_runner_cli.py index 15e6131b42..3f95c04fd9 100644 --- a/isaaclab_arena/evaluation/eval_runner_cli.py +++ b/isaaclab_arena/evaluation/eval_runner_cli.py @@ -38,3 +38,12 @@ def add_eval_runner_arguments(parser: argparse.ArgumentParser) -> None: " set only if a long sweep grows in host memory or gets OOM-killed." ), ) + parser.add_argument( + "--episode_summary", + type=str, + default=None, + help=( + "Append one JSONL row per recorded episode (arena_env_args + outcomes) to" + " this file. Consumed by the sensitivity analyzer. Default unset — no recording." + ), + ) diff --git a/isaaclab_arena/evaluation/job_manager.py b/isaaclab_arena/evaluation/job_manager.py index 8c4d09c467..43bbe1ffb4 100644 --- a/isaaclab_arena/evaluation/job_manager.py +++ b/isaaclab_arena/evaluation/job_manager.py @@ -28,6 +28,7 @@ def __init__( policy_config_dict: dict = None, status: Status = None, language_instruction: str = None, + arena_env_args_dict: dict | None = None, ): """Initialize a Job instance. @@ -42,9 +43,13 @@ def __init__( status: Job status (defaults to PENDING) language_instruction: Optional language instruction override for the policy. When set, takes precedence over the task's own description. + arena_env_args_dict: The original dict form of arena_env_args before conversion to + CLI args list. Preserves typed values (e.g. floats stay floats) for downstream + consumers that need to index by key. """ self.name = name self.arena_env_args = arena_env_args + self.arena_env_args_dict = arena_env_args_dict if arena_env_args_dict is not None else {} assert num_envs > 0, "num_envs must be greater than 0" assert not ( num_steps is not None and num_episodes is not None @@ -102,6 +107,7 @@ def from_dict(cls, data: dict) -> "Job": return cls( name=data["name"], arena_env_args=cls.convert_args_dict_to_cli_args_list(data["arena_env_args"]), + arena_env_args_dict=data["arena_env_args"], policy_type=data["policy_type"], num_envs=num_envs, num_steps=num_steps, diff --git a/isaaclab_arena/scripts/analyze_sensitivity.py b/isaaclab_arena/scripts/analyze_sensitivity.py new file mode 100644 index 0000000000..052948b2f5 --- /dev/null +++ b/isaaclab_arena/scripts/analyze_sensitivity.py @@ -0,0 +1,106 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""CLI driver for 1D continuous sensitivity analysis (MVP-1). + +Loads a SensitivityDataset from a paired (factors.yaml, episode_summary.jsonl), trains +NPE on the selected outcome column, and saves a posterior-marginal plot for the chosen +factor. + +Example: + python -m isaaclab_arena.scripts.analyze_sensitivity \\ + --factors_yaml isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_factors.yaml \\ + --episode_summary ./episode_summary.jsonl \\ + --figure_path ./light_intensity_sensitivity.png + +This script runs entirely offline — no Isaac Sim, no policy server. +""" + +from __future__ import annotations + +import argparse + +from isaaclab_arena.analysis.sensitivity.analyzer import make_analyzer +from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset +from isaaclab_arena.analysis.sensitivity.plotting import plot_marginal + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--factors_yaml", type=str, required=True, help="Path to factors.yaml.") + parser.add_argument( + "--episode_summary", type=str, required=True, help="Path to episode_summary.jsonl produced by eval_runner." + ) + parser.add_argument( + "--input_factor", + type=str, + default=None, + help="Name of the factor to plot. Defaults to the only factor declared in factors.yaml.", + ) + parser.add_argument( + "--output_metric", + type=str, + default=None, + help="Outcome name to condition on. Defaults to the first outcome listed in factors.yaml.", + ) + parser.add_argument( + "--outcome_value", + type=float, + default=1.0, + help="Outcome value to condition on (1.0 = success). Default: 1.0.", + ) + parser.add_argument( + "--figure_path", + type=str, + default="./sensitivity.png", + help="Output figure path. Default: ./sensitivity.png.", + ) + args = parser.parse_args() + + print(f"[INFO] Loading dataset: factors={args.factors_yaml} jsonl={args.episode_summary}") + dataset = SensitivityDataset(args.factors_yaml, args.episode_summary) + + available_factors = list(dataset.factor_columns) + available_outcomes = [outcome.name for outcome in dataset.schema.outcomes] + + if args.input_factor is None: + factor_name = available_factors[0] + else: + if args.input_factor not in available_factors: + parser.error( + f"--input_factor {args.input_factor!r} not found in factors.yaml. " + f"Available factors: {available_factors}" + ) + factor_name = args.input_factor + + if args.output_metric is None: + outcome_name = available_outcomes[0] + else: + if args.output_metric not in available_outcomes: + parser.error( + f"--output_metric {args.output_metric!r} not found in factors.yaml. " + f"Available outcomes: {available_outcomes}" + ) + outcome_name = args.output_metric + + print( + f"[INFO] Analyzing factor '{factor_name}' against outcome '{outcome_name}'" + f" (conditioning on outcome={args.outcome_value:g})" + ) + print( + f"[INFO] num_episodes={len(dataset.rows)}; theta shape={tuple(dataset.theta.shape)};" + f" x shape={tuple(dataset.x.shape)}" + ) + + analyzer = make_analyzer(dataset, outcome_name=outcome_name) + print(f"[INFO] Dispatched analyzer: {type(analyzer).__name__}") + analyzer.fit() + print(f"[INFO] Plotting marginal -> {args.figure_path}") + plot_marginal(analyzer, factor_name, output_path=args.figure_path, outcome_value=args.outcome_value) + print("[INFO] Done.") + + +if __name__ == "__main__": + main() diff --git a/isaaclab_arena/scripts/generate_sensitivity_report.py b/isaaclab_arena/scripts/generate_sensitivity_report.py new file mode 100644 index 0000000000..f2da967ef1 --- /dev/null +++ b/isaaclab_arena/scripts/generate_sensitivity_report.py @@ -0,0 +1,56 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""CLI driver: build a static HTML sensitivity report from a (factors.yaml, JSONL) pair. + +Thin wrapper around :func:`isaaclab_arena.analysis.sensitivity.report.generate_report`. +For per-plot inspection or A/B comparisons of a single factor/outcome, use +``analyze_sensitivity.py`` instead — this script produces the full deliverable artifact +covering every declared (factor, outcome) combination in one self-contained HTML file. + +Example:: + + python -m isaaclab_arena.scripts.generate_sensitivity_report \\ + --factors_yaml path/to/factors.yaml \\ + --episode_summary path/to/episode_summary.jsonl \\ + --output_html /tmp/sensitivity_report.html +""" + +from __future__ import annotations + +import argparse + +from isaaclab_arena.analysis.sensitivity.report import generate_report + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--factors_yaml", type=str, required=True, help="Path to factors.yaml.") + parser.add_argument( + "--episode_summary", type=str, required=True, help="Path to episode_summary.jsonl produced by eval_runner." + ) + parser.add_argument( + "--output_html", + type=str, + default="./sensitivity_report.html", + help="Output HTML file. Default: ./sensitivity_report.html. Self-contained interactive HTML.", + ) + parser.add_argument( + "--plotlyjs_mode", + choices=["cdn", "inline"], + default="cdn", + help=( + "Plotly.js bundling. 'cdn' (default, ~500 KB output, needs internet to load) loads" + " Plotly from plot.ly's CDN; 'inline' (~5 MB output, fully offline) embeds Plotly" + " directly. Use 'inline' when sharing to people who may not have internet." + ), + ) + args = parser.parse_args() + + generate_report(args.factors_yaml, args.episode_summary, args.output_html, plotlyjs_mode=args.plotlyjs_mode) + + +if __name__ == "__main__": + main() diff --git a/setup.py b/setup.py index 82cd92b56b..2c1c5944fc 100644 --- a/setup.py +++ b/setup.py @@ -20,6 +20,7 @@ "jupyter", "debugpy", "tenacity", + "sbi", ] setup( From 48fba5dae41f8bf5808cc6a43a1e541276dd7181 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Wed, 3 Jun 2026 17:03:51 +0200 Subject: [PATCH 02/74] Add sensitivity-analysis sweep configs MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Paired (factors.yaml, jobs_config.json) sets for pi0 on the droid pick_and_place_maple_table task: * light_intensity_sweep — single continuous factor (light intensity) * pick_up_object_sweep — single categorical factor (object identity) * multi_factor_overnight_sweep — light_intensity (log-uniform) x 5 objects, num_episodes=4 * two_object_shiny_matte_sweep — focused 2-object contrast (matte mustard vs specular soup can) x log-uniform light, num_episodes=2 factors.yaml declares each factor's type/range/distribution for the analyzer; jobs configs are consumed by eval_runner --episode_summary. Use --chunk_size for the long sweeps to avoid host-RSS OOM. Signed-off-by: Clemens Volk --- .../light_intensity_sweep_factors.yaml | 36 + .../light_intensity_sweep_jobs_config.json | 184 + ...t_intensity_sweep_minimal_jobs_config.json | 64 + .../multi_factor_overnight_sweep_factors.yaml | 54 + ...ti_factor_overnight_sweep_jobs_config.json | 22004 ++++++++++++++++ .../pick_up_object_sweep_factors.yaml | 26 + ...k_up_object_sweep_minimal_jobs_config.json | 70 + .../two_object_shiny_matte_sweep_factors.yaml | 47 + ..._object_shiny_matte_sweep_jobs_config.json | 6604 +++++ 9 files changed, 29089 insertions(+) create mode 100644 isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_factors.yaml create mode 100644 isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_jobs_config.json create mode 100644 isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_minimal_jobs_config.json create mode 100644 isaaclab_arena_environments/eval_jobs_configs/multi_factor_overnight_sweep_factors.yaml create mode 100644 isaaclab_arena_environments/eval_jobs_configs/multi_factor_overnight_sweep_jobs_config.json create mode 100644 isaaclab_arena_environments/eval_jobs_configs/pick_up_object_sweep_factors.yaml create mode 100644 isaaclab_arena_environments/eval_jobs_configs/pick_up_object_sweep_minimal_jobs_config.json create mode 100644 isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_factors.yaml create mode 100644 isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_jobs_config.json diff --git a/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_factors.yaml b/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_factors.yaml new file mode 100644 index 0000000000..4a4c822004 --- /dev/null +++ b/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_factors.yaml @@ -0,0 +1,36 @@ +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +# Sensitivity-analysis schema for the light_intensity sweep on droid + pi0. +# Paired with: light_intensity_sweep_jobs_config.json (and the minimal variant). +# Hand-authored — must stay in sync with --factor_keys passed to eval_runner. +# +# - slice identifies the (policy, task, embodiment) the dataset comes from; MNPE/NPE +# assumes a single data-generating source per analysis. +# - factors declares what the eval varies; eval_runner is told which arena_env_args +# keys to record via --factor_keys (must match the names here). +# - outcomes declares what the eval measures; the writer pulls these from the +# registered task metrics (compute_metric_from_recording on each demo). + +slice: + policy: pi0_remote + task: pick_and_place_maple_table + embodiment: droid_abs_joint_pos + +factors: + light_intensity: + type: continuous + dim: 1 + # Mirrors the robolab evaluated endpoints [10, 5000] for direct comparison; spans the + # dark / normal / bright regimes around the policy's trained operating point (~500). + range: [[10, 5000]] + +outcomes: + success_rate: + # Per-episode value of SuccessRateMetric. Returns 0.0 or 1.0 for a single demo. + type: float + object_moved_rate: + # Per-episode value of ObjectMovedRateMetric. Same shape as success_rate. + type: float diff --git a/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_jobs_config.json b/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_jobs_config.json new file mode 100644 index 0000000000..6da57f7199 --- /dev/null +++ b/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_jobs_config.json @@ -0,0 +1,184 @@ +{ + "jobs": [ + { + "name": "light_intensity_sweep_10", + "arena_env_args": { + "enable_cameras": true, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 10 + }, + "num_episodes": 20, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "light_intensity_sweep_25", + "arena_env_args": { + "enable_cameras": true, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 25 + }, + "num_episodes": 20, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "light_intensity_sweep_60", + "arena_env_args": { + "enable_cameras": true, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 60 + }, + "num_episodes": 20, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "light_intensity_sweep_150", + "arena_env_args": { + "enable_cameras": true, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 150 + }, + "num_episodes": 20, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "light_intensity_sweep_350", + "arena_env_args": { + "enable_cameras": true, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 350 + }, + "num_episodes": 20, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "light_intensity_sweep_800", + "arena_env_args": { + "enable_cameras": true, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 800 + }, + "num_episodes": 20, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "light_intensity_sweep_1800", + "arena_env_args": { + "enable_cameras": true, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1800 + }, + "num_episodes": 20, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "light_intensity_sweep_4000", + "arena_env_args": { + "enable_cameras": true, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4000 + }, + "num_episodes": 20, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "light_intensity_sweep_5000", + "arena_env_args": { + "enable_cameras": true, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 5000 + }, + "num_episodes": 20, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + } + ] +} diff --git a/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_minimal_jobs_config.json b/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_minimal_jobs_config.json new file mode 100644 index 0000000000..0e6f1d3ce0 --- /dev/null +++ b/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_minimal_jobs_config.json @@ -0,0 +1,64 @@ +{ + "jobs": [ + { + "name": "light_intensity_minimal_100", + "arena_env_args": { + "enable_cameras": true, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 100 + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "light_intensity_minimal_500", + "arena_env_args": { + "enable_cameras": true, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 500 + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "light_intensity_minimal_5000", + "arena_env_args": { + "enable_cameras": true, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 5000 + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + } + ] +} diff --git a/isaaclab_arena_environments/eval_jobs_configs/multi_factor_overnight_sweep_factors.yaml b/isaaclab_arena_environments/eval_jobs_configs/multi_factor_overnight_sweep_factors.yaml new file mode 100644 index 0000000000..35ec94ce53 --- /dev/null +++ b/isaaclab_arena_environments/eval_jobs_configs/multi_factor_overnight_sweep_factors.yaml @@ -0,0 +1,54 @@ +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +# Sensitivity-analysis schema for the multi-factor overnight pick-and-place sweep +# on droid + pi0. Paired with: multi_factor_overnight_sweep_jobs_config.json +# (1000 jobs × num_envs=4 = ~4000 expected episodes). +# +# Sampling distribution: ``light_intensity`` was drawn LOG-uniformly across [0.01, 5000] +# (5.7 decades, equal density per decade). The analyzer in ``dataset.py`` honors the +# ``distribution: log_uniform`` declaration below by transforming theta to log10 space at +# load time and building a matching BoxUniform prior in log space. + +slice: + policy: pi0_remote + task: pick_and_place_maple_table + embodiment: droid_abs_joint_pos + +factors: + light_intensity: + type: continuous + dim: 1 + # Spans 5.7 decades from effectively-pitch-black to bright, sampled LOG-uniformly + # so each decade gets equal density. Lower bound at 0.01 (rather than 0) because + # log(0) is undefined; 0.01 is so far below pi0's failure threshold (~10) that + # camera frames are pixel-black there — practical equivalent of "dark." + range: [[0.01, 5000]] + distribution: log_uniform + pick_up_object: + type: categorical + # Same five Robolab assets used in the synthetic categorical generator. Spans + # easy (cube), medium (can, box, mug), to hard (bowl-into-bowl is awkward for pi0). + # Per-job language_instruction in the jobs config describes the specific object so + # pi0 conditions on the right thing. + choices: + - rubiks_cube_hot3d_robolab + - alphabet_soup_can_hope_robolab + - sugar_box_ycb_robolab + - mug_ycb_robolab + - wooden_bowl_hot3d_robolab + +outcomes: + success_rate: + # Per-episode value of SuccessRateMetric. Returns 0.0 or 1.0 for a single demo. + type: float + object_moved_rate: + # Per-episode value of ObjectMovedRateMetric. Same shape as success_rate. + type: float + task_duration: + # Per-episode wall-clock-equivalent seconds before termination, computed in + # episode_writer as demo_step_count * env.step_dt. Continuous; informative even + # within all-success episodes (fast successes != slow successes near the edges). + type: float diff --git a/isaaclab_arena_environments/eval_jobs_configs/multi_factor_overnight_sweep_jobs_config.json b/isaaclab_arena_environments/eval_jobs_configs/multi_factor_overnight_sweep_jobs_config.json new file mode 100644 index 0000000000..a62cf31253 --- /dev/null +++ b/isaaclab_arena_environments/eval_jobs_configs/multi_factor_overnight_sweep_jobs_config.json @@ -0,0 +1,22004 @@ +{ + "jobs": [ + { + "name": "multi_0000", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 44.06301, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0001", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 168.2961, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0002", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.1871, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0003", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 71.86047, + "pick_up_object": "wooden_bowl_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the wooden bowl and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0004", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03129, + "pick_up_object": "mug_ycb_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the mug and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0005", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01517, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0006", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.1762, + "pick_up_object": "wooden_bowl_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the wooden bowl and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0007", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 26.97036, + "pick_up_object": "wooden_bowl_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the wooden bowl and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0008", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.13588, + "pick_up_object": "wooden_bowl_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the wooden bowl and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0009", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.45941, + "pick_up_object": "mug_ycb_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the mug and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0010", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 22.81432, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0011", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 211.06704, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0012", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 95.20844, + "pick_up_object": "sugar_box_ycb_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the sugar box and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0013", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.38334, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0014", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2851.86252, + "pick_up_object": "sugar_box_ycb_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the sugar box and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0015", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03824, + "pick_up_object": "mug_ycb_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the mug and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0016", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03558, + "pick_up_object": "sugar_box_ycb_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the sugar box and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0017", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 27.58142, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0018", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 144.11844, + "pick_up_object": "wooden_bowl_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the wooden bowl and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0019", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.05145, + "pick_up_object": "mug_ycb_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the mug and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0020", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.02812, + "pick_up_object": "sugar_box_ycb_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the sugar box and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0021", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 533.0308, + "pick_up_object": "wooden_bowl_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the wooden bowl and place it in the bowl.", + "policy_type": 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"environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.03617, + "pick_up_object": "wooden_bowl_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the wooden bowl and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0318", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.33852, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": 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"isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0988", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 461.22049, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0989", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.45215, + "pick_up_object": "sugar_box_ycb_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the sugar box and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0990", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2781.66113, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0991", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.94171, + "pick_up_object": "mug_ycb_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the mug and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0992", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 54.74759, + "pick_up_object": "mug_ycb_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the mug and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0993", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.16577, + "pick_up_object": "wooden_bowl_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the wooden bowl and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0994", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.3509, + "pick_up_object": "wooden_bowl_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the wooden bowl and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0995", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 92.78107, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0996", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04193, + "pick_up_object": "wooden_bowl_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the wooden bowl and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0997", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.23103, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0998", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 65.66949, + "pick_up_object": "wooden_bowl_hot3d_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the wooden bowl and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "multi_0999", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 4, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.19324, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 4, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + } + ] +} diff --git a/isaaclab_arena_environments/eval_jobs_configs/pick_up_object_sweep_factors.yaml b/isaaclab_arena_environments/eval_jobs_configs/pick_up_object_sweep_factors.yaml new file mode 100644 index 0000000000..ab5eb24cdf --- /dev/null +++ b/isaaclab_arena_environments/eval_jobs_configs/pick_up_object_sweep_factors.yaml @@ -0,0 +1,26 @@ +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +# Sensitivity-analysis schema for the pick_up_object sweep on droid + pi0. +# Paired with: pick_up_object_sweep_minimal_jobs_config.json +# Hand-authored — must stay in sync with --factor_keys passed to eval_runner. + +slice: + policy: pi0_remote + task: pick_and_place_maple_table + embodiment: droid_abs_joint_pos + +factors: + pick_up_object: + type: categorical + # Three objects with distinct visual / shape characteristics. List them in the order + # the analyzer should use as integer codes (0=rubiks_cube, 1=alphabet_soup_can, 2=sugar_box). + choices: [rubiks_cube_hot3d_robolab, alphabet_soup_can_hope_robolab, sugar_box_ycb_robolab] + +outcomes: + success_rate: + type: float + object_moved_rate: + type: float diff --git a/isaaclab_arena_environments/eval_jobs_configs/pick_up_object_sweep_minimal_jobs_config.json b/isaaclab_arena_environments/eval_jobs_configs/pick_up_object_sweep_minimal_jobs_config.json new file mode 100644 index 0000000000..fc2b3950c9 --- /dev/null +++ b/isaaclab_arena_environments/eval_jobs_configs/pick_up_object_sweep_minimal_jobs_config.json @@ -0,0 +1,70 @@ +{ + "jobs": [ + { + "name": "pick_up_object_minimal_rubiks_cube", + "arena_env_args": { + "enable_cameras": true, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 500, + "pick_up_object": "rubiks_cube_hot3d_robolab", + "destination_location": "wooden_bowl_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "pick_up_object_minimal_alphabet_soup_can", + "arena_env_args": { + "enable_cameras": true, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 500, + "pick_up_object": "alphabet_soup_can_hope_robolab", + "destination_location": "wooden_bowl_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "pick_up_object_minimal_sugar_box", + "arena_env_args": { + "enable_cameras": true, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 500, + "pick_up_object": "sugar_box_ycb_robolab", + "destination_location": "wooden_bowl_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the sugar box and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + } + ] +} diff --git a/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_factors.yaml b/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_factors.yaml new file mode 100644 index 0000000000..d4d3498c24 --- /dev/null +++ b/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_factors.yaml @@ -0,0 +1,47 @@ +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +# Sensitivity-analysis schema for the focused two-object shiny-vs-matte sweep on +# droid + pi0. Paired with: two_object_shiny_matte_sweep_jobs_config.json +# (300 jobs x num_envs=2 x num_episodes=2 = ~600 expected episodes). +# +# Hypothesis: a reflective (shiny) object and a diffuse (matte) object should have +# different competence-vs-light curves. The prior 5-object run hinted at this (the +# matte wooden bowl peaked at low light, the more-specular soup can at high light); +# this run pairs a plastic mustard bottle (semi-matte, easy compact grasp) against +# the metal soup can (specular) and samples both densely across the light range. +# +# Sampling distribution: ``light_intensity`` drawn LOG-uniformly across [0.01, 5000] +# (5.7 decades, equal density per decade), same as the prior sweep so the two runs +# are directly comparable. The analyzer honors ``distribution: log_uniform`` by +# transforming theta to log10 space and building a matching BoxUniform prior there. + +slice: + policy: pi0_remote + task: pick_and_place_maple_table + embodiment: droid_abs_joint_pos + +factors: + light_intensity: + type: continuous + dim: 1 + range: [[0.01, 5000]] + distribution: log_uniform + pick_up_object: + type: categorical + # Two objects chosen for contrasting surface reflectance: + # mustard — plastic bottle, semi-diffuse/MATTE-ish, easy compact grasp + # alphabet_soup_can — metal can w/ label, more specular/SHINY + choices: + - mustard_ycb_robolab + - alphabet_soup_can_hope_robolab + +outcomes: + success_rate: + type: float + object_moved_rate: + type: float + task_duration: + type: float diff --git a/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_jobs_config.json b/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_jobs_config.json new file mode 100644 index 0000000000..cef5310fef --- /dev/null +++ b/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_jobs_config.json @@ -0,0 +1,6604 @@ +{ + "jobs": [ + { + "name": "shinymatte_mustard_0000", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 257.48522, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0001", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 511.69702, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0002", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.1707, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0003", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1279.95104, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0004", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 781.85063, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0005", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06299, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0006", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 94.24959, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0007", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 14.36929, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0008", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03441, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0009", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04157, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0010", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3631.13602, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0011", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 67.77603, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0012", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 217.62678, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0013", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.40063, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0014", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 301.82617, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0015", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 57.28354, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0016", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.05372, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0017", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 139.03383, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0018", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.68753, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0019", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 240.15907, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0020", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.29768, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0021", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04112, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0022", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1912.5195, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0023", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1660.82559, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0024", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 46.7055, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0025", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.20511, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0026", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 488.53283, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0027", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01634, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0028", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.36515, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0029", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 14.52404, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0030", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.19725, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0031", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.2998, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0032", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 14.47312, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0033", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 535.7311, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0034", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.0231, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0035", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 403.83215, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0036", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 520.76912, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0037", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.64175, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0038", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 39.79573, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0039", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + 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"pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.04817, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0043", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 8.61579, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.28757, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0046", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1229.90192, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0047", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2160.15159, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0048", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 272.88785, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0049", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.08672, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0050", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.1286, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0051", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01803, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0052", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4.56908, + "pick_up_object": "mustard_ycb_robolab" + }, + 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"openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0054", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01777, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0055", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4523.95809, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0056", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.07573, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0057", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1206.8186, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0058", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 78.1044, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0059", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 184.62676, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0060", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 175.54044, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0061", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1192.88804, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0062", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3264.4419, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + 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"shinymatte_mustard_0070", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.12017, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0071", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 58.99126, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + 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"droid" + } + }, + { + "name": "shinymatte_alphabet_0073", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.3473, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0074", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 5.14077, + "pick_up_object": "mustard_ycb_robolab" + }, + 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"openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0076", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.1964, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0077", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 180.27338, + "pick_up_object": 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"policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0082", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 556.42683, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0083", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.2362, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0084", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 97.9016, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0085", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.05007, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0086", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.60279, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0087", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 545.11256, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.07474, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0090", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 385.76954, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and 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"shinymatte_mustard_0092", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.61521, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0093", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 26.05341, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0094", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.43972, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0095", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 964.05728, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0096", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 77.53922, + "pick_up_object": "mustard_ycb_robolab" + 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8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0098", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06258, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0099", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.58685, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0100", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.13781, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0101", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 269.40575, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0102", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": 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"policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0104", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 305.25193, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0105", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 7.14018, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0106", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 61.51274, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.20361, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0112", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4.12427, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0113", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.0564, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0114", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 17.42766, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0115", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 72.77068, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0116", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06262, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0117", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04947, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0118", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04495, + "pick_up_object": "mustard_ycb_robolab" + }, + 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"openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0120", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 64.44785, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0121", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 90.48588, + "pick_up_object": 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"127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0123", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 20.50058, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0124", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + 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"policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0126", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 228.93149, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0127", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.05583, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0134", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.53976, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place 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"droid" + } + }, + { + "name": "shinymatte_alphabet_0139", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.84411, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0140", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.16708, + "pick_up_object": "mustard_ycb_robolab" + }, + 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"openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0142", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.12908, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0143", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 6.08732, + "pick_up_object": 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"127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0145", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 59.92986, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0146", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + 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"policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0148", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.02149, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0149", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + 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"policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0151", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1864.04116, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0152", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + 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"shinymatte_mustard_0158", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 293.36776, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0159", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04013, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + 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"droid" + } + }, + { + "name": "shinymatte_alphabet_0161", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06307, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0162", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.07008, + "pick_up_object": "mustard_ycb_robolab" + }, + 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"openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0164", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 435.58985, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0165", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3210.16004, + "pick_up_object": 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"127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0167", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 24.93611, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0168", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01347, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0169", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2076.21036, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0170", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.0326, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0171", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 383.73263, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0172", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 130.82908, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01264, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0178", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 7.16868, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place 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"shinymatte_mustard_0180", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.07379, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0181", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 533.19835, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0182", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 92.96274, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0183", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 347.56552, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0184", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.48844, + "pick_up_object": "mustard_ycb_robolab" + }, 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+ "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0186", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.48398, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0187", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 10.58863, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0188", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.52277, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0189", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 28.42274, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0190", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 39.08063, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0191", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 881.49178, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0192", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.15334, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0193", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 27.35833, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0194", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03159, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { 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"embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04704, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0197", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.35664, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.67356, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0200", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1506.51723, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0201", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 51.92008, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0202", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 97.1874, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0203", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 878.62406, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0204", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.32748, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0205", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.8614, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0206", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3336.62273, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0207", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.25848, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0208", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 274.20669, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0209", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.22322, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0210", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 121.7687, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0211", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 178.44854, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0212", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.63829, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0213", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 450.4026, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0214", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.35604, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0215", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03981, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0216", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03543, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0217", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.02395, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0218", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1392.84883, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0219", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 24.41516, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0220", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.95781, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0221", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06808, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0222", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.14232, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0223", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 500.88322, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0224", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.55416, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0225", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.58693, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0226", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 19.99654, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0227", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06606, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0228", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.10172, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0229", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1772.48716, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0230", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 761.76166, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0231", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.08777, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0232", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 210.27132, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0233", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.41938, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0234", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": 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"policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0236", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.90058, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0237", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04552, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0238", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 37.58498, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + 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"pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 21.31851, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0241", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.02069, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.09892, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0244", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03029, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place 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"shinymatte_mustard_0246", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.34247, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0247", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 23.38357, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + 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"droid" + } + }, + { + "name": "shinymatte_alphabet_0249", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 75.74809, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0250", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 6.5349, + "pick_up_object": "mustard_ycb_robolab" + }, + 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"openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0252", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.75835, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0253", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.64897, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0254", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06663, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0255", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 7.50378, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0256", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03884, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0257", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 969.67661, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0258", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 22.33412, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0259", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 708.88145, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0260", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.0938, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0261", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01769, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0262", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1871.68089, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0263", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.10823, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0264", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 20.48566, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0265", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.22346, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0266", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.94798, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0267", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.26379, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0268", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 23.31362, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0269", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 18.00685, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0270", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01349, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0271", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.3565, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0272", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2902.68697, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0273", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01909, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0274", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 5.60574, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0275", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.34653, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0276", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 288.92464, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0277", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 9.65722, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0278", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.02961, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0279", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03797, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0280", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 5.93542, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0281", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 562.15399, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0282", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 6.25928, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0283", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01978, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0284", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2211.28559, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0285", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1864.8593, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0286", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 18.12429, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0287", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03671, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0288", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4.99346, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0289", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 641.96056, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0290", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.33227, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0291", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1393.77667, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0292", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.77554, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0293", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3824.22931, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0294", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 9.27463, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0295", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 372.15292, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0296", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.17207, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0297", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 276.83381, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_mustard_0298", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01328, + "pick_up_object": "mustard_ycb_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0299", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 45.86622, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + } + ] +} \ No newline at end of file From 5a4e50d1ea4c3e02e7b41784180956b99b7b056f Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 4 Jun 2026 10:23:35 +0200 Subject: [PATCH 03/74] Fix episode_writer for MetricsCfg configclass main's metrics refactor (#733) made cfg.metrics a MetricsCfg configclass (one field per metric) rather than an iterable of metric objects. Iterate its fields and use compute_metric_func/recorder_term_name/params, matching MetricsManager. Fixes 'MetricsCfg object is not iterable' that produced empty episode-summary JSONL. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/episode_writer.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/episode_writer.py b/isaaclab_arena/analysis/sensitivity/episode_writer.py index 199c3479b9..577cc23b88 100644 --- a/isaaclab_arena/analysis/sensitivity/episode_writer.py +++ b/isaaclab_arena/analysis/sensitivity/episode_writer.py @@ -87,9 +87,15 @@ def write_episode_summaries(env, job: Job, output_path: str | Path) -> int: # the SuccessRecorder) produce a (1,) array regardless of episode length — # using `len()` on the wrong one collapses task_duration to a single step. demo_step_count = 0 - for metric in registered_metrics: - recorded_metric_data = demo_group[metric.recorder_term_name][:] - raw_outcome_values[metric.name] = metric.compute_metric_from_recording([recorded_metric_data]) + # cfg.metrics is a MetricsCfg configclass: one field per metric, each a + # MetricTermCfg(compute_metric_func, params, recorder_term_name). Iterate its + # fields the same way MetricsManager does (metrics_manager.py). The per-demo + # value comes from feeding compute_metric_func a single-element list. + for metric_name, metric_cfg in registered_metrics.__dict__.items(): + recorded_metric_data = demo_group[metric_cfg.recorder_term_name][:] + raw_outcome_values[metric_name] = metric_cfg.compute_metric_func( + [recorded_metric_data], **metric_cfg.params + ) demo_step_count = max(demo_step_count, len(recorded_metric_data)) # task_duration: wall-clock-equivalent seconds spent on this episode before # termination. Short for fast successes / early failures, max_episode_length From ee5b406b2e2c3c4fe0469c96ce092b145d1e90ba Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 4 Jun 2026 10:31:46 +0200 Subject: [PATCH 04/74] Discard sbi training logs during report fits MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit sbi logs TensorBoard training curves under /sbi-logs by default (get_log_root hardcodes the cwd), so fitting raised PermissionError when the cwd wasn't writable — e.g. generating a report from a repo checkout in a non-root container. A one-shot report fit never reads those curves, so pass a no-op tracker (_NullTracker) that discards them: no files written, no hidden cwd dependency, runs from any directory. Centralize the tracker on the base by having subclasses name their sbi class via _inference_cls. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/analyzer.py | 59 +++++++++++++++---- 1 file changed, 47 insertions(+), 12 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/analyzer.py b/isaaclab_arena/analysis/sensitivity/analyzer.py index 99bf25dbd2..5075e2d3c6 100644 --- a/isaaclab_arena/analysis/sensitivity/analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/analyzer.py @@ -103,6 +103,36 @@ def _factor_spec(self, factor_name: str) -> FactorSpec: return next(factor for factor in self.dataset.schema.factors if factor.name == factor_name) +class _NullTracker: + """A no-op tracker satisfying sbi's ``Tracker`` protocol — discards training metrics. + + By default sbi logs TensorBoard training curves under ``/sbi-logs`` + (``get_log_root`` hardcodes the cwd). One-shot report generation fits each analyzer + once and never reads those curves, yet the default tracker makes fitting fail with + ``PermissionError`` whenever the cwd isn't writable — e.g. a repo checkout in a + non-root container. Discarding the metrics removes the write, and with it the hidden + cwd dependency, so analysis runs from any directory. sbi only calls ``log_metric`` + and ``flush``; the remaining members satisfy the protocol. + """ + + log_dir = None + + def log_metric(self, name, value, step=None): + pass + + def log_metrics(self, metrics, step=None): + pass + + def log_params(self, params): + pass + + def add_figure(self, name, figure, step=None): + pass + + def flush(self): + pass + + class PosteriorAnalyzer(BaseAnalyzer): """Common base for the sbi-driven analyzers (NPE and MNPE). @@ -119,15 +149,22 @@ def __init__(self, dataset: SensitivityDataset, outcome_name: str): super().__init__(dataset, outcome_name) self.posterior = None + def _inference_cls(self): + """Return the sbi inference *class* to train with (e.g. ``sbi.inference.NPE``). + + Subclass-specific: ``NPEAnalyzer`` returns ``NPE``, ``MNPEAnalyzer`` returns + ``MNPE``. The lazy import of sbi lives in the subclass so callers don't pay the + (heavy) sbi import cost until they actually fit. + """ + raise NotImplementedError("PosteriorAnalyzer subclasses must implement _inference_cls") + def _make_inference(self): - """Return the sbi inference object to train with. + """Instantiate the chosen sbi inference class on the dataset's uniform prior. - Subclass-specific: ``NPEAnalyzer`` returns ``sbi.inference.NPE(...)``, - ``MNPEAnalyzer`` returns ``sbi.inference.MNPE(...)``. The lazy import of sbi - lives in the subclass so callers don't pay the (heavy) sbi import cost until - they actually fit. + Passes a ``_NullTracker`` so fitting writes no TensorBoard logs and stays + independent of the launch directory — see ``_NullTracker`` for why. """ - raise NotImplementedError("PosteriorAnalyzer subclasses must implement _make_inference") + return self._inference_cls()(prior=self.dataset.prior, tracker=_NullTracker()) def fit(self, training_batch_size: int = 50) -> None: """Train the chosen sbi estimator on ``(theta, x_selected)`` and stash the posterior. @@ -256,11 +293,10 @@ class NPEAnalyzer(PosteriorAnalyzer): rather than buried in sbi's UserWarning stream. """ - def _make_inference(self): - """Construct ``sbi.inference.NPE`` configured with the dataset's uniform prior.""" + def _inference_cls(self): from sbi.inference import NPE - return NPE(prior=self.dataset.prior) + return NPE def _maybe_warn_binary_outcome(self, selected_outcome_column: torch.Tensor) -> None: """Warn if theta is 1D and the outcome is binary — the configuration that triggers @@ -293,11 +329,10 @@ class MNPEAnalyzer(PosteriorAnalyzer): schemas use ``EmpiricalAnalyzer`` instead — ``make_analyzer`` dispatches correctly. """ - def _make_inference(self): - """Construct ``sbi.inference.MNPE`` configured with the dataset's uniform prior.""" + def _inference_cls(self): from sbi.inference import MNPE - return MNPE(prior=self.dataset.prior) + return MNPE class EmpiricalAnalyzer(BaseAnalyzer): From 95d1d840ca027bc386c47d8efbe12c5ee0d8ac02 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 4 Jun 2026 10:31:51 +0200 Subject: [PATCH 05/74] Expand shiny/matte sweep to 1000 jobs Grow the two-object light sweep to 500 rubiks_cube + 500 alphabet_soup_can (num_envs=2, num_episodes=2) for an overnight run with denser log-uniform light sampling. Update the factors.yaml header to match. Signed-off-by: Clemens Volk --- .../two_object_shiny_matte_sweep_factors.yaml | 19 +- ..._object_shiny_matte_sweep_jobs_config.json | 16600 +++++++++++++++- 2 files changed, 16011 insertions(+), 608 deletions(-) diff --git a/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_factors.yaml b/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_factors.yaml index d4d3498c24..b0fe329c4a 100644 --- a/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_factors.yaml +++ b/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_factors.yaml @@ -5,13 +5,16 @@ # Sensitivity-analysis schema for the focused two-object shiny-vs-matte sweep on # droid + pi0. Paired with: two_object_shiny_matte_sweep_jobs_config.json -# (300 jobs x num_envs=2 x num_episodes=2 = ~600 expected episodes). +# (1000 jobs = 500 rubiks_cube + 500 alphabet_soup_can, x num_envs=2 x +# num_episodes=2 = up to 4000 expected episodes). # -# Hypothesis: a reflective (shiny) object and a diffuse (matte) object should have -# different competence-vs-light curves. The prior 5-object run hinted at this (the -# matte wooden bowl peaked at low light, the more-specular soup can at high light); -# this run pairs a plastic mustard bottle (semi-matte, easy compact grasp) against -# the metal soup can (specular) and samples both densely across the light range. +# Hypothesis: surface reflectance interacts with lighting in pi0's competence curve. +# This run pairs the Rubik's cube (matte plastic body with glossy stickers — less +# specular overall) against the metal soup can (more specular) and samples both +# densely across the light range. NOTE: rubiks_cube replaced the mustard bottle +# because mustard failed to build repeatedly in the eval_runner path (contact-sensor +# `descendants=0`); rubiks_cube is proven-reliable (281 episodes in the prior +# 5-object sweep). The matte/shiny contrast is therefore weaker than intended. # # Sampling distribution: ``light_intensity`` drawn LOG-uniformly across [0.01, 5000] # (5.7 decades, equal density per decade), same as the prior sweep so the two runs @@ -32,10 +35,10 @@ factors: pick_up_object: type: categorical # Two objects chosen for contrasting surface reflectance: - # mustard — plastic bottle, semi-diffuse/MATTE-ish, easy compact grasp + # rubiks_cube — matte plastic body + glossy stickers (less specular), proven-reliable # alphabet_soup_can — metal can w/ label, more specular/SHINY choices: - - mustard_ycb_robolab + - rubiks_cube_hot3d_robolab - alphabet_soup_can_hope_robolab outcomes: diff --git a/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_jobs_config.json b/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_jobs_config.json index cef5310fef..6c0bd8c8c9 100644 --- a/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_jobs_config.json +++ b/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_jobs_config.json @@ -1,7 +1,7 @@ { "jobs": [ { - "name": "shinymatte_mustard_0000", + "name": "shinymatte_rubiks_0000", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -9,10 +9,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 257.48522, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -30,7 +30,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 511.69702, + "light_intensity": 157.92963, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -45,7 +45,7 @@ } }, { - "name": "shinymatte_mustard_0002", + "name": "shinymatte_rubiks_0002", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -53,10 +53,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 3.1707, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -74,7 +74,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 1279.95104, + "light_intensity": 1126.11687, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -89,7 +89,7 @@ } }, { - "name": "shinymatte_mustard_0004", + "name": "shinymatte_rubiks_0004", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -97,10 +97,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 781.85063, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -118,7 +118,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.06299, + "light_intensity": 1774.50546, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -133,7 +133,7 @@ } }, { - "name": "shinymatte_mustard_0006", + "name": "shinymatte_rubiks_0006", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -141,10 +141,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 94.24959, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -162,7 +162,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 14.36929, + "light_intensity": 7.41633, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -177,7 +177,7 @@ } }, { - "name": "shinymatte_mustard_0008", + "name": "shinymatte_rubiks_0008", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -185,10 +185,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.03441, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -206,7 +206,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.04157, + "light_intensity": 9.2264, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -221,7 +221,7 @@ } }, { - "name": "shinymatte_mustard_0010", + "name": "shinymatte_rubiks_0010", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -229,10 +229,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 3631.13602, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -250,7 +250,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 67.77603, + "light_intensity": 361.77411, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -265,7 +265,7 @@ } }, { - "name": "shinymatte_mustard_0012", + "name": "shinymatte_rubiks_0012", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -273,10 +273,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 217.62678, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -294,7 +294,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.40063, + "light_intensity": 0.61951, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -309,7 +309,7 @@ } }, { - "name": "shinymatte_mustard_0014", + "name": "shinymatte_rubiks_0014", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -317,10 +317,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 301.82617, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -338,7 +338,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 57.28354, + "light_intensity": 591.85724, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -353,7 +353,7 @@ } }, { - "name": "shinymatte_mustard_0016", + "name": "shinymatte_rubiks_0016", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -361,10 +361,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.05372, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -382,7 +382,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 139.03383, + "light_intensity": 6.54785, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -397,7 +397,7 @@ } }, { - "name": "shinymatte_mustard_0018", + "name": "shinymatte_rubiks_0018", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -405,10 +405,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 3.68753, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -426,7 +426,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 240.15907, + "light_intensity": 0.04574, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -441,7 +441,7 @@ } }, { - "name": "shinymatte_mustard_0020", + "name": "shinymatte_rubiks_0020", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -449,10 +449,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 1.29768, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -470,7 +470,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.04112, + "light_intensity": 0.02574, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -485,7 +485,7 @@ } }, { - "name": "shinymatte_mustard_0022", + "name": "shinymatte_rubiks_0022", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -493,10 +493,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 1912.5195, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -514,7 +514,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 1660.82559, + "light_intensity": 628.77324, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -529,7 +529,7 @@ } }, { - "name": "shinymatte_mustard_0024", + "name": "shinymatte_rubiks_0024", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -537,10 +537,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 46.7055, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -558,7 +558,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.20511, + "light_intensity": 0.02073, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -573,7 +573,7 @@ } }, { - "name": "shinymatte_mustard_0026", + "name": "shinymatte_rubiks_0026", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -581,10 +581,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 488.53283, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -602,7 +602,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.01634, + "light_intensity": 0.39737, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -617,7 +617,7 @@ } }, { - "name": "shinymatte_mustard_0028", + "name": "shinymatte_rubiks_0028", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -625,10 +625,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 3.36515, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -646,7 +646,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 14.52404, + "light_intensity": 0.80204, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -661,7 +661,7 @@ } }, { - "name": "shinymatte_mustard_0030", + "name": "shinymatte_rubiks_0030", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -669,10 +669,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.19725, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -690,7 +690,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 1.2998, + "light_intensity": 0.0968, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -705,7 +705,7 @@ } }, { - "name": "shinymatte_mustard_0032", + "name": "shinymatte_rubiks_0032", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -713,10 +713,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 14.47312, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -734,7 +734,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 535.7311, + "light_intensity": 0.61499, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -749,7 +749,7 @@ } }, { - "name": "shinymatte_mustard_0034", + "name": "shinymatte_rubiks_0034", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -757,10 +757,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.0231, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -778,7 +778,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 403.83215, + "light_intensity": 170.83728, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -793,7 +793,7 @@ } }, { - "name": "shinymatte_mustard_0036", + "name": "shinymatte_rubiks_0036", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -801,10 +801,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 520.76912, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -822,7 +822,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.64175, + "light_intensity": 0.01212, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -837,7 +837,7 @@ } }, { - "name": "shinymatte_mustard_0038", + "name": "shinymatte_rubiks_0038", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -845,10 +845,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 39.79573, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -866,7 +866,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 2694.91499, + "light_intensity": 517.65038, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -881,7 +881,7 @@ } }, { - "name": "shinymatte_mustard_0040", + "name": "shinymatte_rubiks_0040", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -889,10 +889,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 209.08327, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -910,7 +910,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.45492, + "light_intensity": 761.0996, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -925,7 +925,7 @@ } }, { - "name": "shinymatte_mustard_0042", + "name": "shinymatte_rubiks_0042", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -933,10 +933,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 1.04817, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -954,7 +954,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 8.61579, + "light_intensity": 1.32284, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -969,7 +969,7 @@ } }, { - "name": "shinymatte_mustard_0044", + "name": "shinymatte_rubiks_0044", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -977,10 +977,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 3403.91671, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -998,7 +998,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.28757, + "light_intensity": 0.07506, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1013,7 +1013,7 @@ } }, { - "name": "shinymatte_mustard_0046", + "name": "shinymatte_rubiks_0046", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1021,10 +1021,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 1229.90192, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1042,7 +1042,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 2160.15159, + "light_intensity": 26.55654, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1057,7 +1057,7 @@ } }, { - "name": "shinymatte_mustard_0048", + "name": "shinymatte_rubiks_0048", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1065,10 +1065,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 272.88785, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1086,7 +1086,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.08672, + "light_intensity": 0.04809, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1101,7 +1101,7 @@ } }, { - "name": "shinymatte_mustard_0050", + "name": "shinymatte_rubiks_0050", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1109,10 +1109,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.1286, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1130,7 +1130,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.01803, + "light_intensity": 1.20134, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1145,7 +1145,7 @@ } }, { - "name": "shinymatte_mustard_0052", + "name": "shinymatte_rubiks_0052", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1153,10 +1153,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 4.56908, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1174,7 +1174,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 3.01721, + "light_intensity": 2897.73825, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1189,7 +1189,7 @@ } }, { - "name": "shinymatte_mustard_0054", + "name": "shinymatte_rubiks_0054", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1197,10 +1197,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.01777, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1218,7 +1218,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 4523.95809, + "light_intensity": 4711.09834, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1233,7 +1233,7 @@ } }, { - "name": "shinymatte_mustard_0056", + "name": "shinymatte_rubiks_0056", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1241,10 +1241,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.07573, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1262,7 +1262,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 1206.8186, + "light_intensity": 251.30586, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1277,7 +1277,7 @@ } }, { - "name": "shinymatte_mustard_0058", + "name": "shinymatte_rubiks_0058", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1285,10 +1285,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 78.1044, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1306,7 +1306,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 184.62676, + "light_intensity": 0.59178, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1321,7 +1321,7 @@ } }, { - "name": "shinymatte_mustard_0060", + "name": "shinymatte_rubiks_0060", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1329,10 +1329,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 175.54044, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1350,7 +1350,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 1192.88804, + "light_intensity": 82.98173, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1365,7 +1365,7 @@ } }, { - "name": "shinymatte_mustard_0062", + "name": "shinymatte_rubiks_0062", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1373,10 +1373,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 3264.4419, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1394,7 +1394,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 1235.16677, + "light_intensity": 104.73449, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1409,7 +1409,7 @@ } }, { - "name": "shinymatte_mustard_0064", + "name": "shinymatte_rubiks_0064", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1417,10 +1417,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.71923, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1438,7 +1438,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 9.05645, + "light_intensity": 1.62292, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1453,7 +1453,7 @@ } }, { - "name": "shinymatte_mustard_0066", + "name": "shinymatte_rubiks_0066", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1461,10 +1461,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 1.29193, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1482,7 +1482,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.63164, + "light_intensity": 44.91642, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1497,7 +1497,7 @@ } }, { - "name": "shinymatte_mustard_0068", + "name": "shinymatte_rubiks_0068", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1505,10 +1505,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 4.74225, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1526,7 +1526,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 251.00114, + "light_intensity": 0.01151, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1541,7 +1541,7 @@ } }, { - "name": "shinymatte_mustard_0070", + "name": "shinymatte_rubiks_0070", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1549,10 +1549,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.12017, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1570,7 +1570,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 58.99126, + "light_intensity": 0.15539, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1585,7 +1585,7 @@ } }, { - "name": "shinymatte_mustard_0072", + "name": "shinymatte_rubiks_0072", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1593,10 +1593,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.05501, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1614,7 +1614,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 1.3473, + "light_intensity": 9.82794, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1629,7 +1629,7 @@ } }, { - "name": "shinymatte_mustard_0074", + "name": "shinymatte_rubiks_0074", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1637,10 +1637,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 5.14077, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1658,7 +1658,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.03454, + "light_intensity": 0.08575, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1673,7 +1673,7 @@ } }, { - "name": "shinymatte_mustard_0076", + "name": "shinymatte_rubiks_0076", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1681,10 +1681,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.1964, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1702,7 +1702,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 180.27338, + "light_intensity": 0.08821, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1717,7 +1717,7 @@ } }, { - "name": "shinymatte_mustard_0078", + "name": "shinymatte_rubiks_0078", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1725,10 +1725,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 65.65229, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1746,7 +1746,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.31315, + "light_intensity": 583.54327, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1761,7 +1761,7 @@ } }, { - "name": "shinymatte_mustard_0080", + "name": "shinymatte_rubiks_0080", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1769,10 +1769,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 3.09967, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1790,7 +1790,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 2182.07686, + "light_intensity": 4335.50122, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1805,7 +1805,7 @@ } }, { - "name": "shinymatte_mustard_0082", + "name": "shinymatte_rubiks_0082", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1813,10 +1813,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 556.42683, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1834,7 +1834,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.2362, + "light_intensity": 14.73849, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1849,7 +1849,7 @@ } }, { - "name": "shinymatte_mustard_0084", + "name": "shinymatte_rubiks_0084", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1857,10 +1857,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 97.9016, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1878,7 +1878,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.05007, + "light_intensity": 605.10846, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1893,7 +1893,7 @@ } }, { - "name": "shinymatte_mustard_0086", + "name": "shinymatte_rubiks_0086", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1901,10 +1901,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.60279, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1922,7 +1922,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 545.11256, + "light_intensity": 4403.65675, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1937,7 +1937,7 @@ } }, { - "name": "shinymatte_mustard_0088", + "name": "shinymatte_rubiks_0088", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1945,10 +1945,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 553.38024, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -1966,7 +1966,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.07474, + "light_intensity": 0.06411, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -1981,7 +1981,7 @@ } }, { - "name": "shinymatte_mustard_0090", + "name": "shinymatte_rubiks_0090", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -1989,10 +1989,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 385.76954, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2010,7 +2010,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.10511, + "light_intensity": 3.58541, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2025,7 +2025,7 @@ } }, { - "name": "shinymatte_mustard_0092", + "name": "shinymatte_rubiks_0092", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2033,10 +2033,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 1.61521, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2054,7 +2054,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 26.05341, + "light_intensity": 1.72687, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2069,7 +2069,7 @@ } }, { - "name": "shinymatte_mustard_0094", + "name": "shinymatte_rubiks_0094", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2077,10 +2077,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.43972, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2098,7 +2098,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 964.05728, + "light_intensity": 0.02859, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2113,7 +2113,7 @@ } }, { - "name": "shinymatte_mustard_0096", + "name": "shinymatte_rubiks_0096", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2121,10 +2121,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 77.53922, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2142,7 +2142,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.13167, + "light_intensity": 201.65268, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2157,7 +2157,7 @@ } }, { - "name": "shinymatte_mustard_0098", + "name": "shinymatte_rubiks_0098", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2165,10 +2165,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.06258, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2186,7 +2186,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.58685, + "light_intensity": 2.96547, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2201,7 +2201,7 @@ } }, { - "name": "shinymatte_mustard_0100", + "name": "shinymatte_rubiks_0100", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2209,10 +2209,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.13781, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2230,7 +2230,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 269.40575, + "light_intensity": 4.72803, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2245,7 +2245,7 @@ } }, { - "name": "shinymatte_mustard_0102", + "name": "shinymatte_rubiks_0102", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2253,10 +2253,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.01101, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2274,7 +2274,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 3454.69452, + "light_intensity": 0.07222, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2289,7 +2289,7 @@ } }, { - "name": "shinymatte_mustard_0104", + "name": "shinymatte_rubiks_0104", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2297,10 +2297,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 305.25193, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2318,7 +2318,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 7.14018, + "light_intensity": 0.10742, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2333,7 +2333,7 @@ } }, { - "name": "shinymatte_mustard_0106", + "name": "shinymatte_rubiks_0106", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2341,10 +2341,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 61.51274, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2362,7 +2362,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.06608, + "light_intensity": 1477.59813, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2377,7 +2377,7 @@ } }, { - "name": "shinymatte_mustard_0108", + "name": "shinymatte_rubiks_0108", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2385,10 +2385,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 104.40381, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2406,7 +2406,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.01201, + "light_intensity": 0.01797, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2421,7 +2421,7 @@ } }, { - "name": "shinymatte_mustard_0110", + "name": "shinymatte_rubiks_0110", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2429,10 +2429,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 281.41765, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2450,7 +2450,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.20361, + "light_intensity": 0.21233, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2465,7 +2465,7 @@ } }, { - "name": "shinymatte_mustard_0112", + "name": "shinymatte_rubiks_0112", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2473,10 +2473,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 4.12427, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2494,7 +2494,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.0564, + "light_intensity": 0.46178, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2509,7 +2509,7 @@ } }, { - "name": "shinymatte_mustard_0114", + "name": "shinymatte_rubiks_0114", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2517,10 +2517,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 17.42766, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2538,7 +2538,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 72.77068, + "light_intensity": 6.21758, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2553,7 +2553,7 @@ } }, { - "name": "shinymatte_mustard_0116", + "name": "shinymatte_rubiks_0116", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2561,10 +2561,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.06262, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2582,7 +2582,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.04947, + "light_intensity": 21.98535, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2597,7 +2597,7 @@ } }, { - "name": "shinymatte_mustard_0118", + "name": "shinymatte_rubiks_0118", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2605,10 +2605,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.04495, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2626,7 +2626,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 7.6835, + "light_intensity": 6.47507, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2641,7 +2641,7 @@ } }, { - "name": "shinymatte_mustard_0120", + "name": "shinymatte_rubiks_0120", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2649,10 +2649,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 64.44785, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2670,7 +2670,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 90.48588, + "light_intensity": 0.03016, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2685,7 +2685,7 @@ } }, { - "name": "shinymatte_mustard_0122", + "name": "shinymatte_rubiks_0122", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2693,10 +2693,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 4.83908, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2714,7 +2714,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 20.50058, + "light_intensity": 0.24471, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2729,7 +2729,7 @@ } }, { - "name": "shinymatte_mustard_0124", + "name": "shinymatte_rubiks_0124", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2737,10 +2737,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 16.64423, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2758,7 +2758,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.13757, + "light_intensity": 642.07373, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2773,7 +2773,7 @@ } }, { - "name": "shinymatte_mustard_0126", + "name": "shinymatte_rubiks_0126", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2781,10 +2781,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 228.93149, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2802,7 +2802,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 382.54414, + "light_intensity": 43.01292, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2817,7 +2817,7 @@ } }, { - "name": "shinymatte_mustard_0128", + "name": "shinymatte_rubiks_0128", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2825,10 +2825,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 41.42292, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2846,7 +2846,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 119.42184, + "light_intensity": 50.05886, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2861,7 +2861,7 @@ } }, { - "name": "shinymatte_mustard_0130", + "name": "shinymatte_rubiks_0130", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2869,10 +2869,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 14.28343, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2890,7 +2890,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 162.72252, + "light_intensity": 65.9885, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2905,7 +2905,7 @@ } }, { - "name": "shinymatte_mustard_0132", + "name": "shinymatte_rubiks_0132", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2913,10 +2913,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 15.37818, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2934,7 +2934,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.05583, + "light_intensity": 222.72113, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2949,7 +2949,7 @@ } }, { - "name": "shinymatte_mustard_0134", + "name": "shinymatte_rubiks_0134", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -2957,10 +2957,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.53976, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -2978,7 +2978,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.05073, + "light_intensity": 0.02144, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -2993,7 +2993,7 @@ } }, { - "name": "shinymatte_mustard_0136", + "name": "shinymatte_rubiks_0136", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3001,10 +3001,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.01498, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3022,7 +3022,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 1932.64085, + "light_intensity": 1.22826, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3037,7 +3037,7 @@ } }, { - "name": "shinymatte_mustard_0138", + "name": "shinymatte_rubiks_0138", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3045,10 +3045,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 3.08205, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3066,7 +3066,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 1.84411, + "light_intensity": 11.8782, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3081,7 +3081,7 @@ } }, { - "name": "shinymatte_mustard_0140", + "name": "shinymatte_rubiks_0140", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3089,10 +3089,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.16708, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3110,7 +3110,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.51892, + "light_intensity": 0.84889, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3125,7 +3125,7 @@ } }, { - "name": "shinymatte_mustard_0142", + "name": "shinymatte_rubiks_0142", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3133,10 +3133,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 2.12908, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3154,7 +3154,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 6.08732, + "light_intensity": 649.62057, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3169,7 +3169,7 @@ } }, { - "name": "shinymatte_mustard_0144", + "name": "shinymatte_rubiks_0144", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3177,10 +3177,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 730.32895, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3198,7 +3198,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 59.92986, + "light_intensity": 5.62557, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3213,7 +3213,7 @@ } }, { - "name": "shinymatte_mustard_0146", + "name": "shinymatte_rubiks_0146", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3221,10 +3221,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.21538, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3242,7 +3242,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 2792.9829, + "light_intensity": 240.09636, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3257,7 +3257,7 @@ } }, { - "name": "shinymatte_mustard_0148", + "name": "shinymatte_rubiks_0148", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3265,10 +3265,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.02149, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3286,7 +3286,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.42899, + "light_intensity": 717.15144, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3301,7 +3301,7 @@ } }, { - "name": "shinymatte_mustard_0150", + "name": "shinymatte_rubiks_0150", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3309,10 +3309,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.40142, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3330,7 +3330,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 1864.04116, + "light_intensity": 7.52994, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3345,7 +3345,7 @@ } }, { - "name": "shinymatte_mustard_0152", + "name": "shinymatte_rubiks_0152", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3353,10 +3353,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.47117, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3374,7 +3374,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.01386, + "light_intensity": 1525.84688, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3389,7 +3389,7 @@ } }, { - "name": "shinymatte_mustard_0154", + "name": "shinymatte_rubiks_0154", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3397,10 +3397,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 59.18919, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3418,7 +3418,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 14.59006, + "light_intensity": 22.18205, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3433,7 +3433,7 @@ } }, { - "name": "shinymatte_mustard_0156", + "name": "shinymatte_rubiks_0156", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3441,10 +3441,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 14.94547, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3462,7 +3462,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 41.0209, + "light_intensity": 700.9511, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3477,7 +3477,7 @@ } }, { - "name": "shinymatte_mustard_0158", + "name": "shinymatte_rubiks_0158", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3485,10 +3485,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 293.36776, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3506,7 +3506,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.04013, + "light_intensity": 0.873, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3521,7 +3521,7 @@ } }, { - "name": "shinymatte_mustard_0160", + "name": "shinymatte_rubiks_0160", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3529,10 +3529,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 61.08055, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3550,7 +3550,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.06307, + "light_intensity": 6.96214, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3565,7 +3565,7 @@ } }, { - "name": "shinymatte_mustard_0162", + "name": "shinymatte_rubiks_0162", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3573,10 +3573,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 2.07008, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3594,7 +3594,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 2.44636, + "light_intensity": 10.67814, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3609,7 +3609,7 @@ } }, { - "name": "shinymatte_mustard_0164", + "name": "shinymatte_rubiks_0164", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3617,10 +3617,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 435.58985, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3638,7 +3638,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 3210.16004, + "light_intensity": 0.03965, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3653,7 +3653,7 @@ } }, { - "name": "shinymatte_mustard_0166", + "name": "shinymatte_rubiks_0166", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3661,10 +3661,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.08945, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3682,7 +3682,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 24.93611, + "light_intensity": 1.86784, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3697,7 +3697,7 @@ } }, { - "name": "shinymatte_mustard_0168", + "name": "shinymatte_rubiks_0168", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3705,10 +3705,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.01347, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3726,7 +3726,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 2076.21036, + "light_intensity": 1689.97344, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3741,7 +3741,7 @@ } }, { - "name": "shinymatte_mustard_0170", + "name": "shinymatte_rubiks_0170", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3749,10 +3749,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.0326, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3770,7 +3770,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 383.73263, + "light_intensity": 39.36353, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3785,7 +3785,7 @@ } }, { - "name": "shinymatte_mustard_0172", + "name": "shinymatte_rubiks_0172", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3793,10 +3793,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 130.82908, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3814,7 +3814,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 4.60886, + "light_intensity": 0.10271, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3829,7 +3829,7 @@ } }, { - "name": "shinymatte_mustard_0174", + "name": "shinymatte_rubiks_0174", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3837,10 +3837,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 4.2877, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3858,7 +3858,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 296.71763, + "light_intensity": 0.85335, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3873,7 +3873,7 @@ } }, { - "name": "shinymatte_mustard_0176", + "name": "shinymatte_rubiks_0176", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3881,10 +3881,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.083, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3902,7 +3902,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.01264, + "light_intensity": 0.12358, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3917,7 +3917,7 @@ } }, { - "name": "shinymatte_mustard_0178", + "name": "shinymatte_rubiks_0178", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3925,10 +3925,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 7.16868, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3946,7 +3946,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.04188, + "light_intensity": 0.01385, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -3961,7 +3961,7 @@ } }, { - "name": "shinymatte_mustard_0180", + "name": "shinymatte_rubiks_0180", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -3969,10 +3969,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.07379, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -3990,7 +3990,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 533.19835, + "light_intensity": 1930.05344, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4005,7 +4005,7 @@ } }, { - "name": "shinymatte_mustard_0182", + "name": "shinymatte_rubiks_0182", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4013,10 +4013,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 92.96274, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4034,7 +4034,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 347.56552, + "light_intensity": 3.5836, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4049,7 +4049,7 @@ } }, { - "name": "shinymatte_mustard_0184", + "name": "shinymatte_rubiks_0184", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4057,10 +4057,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 3.48844, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4078,7 +4078,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.21174, + "light_intensity": 0.56576, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4093,7 +4093,7 @@ } }, { - "name": "shinymatte_mustard_0186", + "name": "shinymatte_rubiks_0186", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4101,10 +4101,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 1.48398, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4122,7 +4122,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 10.58863, + "light_intensity": 25.74563, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4137,7 +4137,7 @@ } }, { - "name": "shinymatte_mustard_0188", + "name": "shinymatte_rubiks_0188", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4145,10 +4145,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.52277, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4166,7 +4166,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 28.42274, + "light_intensity": 0.01101, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4181,7 +4181,7 @@ } }, { - "name": "shinymatte_mustard_0190", + "name": "shinymatte_rubiks_0190", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4189,10 +4189,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 39.08063, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4210,7 +4210,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 881.49178, + "light_intensity": 0.3841, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4225,7 +4225,7 @@ } }, { - "name": "shinymatte_mustard_0192", + "name": "shinymatte_rubiks_0192", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4233,10 +4233,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 1.15334, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4254,7 +4254,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 27.35833, + "light_intensity": 101.52351, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4269,7 +4269,7 @@ } }, { - "name": "shinymatte_mustard_0194", + "name": "shinymatte_rubiks_0194", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4277,10 +4277,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.03159, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4298,7 +4298,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 2.24509, + "light_intensity": 40.91051, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4313,7 +4313,7 @@ } }, { - "name": "shinymatte_mustard_0196", + "name": "shinymatte_rubiks_0196", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4321,10 +4321,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.04704, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4342,7 +4342,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 1.35664, + "light_intensity": 3938.06287, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4357,7 +4357,7 @@ } }, { - "name": "shinymatte_mustard_0198", + "name": "shinymatte_rubiks_0198", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4365,10 +4365,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 3032.67546, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4386,7 +4386,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 2.67356, + "light_intensity": 34.30836, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4401,7 +4401,7 @@ } }, { - "name": "shinymatte_mustard_0200", + "name": "shinymatte_rubiks_0200", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4409,10 +4409,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 1506.51723, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4430,7 +4430,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 51.92008, + "light_intensity": 5.26371, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4445,7 +4445,7 @@ } }, { - "name": "shinymatte_mustard_0202", + "name": "shinymatte_rubiks_0202", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4453,10 +4453,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 97.1874, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4474,7 +4474,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 878.62406, + "light_intensity": 218.46473, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4489,7 +4489,7 @@ } }, { - "name": "shinymatte_mustard_0204", + "name": "shinymatte_rubiks_0204", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4497,10 +4497,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.32748, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4518,7 +4518,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 3.8614, + "light_intensity": 1406.172, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4533,7 +4533,7 @@ } }, { - "name": "shinymatte_mustard_0206", + "name": "shinymatte_rubiks_0206", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4541,10 +4541,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 3336.62273, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4562,7 +4562,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.25848, + "light_intensity": 128.00431, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4577,7 +4577,7 @@ } }, { - "name": "shinymatte_mustard_0208", + "name": "shinymatte_rubiks_0208", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4585,10 +4585,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 274.20669, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4606,7 +4606,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.22322, + "light_intensity": 3085.40281, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4621,7 +4621,7 @@ } }, { - "name": "shinymatte_mustard_0210", + "name": "shinymatte_rubiks_0210", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4629,10 +4629,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 121.7687, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4650,7 +4650,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 178.44854, + "light_intensity": 286.16994, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4665,7 +4665,7 @@ } }, { - "name": "shinymatte_mustard_0212", + "name": "shinymatte_rubiks_0212", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4673,10 +4673,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 3.63829, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4694,7 +4694,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 450.4026, + "light_intensity": 870.71374, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4709,7 +4709,7 @@ } }, { - "name": "shinymatte_mustard_0214", + "name": "shinymatte_rubiks_0214", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4717,10 +4717,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.35604, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4738,7 +4738,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.03981, + "light_intensity": 0.0447, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4753,7 +4753,7 @@ } }, { - "name": "shinymatte_mustard_0216", + "name": "shinymatte_rubiks_0216", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4761,10 +4761,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.03543, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4782,7 +4782,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.02395, + "light_intensity": 149.28033, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4797,7 +4797,7 @@ } }, { - "name": "shinymatte_mustard_0218", + "name": "shinymatte_rubiks_0218", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4805,10 +4805,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 1392.84883, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4826,7 +4826,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 24.41516, + "light_intensity": 3.22146, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4841,7 +4841,7 @@ } }, { - "name": "shinymatte_mustard_0220", + "name": "shinymatte_rubiks_0220", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4849,10 +4849,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 3.95781, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4870,7 +4870,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.06808, + "light_intensity": 14.19456, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4885,7 +4885,7 @@ } }, { - "name": "shinymatte_mustard_0222", + "name": "shinymatte_rubiks_0222", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4893,10 +4893,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.14232, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4914,7 +4914,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 500.88322, + "light_intensity": 53.42075, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4929,7 +4929,7 @@ } }, { - "name": "shinymatte_mustard_0224", + "name": "shinymatte_rubiks_0224", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4937,10 +4937,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.55416, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -4958,7 +4958,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.58693, + "light_intensity": 3364.70706, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -4973,7 +4973,7 @@ } }, { - "name": "shinymatte_mustard_0226", + "name": "shinymatte_rubiks_0226", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -4981,10 +4981,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 19.99654, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5002,7 +5002,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.06606, + "light_intensity": 4083.95505, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5017,7 +5017,7 @@ } }, { - "name": "shinymatte_mustard_0228", + "name": "shinymatte_rubiks_0228", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5025,10 +5025,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.10172, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5046,7 +5046,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 1772.48716, + "light_intensity": 0.43914, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5061,7 +5061,7 @@ } }, { - "name": "shinymatte_mustard_0230", + "name": "shinymatte_rubiks_0230", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5069,10 +5069,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 761.76166, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5090,7 +5090,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.08777, + "light_intensity": 151.92847, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5105,7 +5105,7 @@ } }, { - "name": "shinymatte_mustard_0232", + "name": "shinymatte_rubiks_0232", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5113,10 +5113,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 210.27132, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5134,7 +5134,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.41938, + "light_intensity": 187.98954, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5149,7 +5149,7 @@ } }, { - "name": "shinymatte_mustard_0234", + "name": "shinymatte_rubiks_0234", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5157,10 +5157,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 125.94989, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5178,7 +5178,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.07507, + "light_intensity": 0.94331, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5193,7 +5193,7 @@ } }, { - "name": "shinymatte_mustard_0236", + "name": "shinymatte_rubiks_0236", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5201,10 +5201,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 2.90058, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5222,7 +5222,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.04552, + "light_intensity": 0.05081, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5237,7 +5237,7 @@ } }, { - "name": "shinymatte_mustard_0238", + "name": "shinymatte_rubiks_0238", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5245,10 +5245,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 37.58498, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5266,7 +5266,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.0132, + "light_intensity": 0.01711, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5281,7 +5281,7 @@ } }, { - "name": "shinymatte_mustard_0240", + "name": "shinymatte_rubiks_0240", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5289,10 +5289,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 21.31851, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5310,7 +5310,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.02069, + "light_intensity": 269.18767, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5325,7 +5325,7 @@ } }, { - "name": "shinymatte_mustard_0242", + "name": "shinymatte_rubiks_0242", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5333,10 +5333,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 50.51918, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5354,7 +5354,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.09892, + "light_intensity": 6.17709, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5369,7 +5369,7 @@ } }, { - "name": "shinymatte_mustard_0244", + "name": "shinymatte_rubiks_0244", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5377,10 +5377,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.03029, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5398,7 +5398,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.02015, + "light_intensity": 4135.84128, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5413,7 +5413,7 @@ } }, { - "name": "shinymatte_mustard_0246", + "name": "shinymatte_rubiks_0246", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5421,10 +5421,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 2.34247, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5442,7 +5442,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 23.38357, + "light_intensity": 4.4655, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5457,7 +5457,7 @@ } }, { - "name": "shinymatte_mustard_0248", + "name": "shinymatte_rubiks_0248", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5465,10 +5465,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.01726, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5486,7 +5486,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 75.74809, + "light_intensity": 3742.13569, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5501,7 +5501,7 @@ } }, { - "name": "shinymatte_mustard_0250", + "name": "shinymatte_rubiks_0250", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5509,10 +5509,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 6.5349, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5530,7 +5530,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 1.75101, + "light_intensity": 2.21595, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5545,7 +5545,7 @@ } }, { - "name": "shinymatte_mustard_0252", + "name": "shinymatte_rubiks_0252", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5553,10 +5553,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.75835, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5574,7 +5574,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.64897, + "light_intensity": 333.55755, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5589,7 +5589,7 @@ } }, { - "name": "shinymatte_mustard_0254", + "name": "shinymatte_rubiks_0254", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5597,10 +5597,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.06663, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5618,7 +5618,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 7.50378, + "light_intensity": 0.03044, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5633,7 +5633,7 @@ } }, { - "name": "shinymatte_mustard_0256", + "name": "shinymatte_rubiks_0256", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5641,10 +5641,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.03884, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5662,7 +5662,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 969.67661, + "light_intensity": 14.64063, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5677,7 +5677,7 @@ } }, { - "name": "shinymatte_mustard_0258", + "name": "shinymatte_rubiks_0258", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5685,10 +5685,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 22.33412, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5706,7 +5706,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 708.88145, + "light_intensity": 372.31854, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5721,7 +5721,7 @@ } }, { - "name": "shinymatte_mustard_0260", + "name": "shinymatte_rubiks_0260", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5729,10 +5729,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.0938, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5750,7 +5750,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.01769, + "light_intensity": 1861.43152, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5765,7 +5765,7 @@ } }, { - "name": "shinymatte_mustard_0262", + "name": "shinymatte_rubiks_0262", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5773,10 +5773,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 1871.68089, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5794,7 +5794,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.10823, + "light_intensity": 487.38971, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5809,7 +5809,7 @@ } }, { - "name": "shinymatte_mustard_0264", + "name": "shinymatte_rubiks_0264", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5817,10 +5817,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 20.48566, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5838,7 +5838,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.22346, + "light_intensity": 0.01624, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5853,7 +5853,7 @@ } }, { - "name": "shinymatte_mustard_0266", + "name": "shinymatte_rubiks_0266", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5861,10 +5861,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.94798, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5882,7 +5882,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.26379, + "light_intensity": 1.33051, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5897,7 +5897,7 @@ } }, { - "name": "shinymatte_mustard_0268", + "name": "shinymatte_rubiks_0268", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5905,10 +5905,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 23.31362, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5926,7 +5926,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 18.00685, + "light_intensity": 0.01895, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5941,7 +5941,7 @@ } }, { - "name": "shinymatte_mustard_0270", + "name": "shinymatte_rubiks_0270", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5949,10 +5949,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.01349, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -5970,7 +5970,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 2.3565, + "light_intensity": 0.04196, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -5985,7 +5985,7 @@ } }, { - "name": "shinymatte_mustard_0272", + "name": "shinymatte_rubiks_0272", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -5993,10 +5993,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 2902.68697, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -6014,7 +6014,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.01909, + "light_intensity": 70.55804, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -6029,7 +6029,7 @@ } }, { - "name": "shinymatte_mustard_0274", + "name": "shinymatte_rubiks_0274", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -6037,10 +6037,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 5.60574, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -6058,7 +6058,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 1.34653, + "light_intensity": 116.1013, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -6073,7 +6073,7 @@ } }, { - "name": "shinymatte_mustard_0276", + "name": "shinymatte_rubiks_0276", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -6081,10 +6081,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 288.92464, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -6102,7 +6102,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 9.65722, + "light_intensity": 256.69119, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -6117,7 +6117,7 @@ } }, { - "name": "shinymatte_mustard_0278", + "name": "shinymatte_rubiks_0278", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -6125,10 +6125,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.02961, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -6146,7 +6146,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.03797, + "light_intensity": 855.48206, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -6161,7 +6161,7 @@ } }, { - "name": "shinymatte_mustard_0280", + "name": "shinymatte_rubiks_0280", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -6169,10 +6169,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 5.93542, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -6190,7 +6190,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 562.15399, + "light_intensity": 163.68081, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -6205,7 +6205,7 @@ } }, { - "name": "shinymatte_mustard_0282", + "name": "shinymatte_rubiks_0282", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -6213,10 +6213,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 6.25928, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -6234,7 +6234,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.01978, + "light_intensity": 366.5584, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -6249,7 +6249,7 @@ } }, { - "name": "shinymatte_mustard_0284", + "name": "shinymatte_rubiks_0284", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -6257,10 +6257,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 2211.28559, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -6278,7 +6278,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 1864.8593, + "light_intensity": 0.01901, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -6293,7 +6293,7 @@ } }, { - "name": "shinymatte_mustard_0286", + "name": "shinymatte_rubiks_0286", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -6301,10 +6301,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 18.12429, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -6322,7 +6322,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 0.03671, + "light_intensity": 0.21707, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -6337,7 +6337,7 @@ } }, { - "name": "shinymatte_mustard_0288", + "name": "shinymatte_rubiks_0288", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -6345,10 +6345,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 4.99346, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -6366,7 +6366,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 641.96056, + "light_intensity": 35.00876, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -6381,7 +6381,7 @@ } }, { - "name": "shinymatte_mustard_0290", + "name": "shinymatte_rubiks_0290", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -6389,10 +6389,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.33227, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -6410,7 +6410,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 1393.77667, + "light_intensity": 777.01673, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -6425,7 +6425,7 @@ } }, { - "name": "shinymatte_mustard_0292", + "name": "shinymatte_rubiks_0292", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -6433,10 +6433,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.77554, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -6454,7 +6454,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 3824.22931, + "light_intensity": 0.01061, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -6469,7 +6469,7 @@ } }, { - "name": "shinymatte_mustard_0294", + "name": "shinymatte_rubiks_0294", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -6477,10 +6477,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 9.27463, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -6498,7 +6498,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 372.15292, + "light_intensity": 8.56756, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -6513,7 +6513,7 @@ } }, { - "name": "shinymatte_mustard_0296", + "name": "shinymatte_rubiks_0296", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -6521,10 +6521,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 3.17207, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -6542,7 +6542,7 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 276.83381, + "light_intensity": 72.41701, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, @@ -6557,7 +6557,7 @@ } }, { - "name": "shinymatte_mustard_0298", + "name": "shinymatte_rubiks_0298", "arena_env_args": { "enable_cameras": true, "num_envs": 2, @@ -6565,10 +6565,10 @@ "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", "light_intensity": 0.01328, - "pick_up_object": "mustard_ycb_robolab" + "pick_up_object": "rubiks_cube_hot3d_robolab" }, "num_episodes": 2, - "language_instruction": "Pick up the mustard bottle and place it in the bowl.", + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", "policy_config_dict": { "policy_variant": "pi05", @@ -6586,7 +6586,15407 @@ "environment": "pick_and_place_maple_table", "embodiment": "droid_abs_joint_pos", "hdr": "billiard_hall_robolab", - "light_intensity": 45.86622, + "light_intensity": 0.01475, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0300", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 511.69702, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0301", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.93782, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0302", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1279.95104, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0303", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1271.1486, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0304", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06299, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0305", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 67.22042, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0306", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 14.36929, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0307", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.22616, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0308", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04157, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0309", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 724.39272, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0310", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 67.77603, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0311", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.96254, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0312", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.40063, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0313", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 729.76946, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0314", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 57.28354, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0315", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.50544, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0316", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 139.03383, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0317", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 23.13222, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0318", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 240.15907, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0319", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.82873, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0320", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04112, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0321", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.36832, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0322", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1660.82559, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0323", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1128.40488, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0324", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.20511, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0325", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.11724, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0326", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01634, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0327", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03043, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0328", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 14.52404, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0329", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.88845, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0330", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.2998, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0331", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 122.97136, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0332", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 535.7311, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0333", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 399.51078, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0334", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 403.83215, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0335", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4918.2263, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0336", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.64175, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0337", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.48861, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0338", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2694.91499, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0339", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.11275, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + 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"pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06022, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0342", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 8.61579, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0343", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 18.88765, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0344", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.28757, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0345", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4843.71642, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0346", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2160.15159, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0347", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 98.69607, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0348", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.08672, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0349", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 24.66608, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0350", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01803, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0351", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.72226, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0352", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.01721, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0353", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1645.35714, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0354", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4523.95809, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0355", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 6.79067, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0356", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1206.8186, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0357", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.05831, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0358", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 184.62676, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0359", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.2086, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0360", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1192.88804, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0361", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.02414, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0362", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1235.16677, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0363", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.1416, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0364", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 9.05645, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0365", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01261, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0366", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.63164, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0367", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.83026, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0368", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 251.00114, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0369", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 41.32624, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0370", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 58.99126, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0371", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.90451, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0372", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.3473, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0373", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.48739, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0374", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03454, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0375", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2927.55373, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0376", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 180.27338, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0377", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 192.93689, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0378", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.31315, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0379", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 12.0872, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0380", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2182.07686, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0381", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.4184, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0382", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.2362, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0383", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1294.07024, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0384", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.05007, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0385", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.21868, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0386", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 545.11256, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0387", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.71469, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0388", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.07474, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0389", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1516.11837, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0390", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.10511, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0391", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 10.41944, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0392", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 26.05341, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0393", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 169.99953, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0394", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 964.05728, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0395", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 23.26145, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0396", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.13167, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0397", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 52.95786, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0398", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.58685, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0399", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.50837, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0400", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 269.40575, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0401", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.23745, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0402", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3454.69452, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0403", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.68845, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0404", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 7.14018, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0405", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.07689, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0406", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06608, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0407", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 960.92909, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0408", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01201, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0409", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.41135, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0410", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.20361, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0411", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 15.84569, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0412", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.0564, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0413", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 326.16585, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0414", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 72.77068, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0415", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 293.08264, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0416", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04947, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0417", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.15029, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0418", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 7.6835, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0419", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 5.17817, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0420", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 90.48588, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0421", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4664.18138, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0422", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 20.50058, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0423", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 69.90541, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0424", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.13757, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0425", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 439.13702, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0426", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 382.54414, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0427", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1391.96398, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0428", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 119.42184, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0429", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 307.92918, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0430", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 162.72252, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0431", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.11359, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0432", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.05583, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0433", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 15.988, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0434", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.05073, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0435", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03808, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0436", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1932.64085, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0437", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 52.59974, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0438", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.84411, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0439", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2782.96494, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0440", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.51892, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0441", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 8.35689, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0442", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 6.08732, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0443", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.93425, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0444", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 59.92986, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0445", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01601, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0446", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2792.9829, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0447", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2949.34932, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0448", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.42899, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0449", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03864, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0450", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1864.04116, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0451", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01714, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0452", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01386, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0453", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.25254, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0454", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 14.59006, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0455", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.02363, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0456", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 41.0209, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0457", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.92376, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0458", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04013, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0459", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 8.73311, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0460", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06307, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0461", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.6044, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0462", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.44636, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0463", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01952, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0464", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3210.16004, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0465", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04325, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0466", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 24.93611, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0467", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.55338, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0468", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2076.21036, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0469", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.02213, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0470", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 383.73263, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0471", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 95.27135, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0472", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4.60886, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0473", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.15129, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0474", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 296.71763, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0475", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.52662, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0476", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01264, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0477", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.76243, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0478", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04188, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0479", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.36726, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0480", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 533.19835, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0481", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01022, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0482", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 347.56552, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0483", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04352, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0484", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.21174, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0485", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 825.78952, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0486", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 10.58863, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0487", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01016, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0488", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 28.42274, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0489", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 7.87101, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0490", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 881.49178, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0491", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 6.16109, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0492", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 27.35833, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0493", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.79121, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0494", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.24509, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0495", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.87157, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0496", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.35664, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0497", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 280.87203, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0498", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.67356, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0499", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 622.29268, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0500", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 51.92008, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0501", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.30459, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + 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"pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.68844, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0504", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.8614, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0505", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.24094, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0506", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.25848, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0507", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 5.42909, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0508", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.22322, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0509", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 78.31932, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0510", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 178.44854, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0511", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.1999, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0512", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 450.4026, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0513", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.7671, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0514", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03981, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0515", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2005.55247, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0516", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.02395, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0517", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01891, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0518", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 24.41516, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0519", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4.22583, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0520", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06808, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": 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"embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 500.88322, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0523", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.07202, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0524", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.58693, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0525", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01862, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0526", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06606, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0527", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06132, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0528", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1772.48716, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0529", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1723.24021, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 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"openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0531", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01129, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0532", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.41938, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0533", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.11837, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0534", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.07507, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0535", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01508, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0536", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04552, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0537", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.0427, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0538", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.0132, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0539", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 34.21466, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.02015, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0545", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 23.09435, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0546", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 23.38357, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0547", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 693.27651, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0548", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 75.74809, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0549", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01064, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0550", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.75101, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0551", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 730.00688, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0552", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.64897, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0553", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 33.75523, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0554", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + 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"policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0556", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 969.67661, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0557", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1722.57297, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0564", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.22346, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0565", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.88991, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0566", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.26379, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0567", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 27.47248, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0568", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 18.00685, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0569", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4087.78605, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0570", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.3565, + "pick_up_object": "rubiks_cube_hot3d_robolab" 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8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0572", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01909, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0573", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 430.69588, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0574", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.34653, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0575", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.65245, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0576", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": 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"policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0578", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03797, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0579", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 26.51945, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0580", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 562.15399, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01978, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0583", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.28835, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0584", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1864.8593, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0585", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.64595, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0586", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03671, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0587", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.02787, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0588", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 641.96056, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0589", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01479, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0590", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1393.77667, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0591", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.94313, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0592", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3824.22931, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0593", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01284, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0594", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 372.15292, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0595", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.08772, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0596", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 276.83381, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0597", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 135.76818, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0598", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 45.86622, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0599", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 108.49025, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0600", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 275.0913, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0601", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 161.86296, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0602", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.05845, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0603", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.64187, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0604", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 11.35104, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0605", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1180.84852, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0606", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 8.52198, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0607", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 24.2227, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0608", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 771.39697, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0609", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.05227, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0610", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4.3399, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0611", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06592, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0612", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.56536, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0613", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 89.11074, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0614", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 44.14199, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0615", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.09674, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + 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"pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 7.78119, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0618", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06259, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0619", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4488.41484, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0620", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 5.28942, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0621", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01054, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0622", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.37596, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0623", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01243, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0624", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.21155, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0625", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4566.24536, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0626", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.24291, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0627", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 21.47035, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0628", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.22478, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0629", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.05288, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0630", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.73517, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0631", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1299.26674, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0632", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.45397, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0633", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1040.54513, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0634", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 80.91532, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0635", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 11.37078, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0636", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.49192, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0637", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 34.96022, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0638", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2555.71759, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0639", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.36048, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0640", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1668.16835, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0641", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.0194, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0642", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 5.5042, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0643", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 24.45811, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0644", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.74357, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0645", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.47841, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0646", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 11.25711, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0647", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 60.52802, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0648", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 685.36319, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0649", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 583.09079, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0650", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 52.36917, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0651", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01269, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0652", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 383.88832, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0653", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 24.82875, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { 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"embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.208, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0656", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 40.45559, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.43872, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0659", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.27901, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0660", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 154.21765, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0661", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 30.25246, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0662", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.1424, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0663", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 14.2762, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0664", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 91.12419, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0665", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.81026, + "pick_up_object": 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"127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0667", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 72.73449, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0668", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + 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"policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0670", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 31.71989, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0671", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + 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"openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0683", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 343.98766, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0684", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 366.61894, + 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"shinymatte_rubiks_0696", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 23.05817, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0697", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.071, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0698", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03481, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0699", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3018.85669, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0700", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 32.47206, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0701", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.10398, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0702", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.09466, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0703", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.13715, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0704", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 16.58199, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0705", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 785.81001, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0706", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 18.29213, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0707", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1587.74841, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0708", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4.52518, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0709", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.16182, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0710", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 9.51618, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0711", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4.75753, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0712", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 225.72335, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0713", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 151.4138, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0714", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 358.81589, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0715", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1013.39783, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0716", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 6.3792, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0717", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.44614, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0718", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 26.12553, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0719", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 8.84053, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0720", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2028.0907, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0721", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 168.6629, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0722", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04812, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0723", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 146.79863, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0724", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04649, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0725", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 289.77859, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0726", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03161, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0727", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 17.70255, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0728", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 56.12329, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0729", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03946, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0730", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.43017, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0731", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1418.13204, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0732", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 258.72269, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0733", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 856.78516, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0734", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 66.88484, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0735", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 353.45436, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0736", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.79688, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0737", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03708, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0738", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1317.5406, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0739", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.14617, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0740", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 221.63984, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0741", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 172.88455, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0742", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.34815, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0743", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01355, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0744", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.18992, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0745", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3806.90303, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0746", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.61942, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0747", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.41038, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0748", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.07911, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0749", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 125.70153, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0750", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06954, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0751", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1143.51275, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, 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"openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0753", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.77412, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0754", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.13042, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0755", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.65876, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0756", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.52942, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0757", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 29.4576, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0758", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 144.03133, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0759", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 20.46638, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0760", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 14.17395, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0761", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.14623, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + 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"pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 26.88415, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0764", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 279.84323, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 5.39402, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0767", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4.62453, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0768", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.39592, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0769", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.1322, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0770", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4195.49859, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0771", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.41152, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0772", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 123.16691, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0773", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.76199, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0774", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2635.29863, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0775", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.05587, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0776", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + 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"policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0778", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 703.34099, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0779", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.24448, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0786", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 81.29103, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and 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"shinymatte_rubiks_0788", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01175, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0789", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 196.87923, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, 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"openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0791", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1049.53767, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0792", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 505.73971, + 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"remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0794", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.48222, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0795", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": 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"policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0797", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.11447, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0798", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.31692, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0799", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 9.43914, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4.67189, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0802", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1714.63946, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and 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"shinymatte_rubiks_0804", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 283.51189, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0805", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01809, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0806", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04268, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0807", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 5.54632, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0808", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4809.1753, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0809", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2932.28564, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0810", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1024.55332, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0811", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 52.32236, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0812", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.41494, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0813", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 6.66618, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0814", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 588.09638, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0815", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04274, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0816", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04041, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0817", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.27455, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0818", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4941.60343, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0819", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.47853, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0820", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 62.18954, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0821", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 228.34553, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0822", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 50.70409, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0823", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 991.62744, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0824", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03277, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0825", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1375.40272, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0826", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1294.69191, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0827", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4083.4474, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0828", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01463, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0829", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3967.1467, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0830", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.23576, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0831", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2698.44439, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0832", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06532, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0833", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.02566, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0834", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 267.16354, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0835", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06099, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0836", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.13476, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0837", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.54343, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0838", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1547.74689, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0839", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 14.15633, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0840", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 54.96137, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0841", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.0357, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0842", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01607, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0843", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 660.95439, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0844", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01074, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0845", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 32.72465, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0846", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.0197, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0847", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 12.32873, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.08752, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0850", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 369.50541, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0851", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.27752, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0852", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.22883, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0853", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.08196, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0854", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 693.03556, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0855", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 716.3768, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0856", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.02119, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0857", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 21.35488, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0858", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 367.00246, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0859", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + 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"policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0880", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 708.87655, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0881", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + 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"shinymatte_rubiks_0890", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 111.10295, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0891", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.24988, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, 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"openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0893", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 111.60049, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0894", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 30.56108, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0895", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 710.77252, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0896", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.02045, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0897", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 964.223, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0898", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 32.53317, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0899", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.85859, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0900", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01743, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0901", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 10.59987, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.26041, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0904", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 110.62808, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and 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"shinymatte_rubiks_0906", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.09697, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0907", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.08298, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0908", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.03332, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0909", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2275.83172, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0910", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.11116, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0911", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1149.7984, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0912", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3847.20652, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0913", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 269.24273, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0914", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4.1051, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0915", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 8.91533, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0916", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + 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"pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 10.44731, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0920", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 18.28796, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.06716, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0923", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.99614, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and 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"shinymatte_alphabet_0925", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.05635, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0926", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.52161, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0927", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.05202, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0928", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 19.68352, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0929", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2672.03761, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0930", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 97.2752, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0931", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 5.58572, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0932", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": 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"embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.08555, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0936", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.07014, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": 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"enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 7.89018, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0939", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.15268, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0940", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.00715, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0941", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.27726, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0942", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 5.03818, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0943", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01483, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0944", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.0478, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0945", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.04763, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0946", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.0581, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0947", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1679.15804, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0948", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.38437, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0949", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.67984, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0950", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.54513, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0951", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 29.22201, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0952", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.74541, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0953", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4.46788, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0954", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 30.33889, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0955", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.91496, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0956", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 41.37446, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0957", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 10.74491, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0958", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.22279, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0959", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.1167, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0960", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.1362, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0961", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4305.50367, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0962", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.17388, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0963", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 460.7995, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0964", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 22.52888, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0965", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 168.43779, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0966", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.64092, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0967", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4.69294, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0968", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01605, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0969", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.07434, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0970", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.42354, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0971", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1765.41621, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0972", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 5.03579, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0973", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.88217, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0974", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.19304, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0975", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.0193, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0976", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 18.29871, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0977", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.89402, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0978", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 16.76166, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0979", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 336.96655, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0980", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 100.15886, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0981", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 35.38331, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0982", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 49.2764, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0983", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 190.11071, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0984", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 52.26325, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0985", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 333.35291, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0986", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.63401, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0987", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.16141, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0988", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 307.29296, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0989", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1839.13415, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0990", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 13.47589, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0991", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 3.13393, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0992", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 2.87501, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0993", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 43.82477, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0994", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 36.95101, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0995", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.01031, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0996", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 1.13599, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0997", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 4583.28935, + "pick_up_object": "alphabet_soup_can_hope_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the soup can and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_rubiks_0998", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 8.35768, + "pick_up_object": "rubiks_cube_hot3d_robolab" + }, + "num_episodes": 2, + "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", + "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", + "policy_config_dict": { + "policy_variant": "pi05", + "policy_device": "cuda:0", + "remote_host": "127.0.0.1", + "remote_port": 8000, + "openpi_embodiment_adapter": "droid" + } + }, + { + "name": "shinymatte_alphabet_0999", + "arena_env_args": { + "enable_cameras": true, + "num_envs": 2, + "environment": "pick_and_place_maple_table", + "embodiment": "droid_abs_joint_pos", + "hdr": "billiard_hall_robolab", + "light_intensity": 0.39886, "pick_up_object": "alphabet_soup_can_hope_robolab" }, "num_episodes": 2, From 6224293eb530d4e3e9e432e5a1d2ceb9fea4d194 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 4 Jun 2026 15:40:09 +0200 Subject: [PATCH 06/74] Replace HTML report with a single-PDF sensitivity report MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Simplify the deliverable to one PDF on disk showing the most important plots (robolab-style): an outcome x factor grid of marginal-posterior plots, fit one analyzer per outcome. Drop the involved Plotly HTML report (report.py + its CLI) — to be reintroduced in a follow-up PR. - plotting.py: split the renderers into draw_marginal(ax, ...) that draws onto a caller-supplied Axes; plot_marginal keeps its single-figure save behavior. - pdf_report.py: new generate_pdf_report() lays out the grid and saves one PDF. - generate_sensitivity_report.py: now drives the PDF (--output_pdf). Signed-off-by: Clemens Volk --- .../analysis/sensitivity/pdf_report.py | 91 +++ .../analysis/sensitivity/plotting.py | 110 +-- isaaclab_arena/analysis/sensitivity/report.py | 736 ------------------ .../scripts/generate_sensitivity_report.py | 32 +- 4 files changed, 168 insertions(+), 801 deletions(-) create mode 100644 isaaclab_arena/analysis/sensitivity/pdf_report.py delete mode 100644 isaaclab_arena/analysis/sensitivity/report.py diff --git a/isaaclab_arena/analysis/sensitivity/pdf_report.py b/isaaclab_arena/analysis/sensitivity/pdf_report.py new file mode 100644 index 0000000000..74c81059c7 --- /dev/null +++ b/isaaclab_arena/analysis/sensitivity/pdf_report.py @@ -0,0 +1,91 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Single-PDF sensitivity report — the most important plots, one file on disk. + +``generate_pdf_report`` reads a (factors.yaml, JSONL) pair, fits one analyzer per declared +outcome, and lays out an outcome × factor grid of marginal-posterior plots in a single PDF. +Each row is one outcome (conditioned on a sensible default value); each column is one factor. + +This is the minimal, robolab-style deliverable. The richer interactive HTML report is +intentionally out of scope here — see the follow-up PR. +""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np + +from isaaclab_arena.analysis.sensitivity.analyzer import make_analyzer +from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset +from isaaclab_arena.analysis.sensitivity.plotting import draw_marginal + + +def generate_pdf_report( + factors_yaml_path: str | Path, + jsonl_path: str | Path, + output_pdf_path: str | Path, +) -> Path: + """Build a single-PDF sensitivity report covering every (outcome, factor) pair. + + Args: + factors_yaml_path: Schema file declaring factors and outcomes. + jsonl_path: episode_summary.jsonl from eval_runner. + output_pdf_path: Destination ``.pdf`` file (parent dirs created if absent). + + Returns: + The resolved output path. + """ + import matplotlib + + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + dataset = SensitivityDataset(Path(factors_yaml_path), Path(jsonl_path)) + outcomes = dataset.schema.outcomes + factors = dataset.schema.factors + n_rows, n_cols = len(outcomes), len(factors) + print(f"[INFO] PDF report: {n_rows} outcomes × {n_cols} factors ({len(dataset.rows)} episodes)") + + figure, axes = plt.subplots(n_rows, n_cols, figsize=(6.5 * n_cols, 4.5 * n_rows), squeeze=False) + for row_index, outcome in enumerate(outcomes): + analyzer = make_analyzer(dataset, outcome.name) + print(f"[INFO] Fitting {type(analyzer).__name__} for outcome={outcome.name!r}") + analyzer.fit() + outcome_value = _default_outcome_value_for_analysis(dataset, outcome) + for col_index, factor in enumerate(factors): + ax = axes[row_index][col_index] + draw_marginal(ax, analyzer, factor.name, outcome_value=outcome_value) + ax.set_title(f"{outcome.name} vs {factor.name}\n(conditioned on {outcome.name}={outcome_value:g})", fontsize=10) + + slice_info = dataset.schema.slice + figure.suptitle( + f"Sensitivity report — {slice_info.policy} / {slice_info.task} / {slice_info.embodiment}" + f" ({len(dataset.rows)} episodes)", + fontsize=13, + fontweight="bold", + ) + figure.tight_layout(rect=[0, 0, 1, 0.97]) # leave room for suptitle + + output_pdf_path = Path(output_pdf_path) + output_pdf_path.parent.mkdir(parents=True, exist_ok=True) + figure.savefig(output_pdf_path) # .pdf extension → matplotlib's PDF backend + plt.close(figure) + print(f"[INFO] Wrote report → {output_pdf_path}") + return output_pdf_path + + +def _default_outcome_value_for_analysis(dataset: SensitivityDataset, outcome) -> float: + """Pick a sensible value to condition the posterior on for this outcome. + + Binary outcomes (only ``{0, 1}`` observed) → ``1.0`` (the "success" branch). + Continuous outcomes → empirical median; a "typical case" value always inside the data range. + """ + outcome_column_index = dataset.outcome_columns[outcome.name] + values = dataset.x[:, outcome_column_index].cpu().numpy() + if set(values.flatten().tolist()).issubset({0.0, 1.0}): + return 1.0 + return float(np.median(values)) diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index 9fc3274da3..5c13419289 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -10,8 +10,12 @@ figures. Decoupled from the analyzer hierarchy so new plot types can be added without touching inference code, and so existing plot code can be tested with mock posteriors. -The single entry point is ``plot_marginal(analyzer, factor_name, output_path, ...)``, -which dispatches by factor type to the right renderer. +Two entry points: + - ``draw_marginal(ax, analyzer, factor_name, ...)`` draws one factor's marginal onto a + caller-supplied Axes (used by the multi-plot PDF report). + - ``plot_marginal(analyzer, factor_name, output_path, ...)`` wraps it in its own figure + and saves a standalone image (used by the single-factor CLI). +Both dispatch by factor type to the right renderer. """ from __future__ import annotations @@ -25,15 +29,18 @@ from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec -def plot_marginal( +def draw_marginal( + ax, analyzer: BaseAnalyzer, factor_name: str, - output_path, outcome_value: float = 1.0, num_samples: int = 10_000, num_grid_points: int = 200, ) -> None: - """Render the marginal posterior for ``factor_name``, dispatching by factor type. + """Draw ``factor_name``'s marginal posterior onto ``ax``, dispatching by factor type. + + Sets axis labels, scale, legend and grid but NOT the title — the caller titles the + Axes (a standalone plot wants the full slice block; a grid cell wants a compact label). For continuous factors, the analyzer must expose ``continuous_marginal_density`` (only ``PosteriorAnalyzer`` does — ``EmpiricalAnalyzer`` rejects continuous factors at @@ -45,21 +52,48 @@ def plot_marginal( raise NotImplementedError( f"{type(analyzer).__name__} cannot plot continuous factors; expected a PosteriorAnalyzer (NPE/MNPE)." ) - _plot_continuous_marginal(analyzer, factor_spec, output_path, outcome_value, num_grid_points) + _draw_continuous_marginal(ax, analyzer, factor_spec, outcome_value, num_grid_points) elif factor_spec.type == "categorical": - _plot_categorical_marginal(analyzer, factor_spec, output_path, outcome_value, num_samples) + _draw_categorical_marginal(ax, analyzer, factor_spec, outcome_value, num_samples) else: raise NotImplementedError(f"Unsupported factor type {factor_spec.type!r}") -def _plot_continuous_marginal( +def plot_marginal( analyzer: BaseAnalyzer, - factor_spec: FactorSpec, + factor_name: str, output_path, + outcome_value: float = 1.0, + num_samples: int = 10_000, + num_grid_points: int = 200, +) -> None: + """Render one factor's marginal posterior into its own figure and save it. + + Thin wrapper over ``draw_marginal``: sizes a single-Axes figure, draws, titles it with + the full slice block, and saves to ``output_path``. + """ + import matplotlib.pyplot as plt + + factor_spec = analyzer._factor_spec(factor_name) + if factor_spec.type == "categorical" and factor_spec.choices is not None: + figsize = (max(8, 1.0 * len(factor_spec.choices)), 5) + else: + figsize = (8, 5) + figure, axes = plt.subplots(figsize=figsize) + draw_marginal(axes, analyzer, factor_name, outcome_value, num_samples, num_grid_points) + axes.set_title(_plot_title(analyzer, factor_name)) + figure.tight_layout() + _save_figure(figure, output_path) + + +def _draw_continuous_marginal( + ax, + analyzer: BaseAnalyzer, + factor_spec: FactorSpec, outcome_value: float, num_grid_points: int, ) -> None: - """Render a continuous factor's marginal posterior as a density curve. + """Draw a continuous factor's marginal posterior onto ``ax`` as a density curve. The blue curve shows ``P(factor_value | outcome=outcome_value)`` from the analyzer. Below the x-axis is an empirical "rug" — small vertical ticks at the actual recorded @@ -67,8 +101,6 @@ def _plot_continuous_marginal( and red for episodes where it was not. The rug lets a human eyeball whether the smooth posterior actually agrees with where the successful episodes lived. """ - import matplotlib.pyplot as plt - grid, density = analyzer.continuous_marginal_density(factor_spec.name, outcome_value, num_grid_points) factor_column_slice = analyzer.dataset.factor_columns[factor_spec.name] outcome_column_index = analyzer.dataset.outcome_columns[analyzer.outcome_name] @@ -79,15 +111,14 @@ def _plot_continuous_marginal( empirical_theta_values = np.power(10.0, empirical_theta_values) empirical_outcomes = analyzer.dataset.x[:, outcome_column_index].cpu().numpy() - figure, axes = plt.subplots(figsize=(8, 5)) - axes.plot( + ax.plot( grid, density, color="steelblue", linewidth=2, label=f"P({factor_spec.name} | {analyzer.outcome_name}={outcome_value:g})", ) - axes.fill_between(grid, 0, density, color="steelblue", alpha=0.2) + ax.fill_between(grid, 0, density, color="steelblue", alpha=0.2) # Rug coloring depends on outcome shape. For binary outcomes (only 0/1 observed) the # green/red ≥/<0.5 split gives a meaningful "successes vs failures" picture. For @@ -96,7 +127,7 @@ def _plot_continuous_marginal( is_binary_outcome = set(empirical_outcomes.flatten().tolist()).issubset({0.0, 1.0}) if is_binary_outcome: success_mask = empirical_outcomes >= 0.5 - axes.scatter( + ax.scatter( empirical_theta_values[success_mask], np.full(success_mask.sum(), -0.05 * density.max()), marker="|", @@ -104,7 +135,7 @@ def _plot_continuous_marginal( s=80, label=f"{analyzer.outcome_name} ≥ 0.5 (n={success_mask.sum()})", ) - axes.scatter( + ax.scatter( empirical_theta_values[~success_mask], np.full((~success_mask).sum(), -0.1 * density.max()), marker="|", @@ -113,7 +144,7 @@ def _plot_continuous_marginal( label=f"{analyzer.outcome_name} < 0.5 (n={(~success_mask).sum()})", ) else: - axes.scatter( + ax.scatter( empirical_theta_values, np.full(len(empirical_theta_values), -0.05 * density.max()), marker="|", @@ -121,25 +152,22 @@ def _plot_continuous_marginal( s=80, label=f"observed samples (n={len(empirical_theta_values)})", ) - axes.set_xlabel(factor_spec.name) - axes.set_ylabel("posterior density") - axes.set_title(_plot_title(analyzer, factor_spec.name)) + ax.set_xlabel(factor_spec.name) + ax.set_ylabel("posterior density") if factor_spec.distribution == "log_uniform": - axes.set_xscale("log") - axes.legend(loc="best", fontsize=9) - axes.grid(alpha=0.3) - figure.tight_layout() - _save_figure(figure, output_path) + ax.set_xscale("log") + ax.legend(loc="best", fontsize=9) + ax.grid(alpha=0.3) -def _plot_categorical_marginal( +def _draw_categorical_marginal( + ax, analyzer: BaseAnalyzer, factor_spec: FactorSpec, - output_path, outcome_value: float, num_samples: int, ) -> None: - """Render a categorical factor's marginal as side-by-side bars per category. + """Draw a categorical factor's marginal onto ``ax`` as side-by-side bars per category. The blue bar (left of each category) is the analyzer's ``P(category | outcome)``. The green bar (right of each category) is the *empirical* per-category outcome rate @@ -150,8 +178,6 @@ def _plot_categorical_marginal( Each green bar is annotated with the sample count ``n`` for that category, so the user can see how trustworthy each bar is. """ - import matplotlib.pyplot as plt - assert factor_spec.choices is not None choices = factor_spec.choices num_choices = len(choices) @@ -172,10 +198,9 @@ def _plot_categorical_marginal( if category_mask.any(): empirical_rates[code] = float((empirical_outcomes[category_mask] >= 0.5).mean()) - figure, axes = plt.subplots(figsize=(max(8, 1.0 * num_choices), 5)) bar_x_positions = np.arange(num_choices) bar_width = 0.4 - axes.bar( + ax.bar( bar_x_positions - bar_width / 2, posterior_probabilities, bar_width, @@ -183,7 +208,7 @@ def _plot_categorical_marginal( alpha=0.8, label=f"P(category | {analyzer.outcome_name}={outcome_value:g})", ) - axes.bar( + ax.bar( bar_x_positions + bar_width / 2, empirical_rates, bar_width, @@ -192,7 +217,7 @@ def _plot_categorical_marginal( label=f"empirical {analyzer.outcome_name} rate per category", ) for category_index, count in enumerate(empirical_counts): - axes.text( + ax.text( category_index + bar_width / 2, empirical_rates[category_index] + 0.02, f"n={count}", @@ -200,15 +225,12 @@ def _plot_categorical_marginal( fontsize=8, ) - axes.set_xticks(bar_x_positions) - axes.set_xticklabels(choices, rotation=30, ha="right") - axes.set_ylabel("probability") - axes.set_ylim(0, 1.05) - axes.set_title(_plot_title(analyzer, factor_spec.name)) - axes.legend(loc="best", fontsize=9) - axes.grid(alpha=0.3, axis="y") - figure.tight_layout() - _save_figure(figure, output_path) + ax.set_xticks(bar_x_positions) + ax.set_xticklabels(choices, rotation=30, ha="right") + ax.set_ylabel("probability") + ax.set_ylim(0, 1.05) + ax.legend(loc="best", fontsize=9) + ax.grid(alpha=0.3, axis="y") def _plot_title(analyzer: BaseAnalyzer, factor_name: str) -> str: diff --git a/isaaclab_arena/analysis/sensitivity/report.py b/isaaclab_arena/analysis/sensitivity/report.py deleted file mode 100644 index 7bb6a0dd10..0000000000 --- a/isaaclab_arena/analysis/sensitivity/report.py +++ /dev/null @@ -1,736 +0,0 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -"""Interactive HTML sensitivity report generator. - -Single function entry point: :func:`generate_report` reads a (factors.yaml, JSONL) pair, -runs the analyzer pipeline for every declared (outcome, factor) combination, and emits a -self-contained HTML file with interactive Plotly plots embedded inline. Bootstrap CSS via -CDN provides the visual chrome (tabs, cards, accordion). The deliverable is a single .html -file that opens in any modern browser — no server, no Python at view time. - -Why Plotly + Jinja2 + Bootstrap (vs the earlier static-PNG version): - - - Plotly plots support hover (exact (factor, density) readout), drag-to-zoom into - specific regions (critical for sweeps spanning multiple decades), legend-click to - hide/show traces. The static matplotlib version had none of this. - - Bootstrap nav-tabs let users switch between outcomes (success_rate vs task_duration - etc.) without scrolling — only the active outcome's section is visible at a time. - - Jinja2 templating keeps the HTML structure separate from the Python data, which - makes the template editable without touching plot generation. - -The generator produces *one* HTML file. Plotly.js is loaded from the CDN by default, -which keeps file size ~500 KB. For offline viewing, pass ``plotlyjs_mode="inline"`` -to embed the ~3.5 MB Plotly library directly in the HTML. - -The CLI wrapper is ``isaaclab_arena.scripts.generate_sensitivity_report``. -""" - -from __future__ import annotations - -import datetime -import html as html_module -import json -import numpy as np -from pathlib import Path -from typing import Any, Literal - -import plotly.graph_objects as go -from jinja2 import Template - -from isaaclab_arena.analysis.sensitivity.analyzer import make_analyzer -from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec, OutcomeSpec, SensitivityDataset - -_BOOTSTRAP_CSS_URL = "https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/css/bootstrap.min.css" -_BOOTSTRAP_JS_URL = "https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/js/bootstrap.bundle.min.js" - - -def generate_report( - factors_yaml_path: str | Path, - jsonl_path: str | Path, - output_html_path: str | Path, - plotlyjs_mode: Literal["cdn", "inline"] = "cdn", -) -> Path: - """Build a self-contained interactive HTML sensitivity report. - - Reads the schema from ``factors_yaml_path`` and the per-episode data from - ``jsonl_path``, fits one analyzer per declared outcome, and renders one Plotly - figure per (outcome, factor) pair. All figures end up inline in a single HTML file - arranged as Bootstrap nav-tabs (one tab per outcome) with one card per factor inside - each tab. - - Args: - factors_yaml_path: Schema file declaring factors and outcomes. - jsonl_path: episode_summary.jsonl from eval_runner. - output_html_path: Destination for the report file. - plotlyjs_mode: ``"cdn"`` for a small file that needs internet to load Plotly; - ``"inline"`` to embed Plotly.js (~3.5 MB) for offline viewing. - - Returns: - The resolved output path. - """ - factors_yaml_path = Path(factors_yaml_path) - jsonl_path = Path(jsonl_path) - output_html_path = Path(output_html_path) - - dataset = SensitivityDataset(factors_yaml_path, jsonl_path) - - print(f"[INFO] Generating report: {len(dataset.schema.outcomes)} outcomes × {len(dataset.schema.factors)} factors") - outcome_blocks = [] - for outcome in dataset.schema.outcomes: - analyzer = make_analyzer(dataset, outcome.name) - print(f"[INFO] Fitting analyzer for outcome={outcome.name!r} ({type(analyzer).__name__})") - analyzer.fit() - outcome_value = _default_outcome_value_for_analysis(dataset, outcome) - - sections = [] - for factor in dataset.schema.factors: - print(f"[INFO] Rendering ({outcome.name}, {factor.name}) @ outcome_value={outcome_value:g}") - plot_html = _render_marginal_to_plotly_html(analyzer, factor, outcome_value) - stats = _compute_summary_stats(dataset, factor, outcome, outcome_value) - sections.append({ - "factor_name": factor.name, - "plot_html": plot_html, - "stats": _format_stats_for_display(stats), - }) - outcome_blocks.append({ - "name": outcome.name, - "conditioning_value": _format_number(outcome_value), - "analyzer_name": type(analyzer).__name__, - "sections": sections, - }) - - factors_yaml_text = factors_yaml_path.read_text(encoding="utf-8") - raw_jsonl_text = _read_first_rows(jsonl_path, max_rows=10) - - html_text = _render_template( - slice_info=dataset.schema.slice, - num_episodes=len(dataset.rows), - num_factors=len(dataset.schema.factors), - num_outcomes=len(dataset.schema.outcomes), - outcome_blocks=outcome_blocks, - factors_yaml_text=factors_yaml_text, - raw_jsonl_text=raw_jsonl_text, - plotlyjs_mode=plotlyjs_mode, - ) - - output_html_path.parent.mkdir(parents=True, exist_ok=True) - output_html_path.write_text(html_text, encoding="utf-8") - print(f"[INFO] Wrote report → {output_html_path}") - return output_html_path - - -def _default_outcome_value_for_analysis(dataset: SensitivityDataset, outcome: OutcomeSpec) -> float: - """Pick a sensible value to condition the posterior on for this outcome. - - Binary outcomes (only ``{0, 1}`` observed) → ``1.0`` (the "success" branch). - Continuous outcomes → empirical median; a "typical case" value always inside the data range. - """ - outcome_column_index = dataset.outcome_columns[outcome.name] - values = dataset.x[:, outcome_column_index].cpu().numpy() - if set(values.flatten().tolist()).issubset({0.0, 1.0}): - return 1.0 - return float(np.median(values)) - - -def _render_marginal_to_plotly_html(analyzer, factor: FactorSpec, outcome_value: float) -> str: - """Build the Plotly figure for one (analyzer, factor) pair and return its HTML div. - - Dispatches by factor type. Continuous-factor plots get a built-in Plotly slider over - conditioning values when the outcome is continuous (more than 2 distinct values - observed) — the user can drag through "what if we condition on outcome=X instead?" - and watch the posterior curve update in-place. Binary outcomes (only 0/1 observed) - keep the static single-value plot since a 2-step slider has no value. - - Plotly.js itself is *not* embedded per-plot — the page loads it once globally via - CDN or inline (see :func:`_render_template`), so each plot here is just the div + - the constructor JS. - """ - if factor.type == "continuous": - if _outcome_is_continuous(analyzer): - figure = _build_continuous_figure_with_slider(analyzer, factor) - else: - figure = _build_continuous_figure(analyzer, factor, outcome_value) - elif factor.type == "categorical": - figure = _build_categorical_figure(analyzer, factor, outcome_value) - else: - raise NotImplementedError(f"Unsupported factor type {factor.type!r}") - return figure.to_html(include_plotlyjs=False, full_html=False, config={"displaylogo": False}) - - -def _outcome_is_continuous(analyzer) -> bool: - """Heuristic: an outcome is "continuous" (slider-worthy) if it has >2 distinct observed values.""" - outcome_column_index = analyzer.dataset.outcome_columns[analyzer.outcome_name] - values = analyzer.dataset.x[:, outcome_column_index].cpu().numpy().flatten() - unique_values = set(values.tolist()) - if unique_values.issubset({0.0, 1.0}): - return False - return len(unique_values) > 2 - - -def _build_continuous_figure(analyzer, factor: FactorSpec, outcome_value: float) -> go.Figure: - """Continuous-factor density curve + empirical rug. Hover, zoom, pan all native to Plotly.""" - grid, density = analyzer.continuous_marginal_density(factor.name, outcome_value, num_grid_points=200) - factor_column_slice = analyzer.dataset.factor_columns[factor.name] - outcome_column_index = analyzer.dataset.outcome_columns[analyzer.outcome_name] - empirical_theta = analyzer.dataset.theta[:, factor_column_slice].squeeze(-1).cpu().numpy() - # log_uniform factors store theta in log10 space; un-transform for display so rug ticks - # land at the actual intensity values that align with the (linear-scale) curve grid. - if factor.distribution == "log_uniform": - empirical_theta = np.power(10.0, empirical_theta) - empirical_outcomes = analyzer.dataset.x[:, outcome_column_index].cpu().numpy() - density_max = float(np.max(density)) if len(density) else 1.0 - rug_y_value = -0.05 * density_max - - figure = go.Figure() - figure.add_trace( - go.Scatter( - x=grid, - y=density, - mode="lines", - fill="tozeroy", - line={"color": "steelblue", "width": 2}, - fillcolor="rgba(70, 130, 180, 0.2)", - name=f"P({factor.name} | {analyzer.outcome_name}={outcome_value:g})", - hovertemplate=f"{factor.name}=%{{x:.4g}}
    density=%{{y:.4g}}", - ) - ) - - is_binary_outcome = set(empirical_outcomes.flatten().tolist()).issubset({0.0, 1.0}) - if is_binary_outcome: - success_mask = empirical_outcomes >= 0.5 - _add_rug_trace( - figure, - empirical_theta[success_mask], - rug_y_value, - color="seagreen", - name=f"{analyzer.outcome_name} ≥ 0.5 (n={int(success_mask.sum())})", - ) - _add_rug_trace( - figure, - empirical_theta[~success_mask], - rug_y_value * 2, - color="firebrick", - name=f"{analyzer.outcome_name} < 0.5 (n={int((~success_mask).sum())})", - ) - else: - _add_rug_trace( - figure, - empirical_theta, - rug_y_value, - color="slategray", - name=f"observed (n={len(empirical_theta)})", - hover_values=empirical_outcomes, - hover_label=analyzer.outcome_name, - ) - - xaxis_kwargs = {"title": factor.name} - if factor.distribution == "log_uniform": - xaxis_kwargs["type"] = "log" - figure.update_layout( - title=_plot_title(analyzer, factor.name), - xaxis=xaxis_kwargs, - yaxis_title="posterior density", - template="plotly_white", - hovermode="closest", - height=480, - margin={"l": 60, "r": 30, "t": 70, "b": 50}, - legend={"orientation": "h", "yanchor": "bottom", "y": 1.02, "xanchor": "right", "x": 1}, - ) - return figure - - -def _build_continuous_figure_with_slider(analyzer, factor: FactorSpec) -> go.Figure: - """Continuous-factor density curve with a draggable slider over outcome conditioning values. - - Pre-computes the posterior density at ``num_slider_steps`` evenly-spaced conditioning - values across the empirical outcome range, packs them as Plotly frames keyed on the - outcome value, and binds a slider to navigate them. The rug (empirical samples) is - invariant across frames — same data, different conditional curve — so it's drawn once - and held static while only trace 0 (the density curve) updates per frame. - - Total fit cost stays one analyzer.fit() in the outer loop; this adds ~num_slider_steps - calls to ``continuous_marginal_density`` at report-gen time. Each call is a posterior - sample + histogram (~50 ms for KDE, ~100 ms for NPE), so ~1-2 s extra per plot. Trivial - at browse time — the user just drags the slider, no compute. - """ - num_slider_steps = 15 - - outcome_column_index = analyzer.dataset.outcome_columns[analyzer.outcome_name] - empirical_outcomes = analyzer.dataset.x[:, outcome_column_index].cpu().numpy().flatten() - factor_column_slice = analyzer.dataset.factor_columns[factor.name] - empirical_theta = analyzer.dataset.theta[:, factor_column_slice].squeeze(-1).cpu().numpy() - # log_uniform factors store theta in log10 space; un-transform for display so the rug - # aligns with the curve's linear-grid x-coordinates and Plotly's log-axis ticks read as - # actual intensity values. - if factor.distribution == "log_uniform": - empirical_theta = np.power(10.0, empirical_theta) - - min_outcome = float(np.min(empirical_outcomes)) - max_outcome = float(np.max(empirical_outcomes)) - if min_outcome == max_outcome: - # Degenerate (no spread) — fall back to the static plot at that one value. - return _build_continuous_figure(analyzer, factor, min_outcome) - - slider_outcome_values = np.linspace(min_outcome, max_outcome, num_slider_steps) - # Default-active slider step: the empirical median, snapped to the nearest slider value. - default_outcome_value = float(np.median(empirical_outcomes)) - active_step_index = int(np.argmin(np.abs(slider_outcome_values - default_outcome_value))) - - # Pre-compute density curves for each slider step. - density_grids = [] - density_values = [] - for outcome_value in slider_outcome_values: - grid, density = analyzer.continuous_marginal_density(factor.name, float(outcome_value), num_grid_points=200) - density_grids.append(grid) - density_values.append(density) - density_max = float(max(np.max(d) for d in density_values)) if density_values else 1.0 - rug_y_value = -0.05 * density_max - - # Initial figure: trace 0 = density at the default-active slider step, trace 1 = rug. - initial_density = density_values[active_step_index] - initial_grid = density_grids[active_step_index] - figure = go.Figure( - data=[ - go.Scatter( - x=initial_grid, - y=initial_density, - mode="lines", - fill="tozeroy", - line={"color": "steelblue", "width": 2}, - fillcolor="rgba(70, 130, 180, 0.2)", - name=f"P({factor.name} | {analyzer.outcome_name})", - hovertemplate=f"{factor.name}=%{{x:.4g}}
    density=%{{y:.4g}}", - ), - go.Scatter( - x=empirical_theta, - y=np.full(len(empirical_theta), rug_y_value), - mode="markers", - marker={"symbol": "line-ns-open", "size": 14, "color": "slategray", "line": {"width": 2}}, - name=f"observed (n={len(empirical_theta)})", - customdata=empirical_outcomes, - hovertemplate=f"{factor.name}=%{{x:.4g}}
    {analyzer.outcome_name}=%{{customdata:.4g}}", - ), - ], - ) - - # Frames update only trace[0] (density). traces=[0] keeps rug static at trace[1]. - figure.frames = [ - go.Frame( - data=[ - go.Scatter( - x=density_grids[step_index], - y=density_values[step_index], - mode="lines", - fill="tozeroy", - line={"color": "steelblue", "width": 2}, - fillcolor="rgba(70, 130, 180, 0.2)", - hovertemplate=f"{factor.name}=%{{x:.4g}}
    density=%{{y:.4g}}", - ) - ], - name=f"{slider_outcome_values[step_index]:.3g}", - traces=[0], - ) - for step_index in range(num_slider_steps) - ] - - slider_steps = [ - { - "method": "animate", - "args": [ - [f"{slider_outcome_values[step_index]:.3g}"], - { - "mode": "immediate", - "frame": {"duration": 0, "redraw": True}, - "transition": {"duration": 0}, - }, - ], - "label": f"{slider_outcome_values[step_index]:.3g}", - } - for step_index in range(num_slider_steps) - ] - - xaxis_kwargs = {"title": factor.name} - if factor.distribution == "log_uniform": - xaxis_kwargs["type"] = "log" - figure.update_layout( - title=_plot_title(analyzer, factor.name), - xaxis=xaxis_kwargs, - yaxis_title="posterior density", - template="plotly_white", - hovermode="closest", - height=560, - margin={"l": 60, "r": 30, "t": 70, "b": 110}, - legend={"orientation": "h", "yanchor": "bottom", "y": 1.02, "xanchor": "right", "x": 1}, - sliders=[{ - "active": active_step_index, - "currentvalue": { - "prefix": f"Conditioning on {analyzer.outcome_name} = ", - "font": {"size": 14}, - }, - "steps": slider_steps, - "pad": {"t": 50, "b": 10}, - "len": 0.9, - "x": 0.05, - }], - ) - return figure - - -def _add_rug_trace( - figure: go.Figure, - x_values: np.ndarray, - y_value: float, - color: str, - name: str, - hover_values: np.ndarray | None = None, - hover_label: str | None = None, -) -> None: - """Add a single rug (vertical-tick scatter) trace to ``figure``. - - Uses ``line-ns-open`` marker symbol for the classic rug look. If ``hover_values`` is - supplied, hover reveals the per-sample outcome value alongside the factor value. - """ - customdata = None - if hover_values is not None and hover_label is not None: - customdata = hover_values - hovertemplate = f"%{{x:.4g}}
    {hover_label}=%{{customdata:.4g}}" - else: - hovertemplate = f"%{{x:.4g}}{html_module.escape(name)}" - figure.add_trace( - go.Scatter( - x=x_values, - y=np.full(len(x_values), y_value), - mode="markers", - marker={"symbol": "line-ns-open", "size": 14, "color": color, "line": {"width": 2}}, - name=name, - customdata=customdata, - hovertemplate=hovertemplate, - ) - ) - - -def _build_categorical_figure(analyzer, factor: FactorSpec, outcome_value: float) -> go.Figure: - """Categorical-factor side-by-side bars: analyzer posterior vs empirical rate per category.""" - assert factor.choices is not None - choices = factor.choices - num_choices = len(choices) - factor_column_slice = analyzer.dataset.factor_columns[factor.name] - outcome_column_index = analyzer.dataset.outcome_columns[analyzer.outcome_name] - - posterior_probabilities = analyzer.categorical_marginal_probs(factor.name, outcome_value, num_samples=10_000) - empirical_theta_codes = analyzer.dataset.theta[:, factor_column_slice].squeeze(-1).long().cpu().numpy() - empirical_outcomes = analyzer.dataset.x[:, outcome_column_index].cpu().numpy() - empirical_rates = np.zeros(num_choices) - empirical_counts = np.zeros(num_choices, dtype=int) - for code in range(num_choices): - category_mask = empirical_theta_codes == code - empirical_counts[code] = int(category_mask.sum()) - if category_mask.any(): - empirical_rates[code] = float((empirical_outcomes[category_mask] >= 0.5).mean()) - - figure = go.Figure() - figure.add_trace( - go.Bar( - x=choices, - y=posterior_probabilities, - name=f"P(category | {analyzer.outcome_name}={outcome_value:g})", - marker_color="steelblue", - hovertemplate="%{x}
    posterior=%{y:.4g}", - ) - ) - figure.add_trace( - go.Bar( - x=choices, - y=empirical_rates, - name=f"empirical {analyzer.outcome_name} rate", - marker_color="seagreen", - customdata=empirical_counts, - hovertemplate="%{x}
    empirical=%{y:.4g}
    n=%{customdata}", - ) - ) - figure.update_layout( - title=_plot_title(analyzer, factor.name), - barmode="group", - xaxis_title=factor.name, - yaxis_title="probability", - template="plotly_white", - yaxis={"range": [0, 1.05]}, - height=480, - margin={"l": 60, "r": 30, "t": 70, "b": 80}, - legend={"orientation": "h", "yanchor": "bottom", "y": 1.02, "xanchor": "right", "x": 1}, - ) - return figure - - -def _plot_title(analyzer, factor_name: str) -> str: - return ( - f"Sensitivity of {analyzer.outcome_name} to {factor_name}" - f" — {analyzer.dataset.schema.slice.policy}" - f" / {analyzer.dataset.schema.slice.task}" - f" / {analyzer.dataset.schema.slice.embodiment}" - ) - - -def _compute_summary_stats( - dataset: SensitivityDataset, factor: FactorSpec, outcome: OutcomeSpec, outcome_value: float -) -> dict: - """Empirical summary stats kept distinct from the analyzer's posterior for cross-checking.""" - outcome_column_index = dataset.outcome_columns[outcome.name] - outcome_values = dataset.x[:, outcome_column_index].cpu().numpy() - is_binary_outcome = set(outcome_values.flatten().tolist()).issubset({0.0, 1.0}) - - stats: dict[str, Any] = { - "num_episodes": int(len(dataset.rows)), - "is_binary_outcome": is_binary_outcome, - } - if is_binary_outcome: - success_count = int((outcome_values >= 0.5).sum()) - stats["success_count"] = success_count - stats["failure_count"] = int(len(outcome_values) - success_count) - stats["overall_success_rate"] = float(outcome_values.mean()) - else: - stats["outcome_min"] = float(outcome_values.min()) - stats["outcome_max"] = float(outcome_values.max()) - stats["outcome_median"] = float(np.median(outcome_values)) - stats["outcome_mean"] = float(outcome_values.mean()) - - factor_column_slice = dataset.factor_columns[factor.name] - factor_values = dataset.theta[:, factor_column_slice].squeeze(-1).cpu().numpy() - if factor.type == "continuous": - stats["factor_min_observed"] = float(factor_values.min()) - stats["factor_max_observed"] = float(factor_values.max()) - stats["factor_unique_count"] = int(np.unique(np.round(factor_values, 6)).size) - if factor.range is not None and len(factor.range) == 1: - range_low, range_high = factor.range[0] - stats["factor_range"] = [float(range_low), float(range_high)] - elif factor.type == "categorical" and factor.choices is not None: - per_category_counts = {} - for code, choice in enumerate(factor.choices): - per_category_counts[choice] = int((factor_values == code).sum()) - stats["per_category_counts"] = per_category_counts - return stats - - -def _format_stats_for_display(stats: dict) -> list[tuple[str, str]]: - """Flatten a stats dict into ordered (label, html_value) pairs for the template.""" - formatted: list[tuple[str, str]] = [] - for key, value in stats.items(): - formatted.append((key, _format_value(value))) - return formatted - - -def _format_value(value: Any) -> str: - """Render one stats value with light formatting (numbers compact, dicts as nested list).""" - if isinstance(value, dict): - items = "".join( - f"
  • {html_module.escape(str(k))}: {_format_value(v)}
    • " for k, v in value.items() - ) - return f"
      {items}
    " - if isinstance(value, list): - return html_module.escape( - ", ".join(_format_number(item) if isinstance(item, (int, float)) else str(item) for item in value) - ) - if isinstance(value, bool): - return "✓" if value else "✗" - if isinstance(value, (int, float)): - return _format_number(value) - return html_module.escape(str(value)) - - -def _format_number(value) -> str: - """Compact number formatting: int-ish values stay integer; floats use 4-significant-digit g-format.""" - if isinstance(value, bool): - return str(value) - if isinstance(value, int): - return str(value) - if isinstance(value, float): - if value == int(value): - return f"{int(value)}" - return f"{value:.4g}" - return str(value) - - -def _read_first_rows(jsonl_path: Path, max_rows: int) -> str: - """Return the first ``max_rows`` JSONL rows pretty-printed for the accordion snippet.""" - lines: list[str] = [] - with open(jsonl_path, encoding="utf-8") as jsonl_file: - for row_index, raw_line in enumerate(jsonl_file): - if row_index >= max_rows: - lines.append("…") - break - try: - lines.append(json.dumps(json.loads(raw_line), indent=2)) - except json.JSONDecodeError: - lines.append(raw_line.rstrip()) - return "\n".join(lines) - - -_HTML_TEMPLATE = """\ - - - - - - {{ title }} - - {{ plotlyjs_block|safe }} - - - - -

    {{ title }}

    - -
    -
    -
    -
    Policy: {{ slice.policy }}
    -
    Task: {{ slice.task }}
    -
    Embodiment: {{ slice.embodiment }}
    -
    Episodes: {{ num_episodes }}
    -
    Factors declared: {{ num_factors }}
    -
    Outcomes declared: {{ num_outcomes }}
    -
    Generated {{ timestamp }}
    -
    -
    -
    - - - -
    - {% for outcome in outcome_blocks %} -
    -
    -

    - Posterior conditioned on {{ outcome.name }} = {{ outcome.conditioning_value }} - (analyzer: {{ outcome.analyzer_name }}). -

    - {% for section in outcome.sections %} -
    -
    - Factor: {{ section.factor_name }} -
    -
    - {{ section.plot_html|safe }} -
    -
    Empirical summary
    - - - {% for key, value in section.stats %} - - {% endfor %} - -
    {{ key }}{{ value|safe }}
    -
    -
    - {% endfor %} -
    -
    - {% endfor %} -
    - -
    -
    -

    - -

    -
    - 
    {{ factors_yaml_text }}
    -
    -
    -
    -

    - -

    -
    - 
    {{ raw_jsonl_text }}
    -
    -
    -
    - -
    - Generated by isaaclab_arena.analysis.sensitivity.report. Plots are - interactive: hover for exact values, drag-rectangle to zoom, double-click to reset, - click legend entries to hide/show traces. -
    - - - - -""" - - -def _render_template( - slice_info, - num_episodes: int, - num_factors: int, - num_outcomes: int, - outcome_blocks: list[dict], - factors_yaml_text: str, - raw_jsonl_text: str, - plotlyjs_mode: Literal["cdn", "inline"], -) -> str: - """Apply the Jinja2 template to the assembled context.""" - title = f"Sensitivity report — {slice_info.policy} / {slice_info.task} / {slice_info.embodiment}" - if plotlyjs_mode == "inline": - from plotly.offline import get_plotlyjs - - plotlyjs_block = f"" - else: - plotlyjs_block = "" - - rendered = Template(_HTML_TEMPLATE).render( - title=title, - slice=slice_info, - num_episodes=num_episodes, - num_factors=num_factors, - num_outcomes=num_outcomes, - outcome_blocks=outcome_blocks, - factors_yaml_text=factors_yaml_text, - raw_jsonl_text=raw_jsonl_text, - raw_jsonl_max_rows=10, - timestamp=datetime.datetime.now().isoformat(timespec="seconds"), - bootstrap_css=_BOOTSTRAP_CSS_URL, - bootstrap_js=_BOOTSTRAP_JS_URL, - plotlyjs_block=plotlyjs_block, - ) - return rendered diff --git a/isaaclab_arena/scripts/generate_sensitivity_report.py b/isaaclab_arena/scripts/generate_sensitivity_report.py index f2da967ef1..1563bb8575 100644 --- a/isaaclab_arena/scripts/generate_sensitivity_report.py +++ b/isaaclab_arena/scripts/generate_sensitivity_report.py @@ -3,26 +3,26 @@ # # SPDX-License-Identifier: Apache-2.0 -"""CLI driver: build a static HTML sensitivity report from a (factors.yaml, JSONL) pair. +"""CLI driver: build a single-PDF sensitivity report from a (factors.yaml, JSONL) pair. -Thin wrapper around :func:`isaaclab_arena.analysis.sensitivity.report.generate_report`. -For per-plot inspection or A/B comparisons of a single factor/outcome, use -``analyze_sensitivity.py`` instead — this script produces the full deliverable artifact -covering every declared (factor, outcome) combination in one self-contained HTML file. +Thin wrapper around :func:`isaaclab_arena.analysis.sensitivity.pdf_report.generate_pdf_report`. +Produces one PDF with an outcome × factor grid of marginal-posterior plots — the most +important plots in a single file. For per-plot inspection of a single factor/outcome, use +``analyze_sensitivity.py`` instead. Example:: python -m isaaclab_arena.scripts.generate_sensitivity_report \\ --factors_yaml path/to/factors.yaml \\ --episode_summary path/to/episode_summary.jsonl \\ - --output_html /tmp/sensitivity_report.html + --output_pdf /tmp/sensitivity_report.pdf """ from __future__ import annotations import argparse -from isaaclab_arena.analysis.sensitivity.report import generate_report +from isaaclab_arena.analysis.sensitivity.pdf_report import generate_pdf_report def main(): @@ -32,24 +32,14 @@ def main(): "--episode_summary", type=str, required=True, help="Path to episode_summary.jsonl produced by eval_runner." ) parser.add_argument( - "--output_html", + "--output_pdf", type=str, - default="./sensitivity_report.html", - help="Output HTML file. Default: ./sensitivity_report.html. Self-contained interactive HTML.", - ) - parser.add_argument( - "--plotlyjs_mode", - choices=["cdn", "inline"], - default="cdn", - help=( - "Plotly.js bundling. 'cdn' (default, ~500 KB output, needs internet to load) loads" - " Plotly from plot.ly's CDN; 'inline' (~5 MB output, fully offline) embeds Plotly" - " directly. Use 'inline' when sharing to people who may not have internet." - ), + default="./sensitivity_report.pdf", + help="Output PDF file. Default: ./sensitivity_report.pdf.", ) args = parser.parse_args() - generate_report(args.factors_yaml, args.episode_summary, args.output_html, plotlyjs_mode=args.plotlyjs_mode) + generate_pdf_report(args.factors_yaml, args.episode_summary, args.output_pdf) if __name__ == "__main__": From 730a1db38c538a376160af1f3acc29ea3260f281 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 5 Jun 2026 15:05:49 +0200 Subject: [PATCH 07/74] Fix PDF suptitle clipping on narrow single-factor figures Split the report title across two lines (report+episodes / slice) and widen the top margin so it doesn't clip when the grid is a single column. Signed-off-by: Clemens Volk --- isaaclab_arena/analysis/sensitivity/pdf_report.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/pdf_report.py b/isaaclab_arena/analysis/sensitivity/pdf_report.py index 74c81059c7..6ee73802e0 100644 --- a/isaaclab_arena/analysis/sensitivity/pdf_report.py +++ b/isaaclab_arena/analysis/sensitivity/pdf_report.py @@ -62,13 +62,14 @@ def generate_pdf_report( ax.set_title(f"{outcome.name} vs {factor.name}\n(conditioned on {outcome.name}={outcome_value:g})", fontsize=10) slice_info = dataset.schema.slice + # Two lines so the title doesn't clip on narrow (single-factor) figures. figure.suptitle( - f"Sensitivity report — {slice_info.policy} / {slice_info.task} / {slice_info.embodiment}" - f" ({len(dataset.rows)} episodes)", - fontsize=13, + f"Sensitivity report — {len(dataset.rows)} episodes\n" + f"{slice_info.policy} / {slice_info.task} / {slice_info.embodiment}", + fontsize=12, fontweight="bold", ) - figure.tight_layout(rect=[0, 0, 1, 0.97]) # leave room for suptitle + figure.tight_layout(rect=[0, 0, 1, 0.94]) # leave room for the two-line suptitle output_pdf_path = Path(output_pdf_path) output_pdf_path.parent.mkdir(parents=True, exist_ok=True) From 47fdcb03d939d830931170c98eb0c6df8c1dc264 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 5 Jun 2026 15:06:35 +0200 Subject: [PATCH 08/74] Park multi-factor and multi-object sweep configs off the MVP branch MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Scope PR #729 to the MVP: sensitivity to a single light_intensity factor on one object. Remove the multi-factor (multi_factor_overnight) and multi-object (two_object_shiny_matte, pick_up_object) sweep configs — including two 22k-line job configs — preserved on branch cvolk/feature/sensitivity_large_sweep_configs for the larger overnight runs. Keep only the single-factor light_intensity configs. Also drop the stale --factor_keys reference (no such flag; the writer logs the full arena_env_args). Signed-off-by: Clemens Volk --- .../light_intensity_sweep_factors.yaml | 6 +- .../multi_factor_overnight_sweep_factors.yaml | 54 - ...ti_factor_overnight_sweep_jobs_config.json | 22004 ---------------- .../pick_up_object_sweep_factors.yaml | 26 - ...k_up_object_sweep_minimal_jobs_config.json | 70 - .../two_object_shiny_matte_sweep_factors.yaml | 50 - ..._object_shiny_matte_sweep_jobs_config.json | 22004 ---------------- 7 files changed, 3 insertions(+), 44211 deletions(-) delete mode 100644 isaaclab_arena_environments/eval_jobs_configs/multi_factor_overnight_sweep_factors.yaml delete mode 100644 isaaclab_arena_environments/eval_jobs_configs/multi_factor_overnight_sweep_jobs_config.json delete mode 100644 isaaclab_arena_environments/eval_jobs_configs/pick_up_object_sweep_factors.yaml delete mode 100644 isaaclab_arena_environments/eval_jobs_configs/pick_up_object_sweep_minimal_jobs_config.json delete mode 100644 isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_factors.yaml delete mode 100644 isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_jobs_config.json diff --git a/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_factors.yaml b/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_factors.yaml index 4a4c822004..9f9fca1e15 100644 --- a/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_factors.yaml +++ b/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_factors.yaml @@ -5,12 +5,12 @@ # Sensitivity-analysis schema for the light_intensity sweep on droid + pi0. # Paired with: light_intensity_sweep_jobs_config.json (and the minimal variant). -# Hand-authored — must stay in sync with --factor_keys passed to eval_runner. +# Hand-authored — the factor names here must match keys in the eval's arena_env_args. # # - slice identifies the (policy, task, embodiment) the dataset comes from; MNPE/NPE # assumes a single data-generating source per analysis. -# - factors declares what the eval varies; eval_runner is told which arena_env_args -# keys to record via --factor_keys (must match the names here). +# - factors declares what the eval varies; the eval writer logs the full arena_env_args +# per episode (--episode_summary) and the analyzer treats these keys as factors. # - outcomes declares what the eval measures; the writer pulls these from the # registered task metrics (compute_metric_from_recording on each demo). diff --git a/isaaclab_arena_environments/eval_jobs_configs/multi_factor_overnight_sweep_factors.yaml b/isaaclab_arena_environments/eval_jobs_configs/multi_factor_overnight_sweep_factors.yaml deleted file mode 100644 index 35ec94ce53..0000000000 --- a/isaaclab_arena_environments/eval_jobs_configs/multi_factor_overnight_sweep_factors.yaml +++ /dev/null @@ -1,54 +0,0 @@ -# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -# Sensitivity-analysis schema for the multi-factor overnight pick-and-place sweep -# on droid + pi0. Paired with: multi_factor_overnight_sweep_jobs_config.json -# (1000 jobs × num_envs=4 = ~4000 expected episodes). -# -# Sampling distribution: ``light_intensity`` was drawn LOG-uniformly across [0.01, 5000] -# (5.7 decades, equal density per decade). The analyzer in ``dataset.py`` honors the -# ``distribution: log_uniform`` declaration below by transforming theta to log10 space at -# load time and building a matching BoxUniform prior in log space. - -slice: - policy: pi0_remote - task: pick_and_place_maple_table - embodiment: droid_abs_joint_pos - -factors: - light_intensity: - type: continuous - dim: 1 - # Spans 5.7 decades from effectively-pitch-black to bright, sampled LOG-uniformly - # so each decade gets equal density. Lower bound at 0.01 (rather than 0) because - # log(0) is undefined; 0.01 is so far below pi0's failure threshold (~10) that - # camera frames are pixel-black there — practical equivalent of "dark." - range: [[0.01, 5000]] - distribution: log_uniform - pick_up_object: - type: categorical - # Same five Robolab assets used in the synthetic categorical generator. Spans - # easy (cube), medium (can, box, mug), to hard (bowl-into-bowl is awkward for pi0). - # Per-job language_instruction in the jobs config describes the specific object so - # pi0 conditions on the right thing. - choices: - - rubiks_cube_hot3d_robolab - - alphabet_soup_can_hope_robolab - - sugar_box_ycb_robolab - - mug_ycb_robolab - - wooden_bowl_hot3d_robolab - -outcomes: - success_rate: - # Per-episode value of SuccessRateMetric. Returns 0.0 or 1.0 for a single demo. - type: float - object_moved_rate: - # Per-episode value of ObjectMovedRateMetric. Same shape as success_rate. - type: float - task_duration: - # Per-episode wall-clock-equivalent seconds before termination, computed in - # episode_writer as demo_step_count * env.step_dt. Continuous; informative even - # within all-success episodes (fast successes != slow successes near the edges). - type: float diff --git a/isaaclab_arena_environments/eval_jobs_configs/multi_factor_overnight_sweep_jobs_config.json b/isaaclab_arena_environments/eval_jobs_configs/multi_factor_overnight_sweep_jobs_config.json deleted file mode 100644 index a62cf31253..0000000000 --- a/isaaclab_arena_environments/eval_jobs_configs/multi_factor_overnight_sweep_jobs_config.json +++ /dev/null @@ -1,22004 +0,0 @@ -{ - "jobs": [ - { - "name": "multi_0000", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 44.06301, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0001", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 168.2961, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0002", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.1871, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0003", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 71.86047, - "pick_up_object": "wooden_bowl_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the wooden bowl and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0004", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.03129, - "pick_up_object": "mug_ycb_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the mug and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0005", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.01517, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0006", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.1762, - "pick_up_object": "wooden_bowl_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the wooden bowl and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0007", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 26.97036, - "pick_up_object": "wooden_bowl_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the wooden bowl and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0008", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.13588, - "pick_up_object": "wooden_bowl_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the wooden bowl and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0009", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 2.45941, - "pick_up_object": "mug_ycb_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the mug and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0010", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 22.81432, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0011", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 211.06704, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0012", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 95.20844, - "pick_up_object": "sugar_box_ycb_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the sugar box and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0013", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.38334, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0014", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 2851.86252, - "pick_up_object": "sugar_box_ycb_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the sugar box and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0015", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.03824, - "pick_up_object": "mug_ycb_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the mug and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0016", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.03558, - "pick_up_object": "sugar_box_ycb_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the sugar box and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0017", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 27.58142, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0018", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 144.11844, - "pick_up_object": "wooden_bowl_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the wooden bowl and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0019", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.05145, - "pick_up_object": "mug_ycb_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the mug and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0020", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.02812, - "pick_up_object": "sugar_box_ycb_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the sugar box and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0021", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 533.0308, - "pick_up_object": "wooden_bowl_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the wooden bowl and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0022", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 1112.14894, - "pick_up_object": "sugar_box_ycb_robolab" - 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"light_intensity": 4878.3425, - "pick_up_object": "mug_ycb_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the mug and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0973", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 9.87822, - "pick_up_object": "sugar_box_ycb_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the sugar box and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0974", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 2832.18699, - "pick_up_object": "mug_ycb_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the mug and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0975", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 1578.22251, - "pick_up_object": "sugar_box_ycb_robolab" - 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"policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0985", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.43662, - "pick_up_object": "wooden_bowl_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the wooden bowl and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0986", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 71.78767, - "pick_up_object": "mug_ycb_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the mug and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0987", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 1273.24506, - "pick_up_object": "mug_ycb_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the mug and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0988", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 461.22049, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0989", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.45215, - "pick_up_object": "sugar_box_ycb_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the sugar box and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0990", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 2781.66113, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0991", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.94171, - "pick_up_object": "mug_ycb_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the mug and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0992", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 54.74759, - "pick_up_object": "mug_ycb_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the mug and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0993", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.16577, - "pick_up_object": "wooden_bowl_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the wooden bowl and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0994", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.3509, - "pick_up_object": "wooden_bowl_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the wooden bowl and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0995", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 92.78107, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0996", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.04193, - "pick_up_object": "wooden_bowl_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the wooden bowl and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0997", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.23103, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0998", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 65.66949, - "pick_up_object": "wooden_bowl_hot3d_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the wooden bowl and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "multi_0999", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 4, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.19324, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 4, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - } - ] -} diff --git a/isaaclab_arena_environments/eval_jobs_configs/pick_up_object_sweep_factors.yaml b/isaaclab_arena_environments/eval_jobs_configs/pick_up_object_sweep_factors.yaml deleted file mode 100644 index ab5eb24cdf..0000000000 --- a/isaaclab_arena_environments/eval_jobs_configs/pick_up_object_sweep_factors.yaml +++ /dev/null @@ -1,26 +0,0 @@ -# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -# Sensitivity-analysis schema for the pick_up_object sweep on droid + pi0. -# Paired with: pick_up_object_sweep_minimal_jobs_config.json -# Hand-authored — must stay in sync with --factor_keys passed to eval_runner. - -slice: - policy: pi0_remote - task: pick_and_place_maple_table - embodiment: droid_abs_joint_pos - -factors: - pick_up_object: - type: categorical - # Three objects with distinct visual / shape characteristics. List them in the order - # the analyzer should use as integer codes (0=rubiks_cube, 1=alphabet_soup_can, 2=sugar_box). - choices: [rubiks_cube_hot3d_robolab, alphabet_soup_can_hope_robolab, sugar_box_ycb_robolab] - -outcomes: - success_rate: - type: float - object_moved_rate: - type: float diff --git a/isaaclab_arena_environments/eval_jobs_configs/pick_up_object_sweep_minimal_jobs_config.json b/isaaclab_arena_environments/eval_jobs_configs/pick_up_object_sweep_minimal_jobs_config.json deleted file mode 100644 index fc2b3950c9..0000000000 --- a/isaaclab_arena_environments/eval_jobs_configs/pick_up_object_sweep_minimal_jobs_config.json +++ /dev/null @@ -1,70 +0,0 @@ -{ - "jobs": [ - { - "name": "pick_up_object_minimal_rubiks_cube", - "arena_env_args": { - "enable_cameras": true, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 500, - "pick_up_object": "rubiks_cube_hot3d_robolab", - "destination_location": "wooden_bowl_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "pick_up_object_minimal_alphabet_soup_can", - "arena_env_args": { - "enable_cameras": true, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 500, - "pick_up_object": "alphabet_soup_can_hope_robolab", - "destination_location": "wooden_bowl_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "pick_up_object_minimal_sugar_box", - "arena_env_args": { - "enable_cameras": true, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 500, - "pick_up_object": "sugar_box_ycb_robolab", - "destination_location": "wooden_bowl_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the sugar box and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - } - ] -} diff --git a/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_factors.yaml b/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_factors.yaml deleted file mode 100644 index b0fe329c4a..0000000000 --- a/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_factors.yaml +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -# Sensitivity-analysis schema for the focused two-object shiny-vs-matte sweep on -# droid + pi0. Paired with: two_object_shiny_matte_sweep_jobs_config.json -# (1000 jobs = 500 rubiks_cube + 500 alphabet_soup_can, x num_envs=2 x -# num_episodes=2 = up to 4000 expected episodes). -# -# Hypothesis: surface reflectance interacts with lighting in pi0's competence curve. -# This run pairs the Rubik's cube (matte plastic body with glossy stickers — less -# specular overall) against the metal soup can (more specular) and samples both -# densely across the light range. NOTE: rubiks_cube replaced the mustard bottle -# because mustard failed to build repeatedly in the eval_runner path (contact-sensor -# `descendants=0`); rubiks_cube is proven-reliable (281 episodes in the prior -# 5-object sweep). The matte/shiny contrast is therefore weaker than intended. -# -# Sampling distribution: ``light_intensity`` drawn LOG-uniformly across [0.01, 5000] -# (5.7 decades, equal density per decade), same as the prior sweep so the two runs -# are directly comparable. The analyzer honors ``distribution: log_uniform`` by -# transforming theta to log10 space and building a matching BoxUniform prior there. - -slice: - policy: pi0_remote - task: pick_and_place_maple_table - embodiment: droid_abs_joint_pos - -factors: - light_intensity: - type: continuous - dim: 1 - range: [[0.01, 5000]] - distribution: log_uniform - pick_up_object: - type: categorical - # Two objects chosen for contrasting surface reflectance: - # rubiks_cube — matte plastic body + glossy stickers (less specular), proven-reliable - # alphabet_soup_can — metal can w/ label, more specular/SHINY - choices: - - rubiks_cube_hot3d_robolab - - alphabet_soup_can_hope_robolab - -outcomes: - success_rate: - type: float - object_moved_rate: - type: float - task_duration: - type: float diff --git a/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_jobs_config.json b/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_jobs_config.json deleted file mode 100644 index 6c0bd8c8c9..0000000000 --- a/isaaclab_arena_environments/eval_jobs_configs/two_object_shiny_matte_sweep_jobs_config.json +++ /dev/null @@ -1,22004 +0,0 @@ -{ - "jobs": [ - { - "name": "shinymatte_rubiks_0000", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 257.48522, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_alphabet_0001", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 157.92963, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_rubiks_0002", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 3.1707, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_alphabet_0003", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 1126.11687, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_rubiks_0004", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 781.85063, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_alphabet_0005", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 1774.50546, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_rubiks_0006", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 94.24959, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_alphabet_0007", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 7.41633, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_rubiks_0008", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.03441, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_alphabet_0009", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 9.2264, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_rubiks_0010", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 3631.13602, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_alphabet_0011", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 361.77411, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_rubiks_0012", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 217.62678, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_alphabet_0013", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.61951, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_rubiks_0014", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 301.82617, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_alphabet_0015", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 591.85724, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_rubiks_0016", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.05372, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_alphabet_0017", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 6.54785, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_rubiks_0018", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 3.68753, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_alphabet_0019", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.04574, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - 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"language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_alphabet_0989", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 1839.13415, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_rubiks_0990", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 13.47589, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_alphabet_0991", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 3.13393, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_rubiks_0992", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 2.87501, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_alphabet_0993", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 43.82477, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_rubiks_0994", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 36.95101, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_alphabet_0995", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.01031, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_rubiks_0996", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 1.13599, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_alphabet_0997", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 4583.28935, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_rubiks_0998", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 8.35768, - "pick_up_object": "rubiks_cube_hot3d_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "shinymatte_alphabet_0999", - "arena_env_args": { - "enable_cameras": true, - "num_envs": 2, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 0.39886, - "pick_up_object": "alphabet_soup_can_hope_robolab" - }, - "num_episodes": 2, - "language_instruction": "Pick up the soup can and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - } - ] -} \ No newline at end of file From 5f08550dcacf1e1952a746c0e0c652d823d8f600 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 5 Jun 2026 15:23:17 +0200 Subject: [PATCH 09/74] Densify verbose inline comments in the sensitivity module Collapse the multi-line explanatory comment blocks to 1-2 dense lines, keeping the non-obvious 'why' (log-space transforms, the step-count/len gotcha, the deferred pxr import) and dropping the over-explanation. No code changes. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/analyzer.py | 13 ++++-------- .../analysis/sensitivity/dataset.py | 21 +++++++------------ .../analysis/sensitivity/episode_writer.py | 20 ++++++------------ .../analysis/sensitivity/plotting.py | 6 ++---- .../sensitivity/synthetic_data_categorical.py | 5 ++--- .../sensitivity/synthetic_data_continuous.py | 5 ++--- isaaclab_arena/evaluation/eval_runner.py | 10 ++++----- 7 files changed, 27 insertions(+), 53 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/analyzer.py b/isaaclab_arena/analysis/sensitivity/analyzer.py index 5075e2d3c6..bb808192e0 100644 --- a/isaaclab_arena/analysis/sensitivity/analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/analyzer.py @@ -227,10 +227,8 @@ def continuous_marginal_density( factor_column_slice = self.dataset.factor_columns[factor_name] observed_outcome = torch.tensor([outcome_value], dtype=torch.float32) - # ``factor_spec.range`` is always in linear (user-readable) units. For log_uniform - # factors the analyzer's parameter space is log10(theta), so we generate the grid - # in log10 space here and convert it back to linear before returning so the caller - # plots intensities directly on a log-scale x-axis. + # ``range`` is linear; log_uniform factors train in log10(theta), so grid in log10 + # and convert back to linear before returning for a log-scale x-axis. is_log_uniform = factor_spec.distribution == "log_uniform" range_low, range_high = factor_spec.range[0] analyzer_low = np.log10(range_low) if is_log_uniform else range_low @@ -479,11 +477,8 @@ def continuous_marginal_density( assert ( factor_spec.range is not None and len(factor_spec.range) == 1 ), "Continuous-factor marginal expects a populated 1D range" - # For log_uniform factors the analyzer's parameter space is log10(theta) (see - # ``dataset._build_factor_tensor``), so the KDE was fit on log10 values and the - # grid we evaluate on must also be in log space. We convert the linear grid back - # for display at the very end so the caller plots intensities directly on a - # log-scale x-axis. + # log_uniform: KDE was fit on log10(theta), so evaluate the grid in log10 too and + # convert back to linear at the end for a log-scale x-axis. is_log_uniform = factor_spec.distribution == "log_uniform" range_low, range_high = factor_spec.range[0] analyzer_low = np.log10(range_low) if is_log_uniform else range_low diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py index d25cb4928f..49bbe8d318 100644 --- a/isaaclab_arena/analysis/sensitivity/dataset.py +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -48,12 +48,9 @@ class FactorSpec: dim: int = 1 range: list[list[float]] | None = None # one [low, high] pair per dim, continuous only choices: list[str] | None = None # categorical only - # Sampling distribution this factor was drawn from (must match the assumed prior). - # Default is linear uniform on ``range``. ``log_uniform`` declares the factor was drawn - # uniformly in log10 space — required when the sweep spans multiple decades to keep - # samples balanced per decade. Only valid for continuous factors with strictly positive - # ``range``. The dataset loader log10-transforms theta values for log_uniform factors - # so the analyzer trains in the same space the prior covers. + # Sampling distribution (must match the assumed prior). ``log_uniform`` = drawn + # uniformly in log10 space (for multi-decade sweeps; continuous + positive range only); + # the loader log10-transforms theta so the analyzer trains in the prior's space. distribution: Literal["uniform", "log_uniform"] = "uniform" @@ -285,10 +282,8 @@ def _build_factor_tensor(self) -> torch.Tensor: f" factor {factor.name!r} has dim={factor.dim}" ) raw_values = [float(row["arena_env_args"][factor.name]) for row in self.rows] - # For log_uniform factors, theta is stored in log10 space so the prior built - # below (BoxUniform on log10(range)) matches the parameter space the analyzer - # trains and queries in. The factor's declared ``range`` stays in linear units - # for human readability — log-transform happens here, not in the schema. + # log_uniform: store theta in log10 space to match the log10(range) prior; + # the declared ``range`` stays linear for readability. if factor.distribution == "log_uniform": for row_index, value in enumerate(raw_values): assert value > 0, ( @@ -385,10 +380,8 @@ def prior(self): low_bounds: list[float] = [] high_bounds: list[float] = [] - # Continuous factor bounds (one [low, high] pair per dim). For log_uniform factors - # the prior is built in log10 space — the corresponding theta column is also - # log10-transformed in ``_build_factor_tensor``, so the analyzer sees a consistent - # (log-space-θ, log-space-prior) pair without any further transformation downstream. + # Continuous bounds (one [low, high] per dim). log_uniform builds the prior in log10 + # space, matching the log10-transformed theta column from ``_build_factor_tensor``. for factor in self.schema.factors: if factor.type != "continuous": continue diff --git a/isaaclab_arena/analysis/sensitivity/episode_writer.py b/isaaclab_arena/analysis/sensitivity/episode_writer.py index 577cc23b88..c5722739c9 100644 --- a/isaaclab_arena/analysis/sensitivity/episode_writer.py +++ b/isaaclab_arena/analysis/sensitivity/episode_writer.py @@ -80,27 +80,19 @@ def write_episode_summaries(env, job: Job, output_path: str | Path) -> int: for demo_index, demo_name in enumerate(recorded_demos): demo_group = recorded_demos[demo_name] raw_outcome_values = {} - # Find the demo's actual step count by taking the max length across all - # recorded metric arrays. Per-step time-series recorders (e.g. the - # ObjectVelocityRecorder for `object_moved_rate`) produce a (T, …) array - # whose first dim is the step count. Per-episode scalar recorders (e.g. - # the SuccessRecorder) produce a (1,) array regardless of episode length — - # using `len()` on the wrong one collapses task_duration to a single step. + # Step count = max array length over metrics: per-step recorders give (T,…), + # per-episode scalars give (1,); the max avoids collapsing task_duration to 1. demo_step_count = 0 - # cfg.metrics is a MetricsCfg configclass: one field per metric, each a - # MetricTermCfg(compute_metric_func, params, recorder_term_name). Iterate its - # fields the same way MetricsManager does (metrics_manager.py). The per-demo - # value comes from feeding compute_metric_func a single-element list. + # cfg.metrics is a MetricsCfg configclass (one MetricTermCfg field per metric); + # the per-demo value is compute_metric_func fed a single-element list. for metric_name, metric_cfg in registered_metrics.__dict__.items(): recorded_metric_data = demo_group[metric_cfg.recorder_term_name][:] raw_outcome_values[metric_name] = metric_cfg.compute_metric_func( [recorded_metric_data], **metric_cfg.params ) demo_step_count = max(demo_step_count, len(recorded_metric_data)) - # task_duration: wall-clock-equivalent seconds spent on this episode before - # termination. Short for fast successes / early failures, max_episode_length - # for timeouts. Provides a continuous outcome that carries information beyond - # binary success metrics. + # task_duration: seconds before termination (short for fast successes, max for + # timeouts) — a continuous outcome beyond the binary metrics. if demo_step_count > 0: raw_outcome_values["task_duration"] = float(demo_step_count) * env_step_dt outcome_values = metrics_to_plain_python_types(raw_outcome_values) diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index 5c13419289..b7eb93c27c 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -120,10 +120,8 @@ def _draw_continuous_marginal( ) ax.fill_between(grid, 0, density, color="steelblue", alpha=0.2) - # Rug coloring depends on outcome shape. For binary outcomes (only 0/1 observed) the - # green/red ≥/<0.5 split gives a meaningful "successes vs failures" picture. For - # continuous outcomes (e.g. task_duration) the same threshold is nonsensical (every - # sample is ≥ 0.5), so we drop the split and just show all samples as one neutral rug. + # Binary outcomes: split the rug green/red at 0.5 (successes vs failures). Continuous + # outcomes (e.g. task_duration): the threshold is meaningless, so show one neutral rug. is_binary_outcome = set(empirical_outcomes.flatten().tolist()).issubset({0.0, 1.0}) if is_binary_outcome: success_mask = empirical_outcomes >= 0.5 diff --git a/isaaclab_arena/analysis/sensitivity/synthetic_data_categorical.py b/isaaclab_arena/analysis/sensitivity/synthetic_data_categorical.py index 550048b03c..21a3eec4fa 100644 --- a/isaaclab_arena/analysis/sensitivity/synthetic_data_categorical.py +++ b/isaaclab_arena/analysis/sensitivity/synthetic_data_categorical.py @@ -35,9 +35,8 @@ import random from pathlib import Path -# Five distinct objects, like the maple-table droid sweep. The first three are "easy" -# (high success), the last two are "hard" (low success) — a known signal the analyzer -# should recover. +# Five objects like the maple-table sweep: first three "easy" (high success), last two +# "hard" (low) — a known signal the analyzer should recover. DEFAULT_CHOICES = [ "rubiks_cube_hot3d_robolab", "wooden_bowl_hot3d_robolab", diff --git a/isaaclab_arena/analysis/sensitivity/synthetic_data_continuous.py b/isaaclab_arena/analysis/sensitivity/synthetic_data_continuous.py index 0c353dbaf5..65537b837b 100644 --- a/isaaclab_arena/analysis/sensitivity/synthetic_data_continuous.py +++ b/isaaclab_arena/analysis/sensitivity/synthetic_data_continuous.py @@ -53,9 +53,8 @@ INTENSITY_LOW = 10.0 INTENSITY_HIGH = 5000.0 -# A self-contained factors.yaml template for the synthetic dataset. Kept inline (rather -# than imported from episode_writer.py) so this module stays a pure-python dev tool — -# importing episode_writer would transitively load pxr via isaaclab_arena.metrics. +# Inline factors.yaml template (not imported) so this stays a pure-python dev tool — +# importing episode_writer would pull in pxr via isaaclab_arena.metrics. _SYNTHETIC_FACTORS_YAML = """\ # factors.yaml — synthetic dataset for analyzer smoke-testing. # Auto-emitted by isaaclab_arena.analysis.sensitivity.synthetic_data_continuous alongside the JSONL. diff --git a/isaaclab_arena/evaluation/eval_runner.py b/isaaclab_arena/evaluation/eval_runner.py index ca7162d713..e6abbbd228 100644 --- a/isaaclab_arena/evaluation/eval_runner.py +++ b/isaaclab_arena/evaluation/eval_runner.py @@ -200,9 +200,8 @@ def main(): # Check if any job requires cameras and enable them if needed before starting simulation enable_cameras_if_required(eval_jobs_config, args_cli) - # Per-episode summary recording is opt-in via --episode_summary. The writer logs the - # full arena_env_args dict per episode; the analyzer side decides which keys to treat - # as factors via factors.yaml. No eval-side knowledge of "factors" required. + # --episode_summary (opt-in): the writer logs the full arena_env_args per episode; + # the analyzer's factors.yaml decides which keys are factors (no eval-side knowledge). episode_summary_enabled = args_cli.episode_summary is not None if episode_summary_enabled: print( @@ -261,9 +260,8 @@ def main(): ) if episode_summary_enabled: - # Deferred import — episode_writer transitively touches pxr via - # isaaclab_arena.metrics.metrics. Matches the policy_runner.py - # pattern for compute_metrics. + # Deferred import: episode_writer transitively touches pxr, so import + # after sim init — same pattern as policy_runner's compute_metrics. from isaaclab_arena.analysis.sensitivity.episode_writer import write_episode_summaries rows = write_episode_summaries(env, job, args_cli.episode_summary) From 3b51bae5ab7ad1143ba7692a831470924604b989 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 5 Jun 2026 15:58:58 +0200 Subject: [PATCH 10/74] Move log_uniform sampling out of the MVP Drop log_uniform from the first PR: the FactorSpec.distribution field, the YAML parsing/validation, the log10 theta transform + log-space prior in dataset, the log-grid handling in NPE/KDE marginals, and the log x-axis in plotting. The MVP sweeps linearly; analyzers and plotting are unchanged for linear factors. The full log_uniform implementation is preserved on branch cvolk/feature/sensitivity_log_uniform for a follow-up PR. Verified post-removal: KDE, MNPE+categorical, and NPE all fit and render. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/analyzer.py | 27 +++-------- .../analysis/sensitivity/dataset.py | 47 ++----------------- .../analysis/sensitivity/plotting.py | 6 --- 3 files changed, 10 insertions(+), 70 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/analyzer.py b/isaaclab_arena/analysis/sensitivity/analyzer.py index bb808192e0..2c2cdde935 100644 --- a/isaaclab_arena/analysis/sensitivity/analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/analyzer.py @@ -227,15 +227,10 @@ def continuous_marginal_density( factor_column_slice = self.dataset.factor_columns[factor_name] observed_outcome = torch.tensor([outcome_value], dtype=torch.float32) - # ``range`` is linear; log_uniform factors train in log10(theta), so grid in log10 - # and convert back to linear before returning for a log-scale x-axis. - is_log_uniform = factor_spec.distribution == "log_uniform" range_low, range_high = factor_spec.range[0] - analyzer_low = np.log10(range_low) if is_log_uniform else range_low - analyzer_high = np.log10(range_high) if is_log_uniform else range_high if self.dataset.theta.shape[1] == 1: - grid_tensor = torch.linspace(analyzer_low, analyzer_high, num_grid_points, dtype=torch.float32).unsqueeze(1) + grid_tensor = torch.linspace(range_low, range_high, num_grid_points, dtype=torch.float32).unsqueeze(1) with torch.no_grad(): log_probabilities = self.posterior.log_prob(grid_tensor, x=observed_outcome) density_numpy = torch.exp(log_probabilities).cpu().numpy() @@ -244,14 +239,12 @@ def continuous_marginal_density( with torch.no_grad(): posterior_samples = self.posterior.sample((10_000,), x=observed_outcome) factor_column_samples = posterior_samples[:, factor_column_slice].squeeze(-1).cpu().numpy() - grid_numpy = np.linspace(analyzer_low, analyzer_high, num_grid_points) + grid_numpy = np.linspace(range_low, range_high, num_grid_points) histogram_density, bin_edges = np.histogram( - factor_column_samples, bins=40, range=(analyzer_low, analyzer_high), density=True + factor_column_samples, bins=40, range=(range_low, range_high), density=True ) density_numpy = np.interp(grid_numpy, 0.5 * (bin_edges[:-1] + bin_edges[1:]), histogram_density) - if is_log_uniform: - grid_numpy = np.power(10.0, grid_numpy) # log10 → linear for display return grid_numpy, density_numpy def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: @@ -477,19 +470,13 @@ def continuous_marginal_density( assert ( factor_spec.range is not None and len(factor_spec.range) == 1 ), "Continuous-factor marginal expects a populated 1D range" - # log_uniform: KDE was fit on log10(theta), so evaluate the grid in log10 too and - # convert back to linear at the end for a log-scale x-axis. - is_log_uniform = factor_spec.distribution == "log_uniform" range_low, range_high = factor_spec.range[0] - analyzer_low = np.log10(range_low) if is_log_uniform else range_low - analyzer_high = np.log10(range_high) if is_log_uniform else range_high - analyzer_grid = np.linspace(analyzer_low, analyzer_high, num_grid_points) - linear_grid = np.power(10.0, analyzer_grid) if is_log_uniform else analyzer_grid + grid = np.linspace(range_low, range_high, num_grid_points) if outcome_value < 0.5 or self._kde is None: - uniform_density = 1.0 / max(analyzer_high - analyzer_low, 1e-9) - return linear_grid, np.full_like(analyzer_grid, uniform_density) - return linear_grid, self._kde(analyzer_grid) + uniform_density = 1.0 / max(range_high - range_low, 1e-9) + return grid, np.full_like(grid, uniform_density) + return grid, self._kde(grid) def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: raise NotImplementedError( diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py index 49bbe8d318..7c108e760f 100644 --- a/isaaclab_arena/analysis/sensitivity/dataset.py +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -27,7 +27,6 @@ from __future__ import annotations import json -import math import torch import yaml from dataclasses import dataclass @@ -48,10 +47,6 @@ class FactorSpec: dim: int = 1 range: list[list[float]] | None = None # one [low, high] pair per dim, continuous only choices: list[str] | None = None # categorical only - # Sampling distribution (must match the assumed prior). ``log_uniform`` = drawn - # uniformly in log10 space (for multi-decade sweeps; continuous + positive range only); - # the loader log10-transforms theta so the analyzer trains in the prior's space. - distribution: Literal["uniform", "log_uniform"] = "uniform" @dataclass @@ -119,27 +114,6 @@ def from_yaml(cls, path: str | Path) -> FactorSchema: f"factors.yaml at {path} factor {factor_name!r} has unknown type {factor_type!r};" " expected 'continuous' or 'categorical'" ) - distribution = factor_block.get("distribution", "uniform") - assert distribution in ("uniform", "log_uniform"), ( - f"factors.yaml at {path} factor {factor_name!r} has unknown distribution" - f" {distribution!r}; expected 'uniform' or 'log_uniform'" - ) - if distribution == "log_uniform": - assert factor_type == "continuous", ( - f"factors.yaml at {path} factor {factor_name!r}: distribution 'log_uniform'" - " is only valid for continuous factors" - ) - factor_range = factor_block.get("range") - assert factor_range is not None, ( - f"factors.yaml at {path} factor {factor_name!r}: distribution 'log_uniform'" - " requires an explicit `range:` (cannot be inferred at load time)" - ) - for dim_index, (dim_low, dim_high) in enumerate(factor_range): - assert float(dim_low) > 0, ( - f"factors.yaml at {path} factor {factor_name!r} dim {dim_index}:" - " log_uniform requires strictly positive range bounds;" - f" got low={dim_low}" - ) factors.append( FactorSpec( name=factor_name, @@ -147,7 +121,6 @@ def from_yaml(cls, path: str | Path) -> FactorSchema: dim=factor_block.get("dim", 1), range=factor_block.get("range"), choices=factor_block.get("choices"), - distribution=distribution, ) ) @@ -282,15 +255,6 @@ def _build_factor_tensor(self) -> torch.Tensor: f" factor {factor.name!r} has dim={factor.dim}" ) raw_values = [float(row["arena_env_args"][factor.name]) for row in self.rows] - # log_uniform: store theta in log10 space to match the log10(range) prior; - # the declared ``range`` stays linear for readability. - if factor.distribution == "log_uniform": - for row_index, value in enumerate(raw_values): - assert value > 0, ( - f"Row {row_index} factor {factor.name!r} has value {value!r}" - " ≤ 0; log_uniform factors require strictly positive samples" - ) - raw_values = [math.log10(v) for v in raw_values] factor_column = torch.tensor(raw_values, dtype=torch.float32).unsqueeze(1) factor_columns.append(factor_column) @@ -380,19 +344,14 @@ def prior(self): low_bounds: list[float] = [] high_bounds: list[float] = [] - # Continuous bounds (one [low, high] per dim). log_uniform builds the prior in log10 - # space, matching the log10-transformed theta column from ``_build_factor_tensor``. + # Continuous bounds (one [low, high] per dim). for factor in self.schema.factors: if factor.type != "continuous": continue assert factor.range is not None, f"Factor {factor.name!r} has no range and was not inferred" for dim_low, dim_high in factor.range: - if factor.distribution == "log_uniform": - low_bounds.append(math.log10(float(dim_low))) - high_bounds.append(math.log10(float(dim_high))) - else: - low_bounds.append(float(dim_low)) - high_bounds.append(float(dim_high)) + low_bounds.append(float(dim_low)) + high_bounds.append(float(dim_high)) # Categorical factor bounds: [0, num_choices - 1] per factor (one column). for factor in self.schema.factors: diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index b7eb93c27c..d9310afeef 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -105,10 +105,6 @@ def _draw_continuous_marginal( factor_column_slice = analyzer.dataset.factor_columns[factor_spec.name] outcome_column_index = analyzer.dataset.outcome_columns[analyzer.outcome_name] empirical_theta_values = analyzer.dataset.theta[:, factor_column_slice].squeeze(-1).cpu().numpy() - # For log_uniform factors the dataset stores log10(theta); the rug ticks need to be at - # the actual intensity values to align with the linear-scale grid returned above. - if factor_spec.distribution == "log_uniform": - empirical_theta_values = np.power(10.0, empirical_theta_values) empirical_outcomes = analyzer.dataset.x[:, outcome_column_index].cpu().numpy() ax.plot( @@ -152,8 +148,6 @@ def _draw_continuous_marginal( ) ax.set_xlabel(factor_spec.name) ax.set_ylabel("posterior density") - if factor_spec.distribution == "log_uniform": - ax.set_xscale("log") ax.legend(loc="best", fontsize=9) ax.grid(alpha=0.3) From 83728462c87040aa52a3985f378e28dc83bb2d50 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 12:15:05 +0200 Subject: [PATCH 11/74] Slim the sensitivity module: drop module docstrings and the sbi null-tracker - Remove the verbose module-level docstrings across the sensitivity package; the two synthetic-data generators and the two CLI scripts now pass a short literal argparse description instead of `description=__doc__`. - Remove the `_NullTracker` workaround. With in-container runs no longer executing as root, sbi's default tracker writes a (gitignored) `sbi-logs/` owned by the user, so the PermissionError it guarded against no longer occurs. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/analyzer.py | 83 +------------------ .../analysis/sensitivity/dataset.py | 21 ----- .../analysis/sensitivity/episode_writer.py | 22 ----- .../analysis/sensitivity/pdf_report.py | 17 +--- .../analysis/sensitivity/plotting.py | 15 ---- .../sensitivity/synthetic_data_categorical.py | 33 ++------ .../sensitivity/synthetic_data_continuous.py | 47 ++--------- isaaclab_arena/scripts/analyze_sensitivity.py | 22 ++--- .../scripts/generate_sensitivity_report.py | 22 ++--- 9 files changed, 32 insertions(+), 250 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/analyzer.py b/isaaclab_arena/analysis/sensitivity/analyzer.py index 2c2cdde935..74c0375576 100644 --- a/isaaclab_arena/analysis/sensitivity/analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/analyzer.py @@ -3,51 +3,6 @@ # # SPDX-License-Identifier: Apache-2.0 -"""Inference-only analyzers for v0.3 sensitivity analysis. - -What this module does in plain English --------------------------------------- -Given a dataset of (factor values, outcome values) pairs from a policy evaluation, the -analyzer learns the *conditional* distribution of factor values given a chosen outcome -value (e.g. "given the episode succeeded, which factor values were most consistent?"). -This is the **posterior** ``P(theta | outcome=success)``. Under v0.3's uniform prior, -this posterior's peak is also the operating point ``argmax P(success | theta)`` — so -plotting the marginal posterior over one factor identifies the values that maximize -success rate. - -The three concrete analyzers cover the three relevant factor-mix cases: - - - ``NPEAnalyzer`` — **N**eural **P**osterior **E**stimation. Used when *all* - declared factors are continuous. Trains a normalizing-flow density estimator on - ``(theta, x)`` pairs and exposes ``posterior.sample`` / ``posterior.log_prob``. - Limitation: with a binary outcome and a 1D theta, sbi falls back to a Gaussian - density and the recovered peak reflects the *mean* of successful theta values - rather than the true *mode* — a known caveat we surface as a [WARN] at fit time. - - ``MNPEAnalyzer`` — **M**ixed **N**eural **P**osterior **E**stimation. Used when - the schema has *both* continuous and categorical factors. sbi's MixedDensityEstimator - routes continuous columns through the same kind of flow NPE uses while routing - discrete columns through a categorical mass estimator. - - ``EmpiricalAnalyzer`` — Pure-categorical schemas. Skip the neural fit entirely: under - a uniform prior the posterior ``P(category | success)`` is *exactly* the normalized - per-category empirical success rate. No smoothing improves on that, and sbi MNPE - in version 0.26 also refuses to train without at least one continuous theta column. - -``make_analyzer(dataset, outcome_name)`` is the factory: callers don't need to know about -the hierarchy, they just hand it a dataset and outcome name. - -How rendering fits in ---------------------- -This module is *inference-only*. The sibling ``plotting`` module reads the analyzer's -public queries (``continuous_marginal_density``, ``categorical_marginal_probs``) and -renders matplotlib figures. Decoupling the two means new plot types don't require -analyzer changes, and analyzer changes don't risk breaking the plot. - -Public posterior-query surface used by ``plotting.py``: - - ``BaseAnalyzer.categorical_marginal_probs(factor_name, outcome_value, num_samples)`` - - ``PosteriorAnalyzer.continuous_marginal_density(factor_name, outcome_value, num_grid_points)`` - (NOT defined on ``EmpiricalAnalyzer`` — that analyzer rejects continuous factors at init time) -""" - from __future__ import annotations import numpy as np @@ -103,36 +58,6 @@ def _factor_spec(self, factor_name: str) -> FactorSpec: return next(factor for factor in self.dataset.schema.factors if factor.name == factor_name) -class _NullTracker: - """A no-op tracker satisfying sbi's ``Tracker`` protocol — discards training metrics. - - By default sbi logs TensorBoard training curves under ``/sbi-logs`` - (``get_log_root`` hardcodes the cwd). One-shot report generation fits each analyzer - once and never reads those curves, yet the default tracker makes fitting fail with - ``PermissionError`` whenever the cwd isn't writable — e.g. a repo checkout in a - non-root container. Discarding the metrics removes the write, and with it the hidden - cwd dependency, so analysis runs from any directory. sbi only calls ``log_metric`` - and ``flush``; the remaining members satisfy the protocol. - """ - - log_dir = None - - def log_metric(self, name, value, step=None): - pass - - def log_metrics(self, metrics, step=None): - pass - - def log_params(self, params): - pass - - def add_figure(self, name, figure, step=None): - pass - - def flush(self): - pass - - class PosteriorAnalyzer(BaseAnalyzer): """Common base for the sbi-driven analyzers (NPE and MNPE). @@ -159,12 +84,8 @@ def _inference_cls(self): raise NotImplementedError("PosteriorAnalyzer subclasses must implement _inference_cls") def _make_inference(self): - """Instantiate the chosen sbi inference class on the dataset's uniform prior. - - Passes a ``_NullTracker`` so fitting writes no TensorBoard logs and stays - independent of the launch directory — see ``_NullTracker`` for why. - """ - return self._inference_cls()(prior=self.dataset.prior, tracker=_NullTracker()) + """Instantiate the chosen sbi inference class on the dataset's uniform prior.""" + return self._inference_cls()(prior=self.dataset.prior) def fit(self, training_batch_size: int = 50) -> None: """Train the chosen sbi estimator on ``(theta, x_selected)`` and stash the posterior. diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py index 7c108e760f..b6ca8c846d 100644 --- a/isaaclab_arena/analysis/sensitivity/dataset.py +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -3,27 +3,6 @@ # # SPDX-License-Identifier: Apache-2.0 -"""Schema parser and dataset loader for sensitivity analysis. - -Combines a hand-authored ``factors.yaml`` (the declared schema + priors) with an -``episode_summary.jsonl`` (per-episode factor draws + outcome values, written by -``episode_writer``) into the tensors that ``sbi`` consumes for posterior inference. - -Vocabulary refresher (for readers new to simulation-based inference / SBI): - - **theta** — the *factor* values per episode. The "inputs" we vary in the eval (e.g. - ``light_intensity``, ``pick_up_object``). Shape ``(num_episodes, total_factor_dim)``, - continuous factors come first then categoricals. - - **x** — the *outcome* values per episode. The "outputs" the policy produced (e.g. - ``success_rate``, ``object_moved_rate``). Shape ``(num_episodes, num_outcomes)``. - - **prior** — the assumed distribution over theta *before* seeing data. v0.3 ships - uniform priors only, encoded as ``sbi.utils.BoxUniform``. - - **factor_columns** — map from factor name to its column slice in theta, so - downstream code can extract a marginal by name without knowing the layout. - -MVP-2 supports continuous-1D and categorical factors. Vector continuous (``dim > 1``) -factors still raise ``NotImplementedError`` so adding them later is a fill-in. -""" - from __future__ import annotations import json diff --git a/isaaclab_arena/analysis/sensitivity/episode_writer.py b/isaaclab_arena/analysis/sensitivity/episode_writer.py index c5722739c9..aef80b4f0a 100644 --- a/isaaclab_arena/analysis/sensitivity/episode_writer.py +++ b/isaaclab_arena/analysis/sensitivity/episode_writer.py @@ -3,28 +3,6 @@ # # SPDX-License-Identifier: Apache-2.0 -"""Per-episode summary writer for sensitivity analysis. - -``write_episode_summaries`` appends one JSONL row per recorded demo for a just-completed -job. Each row carries: - - - ``job_name`` and ``episode_idx`` for traceability, - - ``arena_env_args`` — the *entire* job.arena_env_args_dict, i.e. every value that - parameterized the env for this episode, - - ``outcomes`` — per-episode outcome values from the task's registered metrics, extracted - from the recorded hdf5 demos via each metric's ``compute_metric_from_recording``. - -The eval-side writer is intentionally analysis-agnostic: it logs all env state, and the -analyzer's ``factors.yaml`` decides which subset of those keys to treat as factors. This -keeps the writer free of any "what counts as a factor?" knowledge. - -Import-order note: this module legitimately touches pxr at import time via -``isaaclab_arena.metrics.metrics`` (which imports ``isaaclab.envs.manager_based_rl_env``). -Like ``metrics`` itself, callers must defer importing this module until *after* -``SimulationAppContext`` is active — see ``policy_runner.py`` (which uses the same pattern -for ``compute_metrics``) and ``eval_runner.py``'s per-job try block for examples. -""" - from __future__ import annotations import h5py diff --git a/isaaclab_arena/analysis/sensitivity/pdf_report.py b/isaaclab_arena/analysis/sensitivity/pdf_report.py index 6ee73802e0..92493d7a4e 100644 --- a/isaaclab_arena/analysis/sensitivity/pdf_report.py +++ b/isaaclab_arena/analysis/sensitivity/pdf_report.py @@ -3,21 +3,10 @@ # # SPDX-License-Identifier: Apache-2.0 -"""Single-PDF sensitivity report — the most important plots, one file on disk. - -``generate_pdf_report`` reads a (factors.yaml, JSONL) pair, fits one analyzer per declared -outcome, and lays out an outcome × factor grid of marginal-posterior plots in a single PDF. -Each row is one outcome (conditioned on a sensible default value); each column is one factor. - -This is the minimal, robolab-style deliverable. The richer interactive HTML report is -intentionally out of scope here — see the follow-up PR. -""" - from __future__ import annotations -from pathlib import Path - import numpy as np +from pathlib import Path from isaaclab_arena.analysis.sensitivity.analyzer import make_analyzer from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset @@ -59,7 +48,9 @@ def generate_pdf_report( for col_index, factor in enumerate(factors): ax = axes[row_index][col_index] draw_marginal(ax, analyzer, factor.name, outcome_value=outcome_value) - ax.set_title(f"{outcome.name} vs {factor.name}\n(conditioned on {outcome.name}={outcome_value:g})", fontsize=10) + ax.set_title( + f"{outcome.name} vs {factor.name}\n(conditioned on {outcome.name}={outcome_value:g})", fontsize=10 + ) slice_info = dataset.schema.slice # Two lines so the title doesn't clip on narrow (single-factor) figures. diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index d9310afeef..9f28cd1313 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -3,21 +3,6 @@ # # SPDX-License-Identifier: Apache-2.0 -"""Plot renderers for sensitivity analysis. - -Pure-visualization module. Calls into the analyzer's public posterior queries -(``continuous_marginal_density`` and ``categorical_marginal_probs``) and renders matplotlib -figures. Decoupled from the analyzer hierarchy so new plot types can be added without -touching inference code, and so existing plot code can be tested with mock posteriors. - -Two entry points: - - ``draw_marginal(ax, analyzer, factor_name, ...)`` draws one factor's marginal onto a - caller-supplied Axes (used by the multi-plot PDF report). - - ``plot_marginal(analyzer, factor_name, output_path, ...)`` wraps it in its own figure - and saves a standalone image (used by the single-factor CLI). -Both dispatch by factor type to the right renderer. -""" - from __future__ import annotations import numpy as np diff --git a/isaaclab_arena/analysis/sensitivity/synthetic_data_categorical.py b/isaaclab_arena/analysis/sensitivity/synthetic_data_categorical.py index 21a3eec4fa..3409ac8d91 100644 --- a/isaaclab_arena/analysis/sensitivity/synthetic_data_categorical.py +++ b/isaaclab_arena/analysis/sensitivity/synthetic_data_categorical.py @@ -3,31 +3,6 @@ # # SPDX-License-Identifier: Apache-2.0 -"""Synthetic JSONL generator for the MVP-2 categorical-factor analyzer smoke test. - -Generates a fake ``episode_summary.jsonl`` where a single categorical factor -``pick_up_object`` drives the success probability. Half of the choices are "easy" -(high success rate), the other half are "hard" (low success rate). With enough samples -the analyzer's recovered ``P(category | success=1)`` should concentrate on the easy -choices, and the empirical per-category bar should match the configured rates within -binomial noise. - -Sampling is **uniform over the categorical choices** (matches the semantics of -``Choose(...)`` in Alex's variation system and the uniform prior the analyzer assumes). - -Pair with the auto-emitted factors.yaml. End-to-end smoke test: - - /isaac-sim/python.sh -m isaaclab_arena.analysis.sensitivity.synthetic_data_categorical \\ - --output /tmp/syn_cat.jsonl - /isaac-sim/python.sh -m isaaclab_arena.scripts.analyze_sensitivity \\ - --factors_yaml /tmp/factors.yaml \\ - --episode_summary /tmp/syn_cat.jsonl \\ - --figure_path /tmp/syn_cat_plot.png - -Expected output: a bar chart where the "easy" choices have ~3x more posterior mass and -empirical success rate than the "hard" choices. -""" - from __future__ import annotations import argparse @@ -73,7 +48,13 @@ def _factors_yaml_text(choices: list[str]) -> str: def main(): - parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser = argparse.ArgumentParser( + description=( + "Generate a synthetic episode_summary.jsonl where a single categorical factor drives the " + "success probability, for smoke-testing the categorical sensitivity analysis pipeline." + ), + formatter_class=argparse.RawDescriptionHelpFormatter, + ) parser.add_argument( "--output", type=str, diff --git a/isaaclab_arena/analysis/sensitivity/synthetic_data_continuous.py b/isaaclab_arena/analysis/sensitivity/synthetic_data_continuous.py index 65537b837b..db8d592e02 100644 --- a/isaaclab_arena/analysis/sensitivity/synthetic_data_continuous.py +++ b/isaaclab_arena/analysis/sensitivity/synthetic_data_continuous.py @@ -3,45 +3,6 @@ # # SPDX-License-Identifier: Apache-2.0 -"""Synthetic JSONL generator for smoke-testing the sensitivity analysis pipeline. - -Produces a fake ``episode_summary.jsonl`` with a known linear-Gaussian competence band: - - P(success | intensity) = exp(-(intensity - center)^2 / (2 * sigma^2)) - -i.e. a Gaussian directly in linear intensity space centered on a trained operating point. - -Sampling is **linear-uniform** over ``[10, 5000]`` (one intensity drawn independently per -episode). This matches the semantics of ``Uniform(10, 5000)`` in Alex's variation system -and matches the uniform prior declared in factors.yaml. With these choices the smoke -test should recover the posterior peak exactly at ``center``, because: - - 1. linear uniform sampling matches the declared uniform prior (no sampling bias), - 2. a linear-Gaussian likelihood is symmetric in linear theta-space, so its mode - equals its mean — and the NPE Gaussian fallback for 1D binary outcomes fits - the mean, recovering the true center. - -A more realistic competence band would be log-Gaussian (asymmetric: cameras blind fast -at low intensity, saturate gradually at high), but that introduces a peak-bias artifact -that masks pipeline-correctness signal. This smoke test deliberately matches the -structural assumptions the analyzer can recover exactly, so any mismatch in the output -points to a real bug rather than a known statistical limitation. - -Pair with the hand-authored ``light_intensity_sweep_factors.yaml`` so the analyzer -script can be smoke-tested end-to-end without running Isaac Sim: - - /isaac-sim/python.sh -m isaaclab_arena.analysis.sensitivity.synthetic_data_continuous \\ - --output /tmp/syn.jsonl - /isaac-sim/python.sh -m isaaclab_arena.scripts.analyze_sensitivity \\ - --factors_yaml isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_factors.yaml \\ - --episode_summary /tmp/syn.jsonl \\ - --figure_path /tmp/syn_plot.png - -Expected output: a posterior-density curve peaking at ``center`` (default 500), with -empirical rug markers showing successes clustered around the center and failures at -both extremes. -""" - from __future__ import annotations import argparse @@ -92,7 +53,13 @@ def success_probability(intensity: float, center: float, sigma: float) -> float: def main(): - parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser = argparse.ArgumentParser( + description=( + "Generate a synthetic episode_summary.jsonl with a known linear-Gaussian competence band " + "for smoke-testing the continuous sensitivity analysis pipeline." + ), + formatter_class=argparse.RawDescriptionHelpFormatter, + ) parser.add_argument("--output", type=str, default="/tmp/synthetic_episode_summary.jsonl", help="Output JSONL path.") parser.add_argument( "--factors-yaml-out", diff --git a/isaaclab_arena/scripts/analyze_sensitivity.py b/isaaclab_arena/scripts/analyze_sensitivity.py index 052948b2f5..411d9f42ca 100644 --- a/isaaclab_arena/scripts/analyze_sensitivity.py +++ b/isaaclab_arena/scripts/analyze_sensitivity.py @@ -3,21 +3,6 @@ # # SPDX-License-Identifier: Apache-2.0 -"""CLI driver for 1D continuous sensitivity analysis (MVP-1). - -Loads a SensitivityDataset from a paired (factors.yaml, episode_summary.jsonl), trains -NPE on the selected outcome column, and saves a posterior-marginal plot for the chosen -factor. - -Example: - python -m isaaclab_arena.scripts.analyze_sensitivity \\ - --factors_yaml isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_factors.yaml \\ - --episode_summary ./episode_summary.jsonl \\ - --figure_path ./light_intensity_sensitivity.png - -This script runs entirely offline — no Isaac Sim, no policy server. -""" - from __future__ import annotations import argparse @@ -28,7 +13,12 @@ def main(): - parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser = argparse.ArgumentParser( + description=( + "Offline 1D continuous sensitivity analysis: fit an analyzer on a " + "(factors.yaml, episode_summary.jsonl) pair and save a posterior-marginal plot." + ) + ) parser.add_argument("--factors_yaml", type=str, required=True, help="Path to factors.yaml.") parser.add_argument( "--episode_summary", type=str, required=True, help="Path to episode_summary.jsonl produced by eval_runner." diff --git a/isaaclab_arena/scripts/generate_sensitivity_report.py b/isaaclab_arena/scripts/generate_sensitivity_report.py index 1563bb8575..5a865fd298 100644 --- a/isaaclab_arena/scripts/generate_sensitivity_report.py +++ b/isaaclab_arena/scripts/generate_sensitivity_report.py @@ -3,21 +3,6 @@ # # SPDX-License-Identifier: Apache-2.0 -"""CLI driver: build a single-PDF sensitivity report from a (factors.yaml, JSONL) pair. - -Thin wrapper around :func:`isaaclab_arena.analysis.sensitivity.pdf_report.generate_pdf_report`. -Produces one PDF with an outcome × factor grid of marginal-posterior plots — the most -important plots in a single file. For per-plot inspection of a single factor/outcome, use -``analyze_sensitivity.py`` instead. - -Example:: - - python -m isaaclab_arena.scripts.generate_sensitivity_report \\ - --factors_yaml path/to/factors.yaml \\ - --episode_summary path/to/episode_summary.jsonl \\ - --output_pdf /tmp/sensitivity_report.pdf -""" - from __future__ import annotations import argparse @@ -26,7 +11,12 @@ def main(): - parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser = argparse.ArgumentParser( + description=( + "Build a single-PDF sensitivity report (outcome × factor grid of " + "marginal-posterior plots) from a (factors.yaml, episode_summary.jsonl) pair." + ) + ) parser.add_argument("--factors_yaml", type=str, required=True, help="Path to factors.yaml.") parser.add_argument( "--episode_summary", type=str, required=True, help="Path to episode_summary.jsonl produced by eval_runner." From 3e67c1cbb7419a1709d90efdab422f346f96bb4a Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 14:16:38 +0200 Subject: [PATCH 12/74] Add a shared EmpiricalAnalyzer base for the KDE and frequency-table analyzers - Make `EmpiricalAnalyzer` an abstract base for the two direct (non-neural) analyzers, which estimate the posterior straight from data under a uniform prior. Rename the concrete categorical analyzer to `FrequencyTableAnalyzer` and reparent `KDEAnalyzer` beneath the same base. Both now share a named `SUCCESS_THRESHOLD` and a `_success_mask()` helper instead of inlining the `>= 0.5` success test. - Update `make_analyzer` dispatch and the plotting/docstring references; fix a stale claim that only `PosteriorAnalyzer` provides `continuous_marginal_density` (KDEAnalyzer does too). - Drop `v0.3`/`MVP-1` wording from the analyzer docstrings, keeping the substantive uniform-prior and binary-outcome assumptions. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/analyzer.py | 77 ++++++++++++------- .../analysis/sensitivity/plotting.py | 6 +- 2 files changed, 54 insertions(+), 29 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/analyzer.py b/isaaclab_arena/analysis/sensitivity/analyzer.py index 74c0375576..c5c965ad51 100644 --- a/isaaclab_arena/analysis/sensitivity/analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/analyzer.py @@ -18,8 +18,9 @@ class BaseAnalyzer(ABC): Subclasses must implement: - ``fit`` — train (or no-op) so queries can be called afterwards. - ``categorical_marginal_probs`` — return ``P(category | outcome)`` for a categorical factor. - Continuous-factor queries (``continuous_marginal_density``) live on ``PosteriorAnalyzer`` - only — the empirical analyzer never needs them by construction. + Continuous-factor queries (``continuous_marginal_density``) live on the analyzers that + provide them (``PosteriorAnalyzer`` and ``KDEAnalyzer``); the categorical-only analyzers + never need them by construction. """ def __init__(self, dataset: SensitivityDataset, outcome_name: str): @@ -248,9 +249,37 @@ def _inference_cls(self): class EmpiricalAnalyzer(BaseAnalyzer): + """Abstract base for the direct (non-neural) analyzers. + + Both subclasses exploit the same fact: under a uniform prior, + ``P(theta | success) ∝ P(success | theta)``, so the posterior is read directly off + the data — no neural density estimator, no parametric shape constraint. They differ + only in factor type, which dictates the estimator: + + - :class:`FrequencyTableAnalyzer` (categorical) — the per-category empirical success + rate: the raw empirical measure, usable as-is. + - :class:`KDEAnalyzer` (continuous) — a Gaussian KDE over the successful-theta + samples: the same empirical measure, kernel-smoothed because a raw continuous + empirical measure is a sum of Diracs. + + Outcome is treated as binary: an episode is a "success" when its selected outcome + column is ``>= SUCCESS_THRESHOLD``. + """ + + SUCCESS_THRESHOLD = 0.5 + """Outcome value at or above which an episode counts as a success.""" + + def _success_mask(self) -> np.ndarray: + """Boolean array over episodes: True where the selected outcome counts as a success.""" + outcome_column_index = self.dataset.outcome_columns[self.outcome_name] + outcome_values = self.dataset.x[:, outcome_column_index].cpu().numpy() + return outcome_values >= self.SUCCESS_THRESHOLD + + +class FrequencyTableAnalyzer(EmpiricalAnalyzer): """Frequency-table analyzer for pure-categorical factor schemas — no neural fit. - Use this when every declared factor is categorical. Under v0.3's uniform prior, + Use this when every declared factor is categorical. Under a uniform prior, Bayes' rule simplifies ``P(category | success) ∝ P(success | category) · P(category)`` to ``P(category | success) ∝ P(success | category)`` — i.e. the posterior is *exactly* the per-category empirical success rate, normalized to sum to 1. No neural network @@ -267,7 +296,7 @@ def __init__(self, dataset: SensitivityDataset, outcome_name: str): super().__init__(dataset, outcome_name) has_continuous_factor = any(factor.type == "continuous" for factor in dataset.schema.factors) assert not has_continuous_factor, ( - "EmpiricalAnalyzer is only valid for all-categorical schemas. For mixed" + "FrequencyTableAnalyzer is only valid for all-categorical schemas. For mixed" " continuous + categorical factors, use MNPEAnalyzer." ) @@ -278,57 +307,55 @@ def fit(self, training_batch_size: int = 50) -> None: def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: """Return ``P(category | outcome) = per_category_success_rate / sum(per_category_success_rate)``. - For each category, computes the fraction of rows assigned to it whose outcome - column is ``>= 0.5`` (treating outcome as binary). Then normalizes across - categories so the result sums to 1. ``outcome_value`` and ``num_samples`` are - accepted for interface compatibility with ``PosteriorAnalyzer`` but not used — - empirical analysis treats outcome as binary (success vs not-success). + For each category, computes the fraction of its rows that count as a success (see + ``SUCCESS_THRESHOLD``), then normalizes across categories so the result sums to 1. + ``outcome_value`` and ``num_samples`` are accepted for interface compatibility with + ``PosteriorAnalyzer`` but not used — empirical analysis treats outcome as binary. """ factor_spec = self._factor_spec(factor_name) assert factor_spec.type == "categorical" assert factor_spec.choices is not None factor_column_slice = self.dataset.factor_columns[factor_name] num_choices = len(factor_spec.choices) - outcome_column_index = self.dataset.outcome_columns[self.outcome_name] empirical_theta_codes = self.dataset.theta[:, factor_column_slice].squeeze(-1).long().cpu().numpy() - empirical_outcomes = self.dataset.x[:, outcome_column_index].cpu().numpy() + success_mask = self._success_mask() empirical_rates = np.zeros(num_choices) for code in range(num_choices): category_mask = empirical_theta_codes == code if category_mask.any(): - empirical_rates[code] = float((empirical_outcomes[category_mask] >= 0.5).mean()) + empirical_rates[code] = float(success_mask[category_mask].mean()) total_rate = float(empirical_rates.sum()) if total_rate > 0: return empirical_rates / total_rate return np.full(num_choices, 1.0 / num_choices) -class KDEAnalyzer(BaseAnalyzer): +class KDEAnalyzer(EmpiricalAnalyzer): """KDE-based analyzer for the 1-continuous-factor + binary-outcome case. - Under v0.3's uniform prior and a binary outcome, Bayes' rule reduces to + Under a uniform prior and a binary outcome, Bayes' rule reduces to ``P(theta | success=1) ∝ P(success=1 | theta) · P(theta) = P(success=1 | theta) · const``. The empirical density of *successful*-theta samples (i.e. rows where the chosen outcome is 1) is directly proportional to ``P(success=1 | theta)``, and a Gaussian KDE over those samples gives a smoothed estimate of that conditional density. No neural fit, no Gaussian-shape constraint. - This is the right primitive for our MVP-1 case (1 continuous factor, binary outcome): + This is the right primitive for the 1-continuous-factor + binary-outcome case: sbi NPE forces a Gaussian shape when theta is 1D — biasing the recovered peak toward the mean of successful-theta values rather than the true mode of the success curve. KDE has no such constraint and recovers multi-modal / plateau / skewed shapes faithfully. - Conceptual sibling of :class:`EmpiricalAnalyzer` (which does the same trick for purely - categorical theta via frequency counts). For multi-factor or non-binary-outcome - workloads, :func:`make_analyzer` dispatches to NPE/MNPE instead. + Sibling of :class:`FrequencyTableAnalyzer` under the shared :class:`EmpiricalAnalyzer` + base (which does the same trick for purely categorical theta via frequency counts). For + multi-factor or non-binary-outcome workloads, :func:`make_analyzer` dispatches to NPE/MNPE. Caveats: - Bandwidth is scipy's Scott rule default; haven't tuned for non-uniform sample distributions or sparse data. May over-smooth the empirical mode. - Only ``continuous_marginal_density`` is implemented and only for ``outcome_value >= 0.5`` (i.e. success conditioning). Failure conditioning would - require fitting a second KDE over failed-theta samples; left out for v0.3 simplicity. + require fitting a second KDE over failed-theta samples; left out for simplicity. """ def __init__(self, dataset: SensitivityDataset, outcome_name: str): @@ -347,10 +374,8 @@ def fit(self, training_batch_size: int = 50) -> None: """Fit a Gaussian KDE on the successful-theta samples (no neural network involved).""" from scipy.stats import gaussian_kde - outcome_column_index = self.dataset.outcome_columns[self.outcome_name] - outcome_values = self.dataset.x[:, outcome_column_index].cpu().numpy() theta_values = self.dataset.theta[:, 0].cpu().numpy() - success_mask = outcome_values >= 0.5 + success_mask = self._success_mask() self._num_total_samples = int(len(theta_values)) self._num_successful_samples = int(success_mask.sum()) @@ -394,7 +419,7 @@ def continuous_marginal_density( range_low, range_high = factor_spec.range[0] grid = np.linspace(range_low, range_high, num_grid_points) - if outcome_value < 0.5 or self._kde is None: + if outcome_value < self.SUCCESS_THRESHOLD or self._kde is None: uniform_density = 1.0 / max(range_high - range_low, 1e-9) return grid, np.full_like(grid, uniform_density) return grid, self._kde(grid) @@ -402,7 +427,7 @@ def continuous_marginal_density( def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: raise NotImplementedError( "KDEAnalyzer is for continuous factors only. Categorical schemas dispatch to" - " EmpiricalAnalyzer via make_analyzer." + " FrequencyTableAnalyzer via make_analyzer." ) @@ -413,7 +438,7 @@ def make_analyzer(dataset: SensitivityDataset, outcome_name: str) -> BaseAnalyze - 1 continuous + 0 categorical AND the outcome is binary → :class:`KDEAnalyzer` (avoids sbi NPE's 1D-theta Gaussian-shape constraint; exact under uniform prior) - any continuous + any categorical → :class:`MNPEAnalyzer` - - all categorical (zero continuous) → :class:`EmpiricalAnalyzer` + - all categorical (zero continuous) → :class:`FrequencyTableAnalyzer` - all continuous (zero categorical) → :class:`NPEAnalyzer` (the multi-continuous-factor case; theta is multi-D so no Gaussian fallback) @@ -435,5 +460,5 @@ def make_analyzer(dataset: SensitivityDataset, outcome_name: str) -> BaseAnalyze if num_continuous_factors > 0 and num_categorical_factors > 0: return MNPEAnalyzer(dataset, outcome_name) if num_categorical_factors > 0: - return EmpiricalAnalyzer(dataset, outcome_name) + return FrequencyTableAnalyzer(dataset, outcome_name) return NPEAnalyzer(dataset, outcome_name) diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index 9f28cd1313..0d841b1e94 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -28,8 +28,8 @@ def draw_marginal( Axes (a standalone plot wants the full slice block; a grid cell wants a compact label). For continuous factors, the analyzer must expose ``continuous_marginal_density`` - (only ``PosteriorAnalyzer`` does — ``EmpiricalAnalyzer`` rejects continuous factors at - construction time, so this branch isn't reachable through ``make_analyzer``). + (``PosteriorAnalyzer`` and ``KDEAnalyzer`` do — ``FrequencyTableAnalyzer`` rejects + continuous factors at construction time, so it never reaches this branch). """ factor_spec = analyzer._factor_spec(factor_name) if factor_spec.type == "continuous": @@ -149,7 +149,7 @@ def _draw_categorical_marginal( The blue bar (left of each category) is the analyzer's ``P(category | outcome)``. The green bar (right of each category) is the *empirical* per-category outcome rate — independent of the analyzer's posterior, computed directly from the raw data. - For the ``EmpiricalAnalyzer`` the two will agree exactly (up to normalization); for + For the ``FrequencyTableAnalyzer`` the two will agree exactly (up to normalization); for a posterior-based analyzer they may differ slightly if the model smooths. Each green bar is annotated with the sample count ``n`` for that category, so the From 6699cf71d66681b6fd411d16776817bef2700892 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 14:48:46 +0200 Subject: [PATCH 13/74] Split analyzer module into base/posterior/empirical + factory - Break the monolithic analyzer.py into analyzer_base, posterior_analyzer, and empirical_analyzer modules along the neural/empirical family seam. - Move the make_analyzer dispatch into factory.py, re-exported from the package __init__; lazy concrete imports keep package import free of torch/sbi so the eval-time episode_writer path stays light. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/__init__.py | 4 + .../analysis/sensitivity/analyzer.py | 464 ------------------ .../analysis/sensitivity/analyzer_base.py | 58 +++ .../sensitivity/empirical_analyzer.py | 194 ++++++++ .../analysis/sensitivity/factory.py | 52 ++ .../analysis/sensitivity/pdf_report.py | 2 +- .../analysis/sensitivity/plotting.py | 2 +- .../sensitivity/posterior_analyzer.py | 201 ++++++++ isaaclab_arena/scripts/analyze_sensitivity.py | 2 +- 9 files changed, 512 insertions(+), 467 deletions(-) delete mode 100644 isaaclab_arena/analysis/sensitivity/analyzer.py create mode 100644 isaaclab_arena/analysis/sensitivity/analyzer_base.py create mode 100644 isaaclab_arena/analysis/sensitivity/empirical_analyzer.py create mode 100644 isaaclab_arena/analysis/sensitivity/factory.py create mode 100644 isaaclab_arena/analysis/sensitivity/posterior_analyzer.py diff --git a/isaaclab_arena/analysis/sensitivity/__init__.py b/isaaclab_arena/analysis/sensitivity/__init__.py index fee3a6a9f6..354dc638bb 100644 --- a/isaaclab_arena/analysis/sensitivity/__init__.py +++ b/isaaclab_arena/analysis/sensitivity/__init__.py @@ -2,3 +2,7 @@ # All rights reserved. # # SPDX-License-Identifier: Apache-2.0 + +from isaaclab_arena.analysis.sensitivity.factory import make_analyzer + +__all__ = ["make_analyzer"] diff --git a/isaaclab_arena/analysis/sensitivity/analyzer.py b/isaaclab_arena/analysis/sensitivity/analyzer.py deleted file mode 100644 index c5c965ad51..0000000000 --- a/isaaclab_arena/analysis/sensitivity/analyzer.py +++ /dev/null @@ -1,464 +0,0 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -from __future__ import annotations - -import numpy as np -import torch -from abc import ABC, abstractmethod - -from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec, SensitivityDataset - - -class BaseAnalyzer(ABC): - """Abstract base — owns state validation and the abstract posterior-query surface. - - Subclasses must implement: - - ``fit`` — train (or no-op) so queries can be called afterwards. - - ``categorical_marginal_probs`` — return ``P(category | outcome)`` for a categorical factor. - Continuous-factor queries (``continuous_marginal_density``) live on the analyzers that - provide them (``PosteriorAnalyzer`` and ``KDEAnalyzer``); the categorical-only analyzers - never need them by construction. - """ - - def __init__(self, dataset: SensitivityDataset, outcome_name: str): - self.dataset = dataset - self.outcome_name = outcome_name - assert ( - outcome_name in dataset.outcome_columns - ), f"Outcome {outcome_name!r} not found in schema; available: {list(dataset.outcome_columns)}" - assert len(dataset.schema.factors) > 0, "Schema declares no factors" - - @abstractmethod - def fit(self, training_batch_size: int = 50) -> None: - """Train the posterior (or no-op for empirical) so queries can be called afterwards. - - For NPE/MNPE this trains a neural density estimator on ``(theta, x_selected)``, - where ``x_selected`` is the single outcome column named by ``outcome_name``. For - the empirical analyzer this is a no-op — the categorical posterior is computed - directly from the data at query time. - """ - - @abstractmethod - def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: - """Return ``P(category | outcome=outcome_value)`` for one categorical factor. - - Output is a 1D numpy array of length ``len(factor.choices)`` whose entries sum to 1. - For posterior analyzers this is computed by sampling the trained posterior and - counting category frequencies; for the empirical analyzer it's the normalized - per-category empirical success rate. - """ - - def _factor_spec(self, factor_name: str) -> FactorSpec: - """Return the ``FactorSpec`` for ``factor_name``, asserting it exists in the schema.""" - assert ( - factor_name in self.dataset.factor_columns - ), f"Factor {factor_name!r} not in schema; available: {list(self.dataset.factor_columns)}" - return next(factor for factor in self.dataset.schema.factors if factor.name == factor_name) - - -class PosteriorAnalyzer(BaseAnalyzer): - """Common base for the sbi-driven analyzers (NPE and MNPE). - - NPE and MNPE differ only in *which* sbi inference class they instantiate; everything - else (training loop, posterior storage, density and sample queries) is identical. - Subclasses override ``_make_inference`` to choose the class, and the - binary-outcome WARN hook to surface any method-specific caveats. - - After ``fit()`` returns, ``self.posterior`` is an sbi posterior object that supports - ``posterior.sample(shape, x=...)`` and (for NPE) ``posterior.log_prob(theta, x=...)``. - """ - - def __init__(self, dataset: SensitivityDataset, outcome_name: str): - super().__init__(dataset, outcome_name) - self.posterior = None - - def _inference_cls(self): - """Return the sbi inference *class* to train with (e.g. ``sbi.inference.NPE``). - - Subclass-specific: ``NPEAnalyzer`` returns ``NPE``, ``MNPEAnalyzer`` returns - ``MNPE``. The lazy import of sbi lives in the subclass so callers don't pay the - (heavy) sbi import cost until they actually fit. - """ - raise NotImplementedError("PosteriorAnalyzer subclasses must implement _inference_cls") - - def _make_inference(self): - """Instantiate the chosen sbi inference class on the dataset's uniform prior.""" - return self._inference_cls()(prior=self.dataset.prior) - - def fit(self, training_batch_size: int = 50) -> None: - """Train the chosen sbi estimator on ``(theta, x_selected)`` and stash the posterior. - - Steps: - 1. Slice ``self.dataset.x`` to the single outcome column named by ``outcome_name``. - 2. Surface any method-specific caveats about the outcome (e.g. NPE's - 1D-theta Gaussian fallback) via ``_maybe_warn_binary_outcome``. - 3. Instantiate the sbi inference object (NPE or MNPE) via ``_make_inference``. - 4. Append the simulations and train. - 5. Build a posterior object from the trained estimator and store it on ``self``. - """ - outcome_column_index = self.dataset.outcome_columns[self.outcome_name] - selected_outcome_column = self.dataset.x[:, outcome_column_index : outcome_column_index + 1] - self._maybe_warn_binary_outcome(selected_outcome_column) - - print( - f"[INFO] {type(self).__name__}: fitting on {self.dataset.theta.shape[0]} samples" - f" (theta dim={self.dataset.theta.shape[1]}," - f" x dim={selected_outcome_column.shape[1]})." - ) - inference = self._make_inference() - inference.append_simulations(self.dataset.theta, selected_outcome_column) - density_estimator = inference.train(training_batch_size=training_batch_size) - self.posterior = inference.build_posterior(density_estimator) - - def _maybe_warn_binary_outcome(self, selected_outcome_column: torch.Tensor) -> None: - """Optional hook for subclass-specific caveats about the outcome. Default: no-op. - - ``NPEAnalyzer`` overrides this to warn that sbi falls back to a Gaussian density - when *theta* is 1D, biasing the recovered peak toward the mean of successful - theta values rather than the true mode. The fallback fires regardless of how - many outcome columns are logged — it's a property of single-factor analysis. - For 1-continuous-factor + binary-outcome workloads, prefer ``KDEAnalyzer``. - """ - - def continuous_marginal_density( - self, factor_name: str, outcome_value: float, num_grid_points: int - ) -> tuple[np.ndarray, np.ndarray]: - """Evaluate ``P(factor_value | outcome=outcome_value)`` over the factor's prior range. - - Returns ``(grid, density)`` as numpy arrays of length ``num_grid_points``, suitable - for plotting as a smooth curve. - - Two evaluation paths depending on whether other factors are present: - - **1D theta** (the only declared factor is this one): evaluate - ``posterior.log_prob`` directly on a regular grid — exact, no sampling. - - **Multi-dim theta**: sample the posterior at the given outcome value, extract - this factor's column, and histogram-then-interpolate to a grid. This - marginalizes over the other factor dims implicitly. - """ - assert self.posterior is not None, "Call fit() before querying the posterior" - factor_spec = self._factor_spec(factor_name) - assert ( - factor_spec.type == "continuous" - ), f"continuous_marginal_density expects a continuous factor; {factor_name!r} is {factor_spec.type!r}" - assert ( - factor_spec.range is not None and len(factor_spec.range) == 1 - ), "Continuous-factor marginal expects a populated 1D range" - - factor_column_slice = self.dataset.factor_columns[factor_name] - observed_outcome = torch.tensor([outcome_value], dtype=torch.float32) - range_low, range_high = factor_spec.range[0] - - if self.dataset.theta.shape[1] == 1: - grid_tensor = torch.linspace(range_low, range_high, num_grid_points, dtype=torch.float32).unsqueeze(1) - with torch.no_grad(): - log_probabilities = self.posterior.log_prob(grid_tensor, x=observed_outcome) - density_numpy = torch.exp(log_probabilities).cpu().numpy() - grid_numpy = grid_tensor.squeeze(-1).cpu().numpy() - else: - with torch.no_grad(): - posterior_samples = self.posterior.sample((10_000,), x=observed_outcome) - factor_column_samples = posterior_samples[:, factor_column_slice].squeeze(-1).cpu().numpy() - grid_numpy = np.linspace(range_low, range_high, num_grid_points) - histogram_density, bin_edges = np.histogram( - factor_column_samples, bins=40, range=(range_low, range_high), density=True - ) - density_numpy = np.interp(grid_numpy, 0.5 * (bin_edges[:-1] + bin_edges[1:]), histogram_density) - - return grid_numpy, density_numpy - - def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: - """Estimate ``P(category | outcome)`` by sampling the trained posterior. - - Draws ``num_samples`` from ``posterior(theta | x=outcome_value)``, extracts the - factor's column (which sbi returns as floats over the BoxUniform support), rounds - to the nearest integer in ``[0, num_choices - 1]``, and tallies frequencies. - Result is a length-``num_choices`` numpy array that sums to 1. - """ - assert self.posterior is not None, "Call fit() before querying the posterior" - factor_spec = self._factor_spec(factor_name) - assert factor_spec.type == "categorical" - assert factor_spec.choices is not None - factor_column_slice = self.dataset.factor_columns[factor_name] - num_choices = len(factor_spec.choices) - - observed_outcome = torch.tensor([outcome_value], dtype=torch.float32) - with torch.no_grad(): - posterior_samples = self.posterior.sample((num_samples,), x=observed_outcome) - factor_column_samples = posterior_samples[:, factor_column_slice].squeeze(-1).cpu().numpy() - clipped_codes = np.clip(np.round(factor_column_samples), 0, num_choices - 1).astype(int) - return np.bincount(clipped_codes, minlength=num_choices) / num_samples - - -class NPEAnalyzer(PosteriorAnalyzer): - """Neural Posterior Estimation analyzer for continuous-only factor schemas. - - Use this when every declared factor is continuous (no categoricals). Internally - trains ``sbi.inference.NPE``, which fits a normalizing-flow density over - ``(theta, x_selected)`` and exposes both ``sample`` and ``log_prob`` on the result. - - **Caveat for binary outcomes (1D x):** sbi's flow code falls back to a Gaussian - density when the output space is 1D, which biases the recovered posterior peak - toward the *mean* of successful theta values rather than the true *mode* of the - success curve. We surface a [WARN] at fit time so users see this in plain text - rather than buried in sbi's UserWarning stream. - """ - - def _inference_cls(self): - from sbi.inference import NPE - - return NPE - - def _maybe_warn_binary_outcome(self, selected_outcome_column: torch.Tensor) -> None: - """Warn if theta is 1D and the outcome is binary — the configuration that triggers - the sbi Gaussian fallback. Multi-factor theta (dim ≥ 2) escapes the fallback. - """ - if self.dataset.theta.shape[1] > 1: - return - unique_values = set(selected_outcome_column.flatten().tolist()) - if unique_values.issubset({0.0, 1.0}): - print( - f"[WARN] Theta is 1D ({self.dataset.schema.factors[0].name!r}) and outcome" - f" {self.outcome_name!r} is binary. sbi NPE falls back to a Gaussian density" - " in 1D theta space, so the recovered posterior peak reflects the *mean* of" - " successful theta values rather than the true *mode* of the success curve." - " For this configuration prefer KDEAnalyzer (uniform prior + binary outcome" - " → KDE on successful-theta samples is the correct posterior)." - ) - - -class MNPEAnalyzer(PosteriorAnalyzer): - """Mixed Neural Posterior Estimation analyzer for schemas with at least one of each type. - - Use this when the schema mixes continuous and categorical factors. Internally trains - ``sbi.inference.MNPE``, whose mixed density estimator routes continuous theta columns - through a normalizing flow while routing categorical columns through a categorical - mass estimator. The continuous-first / categorical-after column ordering in - ``factor_columns`` matches MNPE's expected layout exactly. - - sbi MNPE 0.26 requires at least one continuous theta column. For pure-categorical - schemas use ``EmpiricalAnalyzer`` instead — ``make_analyzer`` dispatches correctly. - """ - - def _inference_cls(self): - from sbi.inference import MNPE - - return MNPE - - -class EmpiricalAnalyzer(BaseAnalyzer): - """Abstract base for the direct (non-neural) analyzers. - - Both subclasses exploit the same fact: under a uniform prior, - ``P(theta | success) ∝ P(success | theta)``, so the posterior is read directly off - the data — no neural density estimator, no parametric shape constraint. They differ - only in factor type, which dictates the estimator: - - - :class:`FrequencyTableAnalyzer` (categorical) — the per-category empirical success - rate: the raw empirical measure, usable as-is. - - :class:`KDEAnalyzer` (continuous) — a Gaussian KDE over the successful-theta - samples: the same empirical measure, kernel-smoothed because a raw continuous - empirical measure is a sum of Diracs. - - Outcome is treated as binary: an episode is a "success" when its selected outcome - column is ``>= SUCCESS_THRESHOLD``. - """ - - SUCCESS_THRESHOLD = 0.5 - """Outcome value at or above which an episode counts as a success.""" - - def _success_mask(self) -> np.ndarray: - """Boolean array over episodes: True where the selected outcome counts as a success.""" - outcome_column_index = self.dataset.outcome_columns[self.outcome_name] - outcome_values = self.dataset.x[:, outcome_column_index].cpu().numpy() - return outcome_values >= self.SUCCESS_THRESHOLD - - -class FrequencyTableAnalyzer(EmpiricalAnalyzer): - """Frequency-table analyzer for pure-categorical factor schemas — no neural fit. - - Use this when every declared factor is categorical. Under a uniform prior, - Bayes' rule simplifies ``P(category | success) ∝ P(success | category) · P(category)`` - to ``P(category | success) ∝ P(success | category)`` — i.e. the posterior is *exactly* - the per-category empirical success rate, normalized to sum to 1. No neural network - can do better than this with a uniform prior; smoothing only hurts. - - Also covers a sbi limitation: MNPE 0.26 refuses to train if theta has zero continuous - columns. The empirical path sidesteps that entirely. - - Rejects continuous factors at construction time — ``make_analyzer`` shouldn't even - dispatch here for mixed schemas, but the explicit guard makes the constraint clear. - """ - - def __init__(self, dataset: SensitivityDataset, outcome_name: str): - super().__init__(dataset, outcome_name) - has_continuous_factor = any(factor.type == "continuous" for factor in dataset.schema.factors) - assert not has_continuous_factor, ( - "FrequencyTableAnalyzer is only valid for all-categorical schemas. For mixed" - " continuous + categorical factors, use MNPEAnalyzer." - ) - - def fit(self, training_batch_size: int = 50) -> None: - """No-op — the posterior is computed directly from the data at query time.""" - print(f"[INFO] {type(self).__name__}: no neural fit needed for pure-categorical schema.") - - def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: - """Return ``P(category | outcome) = per_category_success_rate / sum(per_category_success_rate)``. - - For each category, computes the fraction of its rows that count as a success (see - ``SUCCESS_THRESHOLD``), then normalizes across categories so the result sums to 1. - ``outcome_value`` and ``num_samples`` are accepted for interface compatibility with - ``PosteriorAnalyzer`` but not used — empirical analysis treats outcome as binary. - """ - factor_spec = self._factor_spec(factor_name) - assert factor_spec.type == "categorical" - assert factor_spec.choices is not None - factor_column_slice = self.dataset.factor_columns[factor_name] - num_choices = len(factor_spec.choices) - - empirical_theta_codes = self.dataset.theta[:, factor_column_slice].squeeze(-1).long().cpu().numpy() - success_mask = self._success_mask() - empirical_rates = np.zeros(num_choices) - for code in range(num_choices): - category_mask = empirical_theta_codes == code - if category_mask.any(): - empirical_rates[code] = float(success_mask[category_mask].mean()) - total_rate = float(empirical_rates.sum()) - if total_rate > 0: - return empirical_rates / total_rate - return np.full(num_choices, 1.0 / num_choices) - - -class KDEAnalyzer(EmpiricalAnalyzer): - """KDE-based analyzer for the 1-continuous-factor + binary-outcome case. - - Under a uniform prior and a binary outcome, Bayes' rule reduces to - ``P(theta | success=1) ∝ P(success=1 | theta) · P(theta) = P(success=1 | theta) · const``. - The empirical density of *successful*-theta samples (i.e. rows where the chosen outcome - is 1) is directly proportional to ``P(success=1 | theta)``, and a Gaussian KDE over - those samples gives a smoothed estimate of that conditional density. No neural fit, - no Gaussian-shape constraint. - - This is the right primitive for the 1-continuous-factor + binary-outcome case: - sbi NPE forces a Gaussian shape when theta is 1D — biasing the recovered peak toward - the mean of successful-theta values rather than the true mode of the success curve. - KDE has no such constraint and recovers multi-modal / plateau / skewed shapes faithfully. - - Sibling of :class:`FrequencyTableAnalyzer` under the shared :class:`EmpiricalAnalyzer` - base (which does the same trick for purely categorical theta via frequency counts). For - multi-factor or non-binary-outcome workloads, :func:`make_analyzer` dispatches to NPE/MNPE. - - Caveats: - - Bandwidth is scipy's Scott rule default; haven't tuned for non-uniform sample - distributions or sparse data. May over-smooth the empirical mode. - - Only ``continuous_marginal_density`` is implemented and only for - ``outcome_value >= 0.5`` (i.e. success conditioning). Failure conditioning would - require fitting a second KDE over failed-theta samples; left out for simplicity. - """ - - def __init__(self, dataset: SensitivityDataset, outcome_name: str): - super().__init__(dataset, outcome_name) - num_continuous = sum(1 for factor in dataset.schema.factors if factor.type == "continuous") - num_categorical = sum(1 for factor in dataset.schema.factors if factor.type == "categorical") - assert num_continuous == 1 and num_categorical == 0, ( - f"KDEAnalyzer requires exactly one continuous factor and no categoricals; got {num_continuous} continuous," - f" {num_categorical} categorical." - ) - self._kde = None - self._num_successful_samples = 0 - self._num_total_samples = 0 - - def fit(self, training_batch_size: int = 50) -> None: - """Fit a Gaussian KDE on the successful-theta samples (no neural network involved).""" - from scipy.stats import gaussian_kde - - theta_values = self.dataset.theta[:, 0].cpu().numpy() - success_mask = self._success_mask() - self._num_total_samples = int(len(theta_values)) - self._num_successful_samples = int(success_mask.sum()) - - if self._num_successful_samples < 2: - print( - f"[WARN] KDEAnalyzer: only {self._num_successful_samples} successful samples" - f" / {self._num_total_samples} total — KDE undefined, marginal will be uniform." - ) - return - - successful_theta = theta_values[success_mask] - if float(np.std(successful_theta)) < 1e-9: - print( - "[WARN] KDEAnalyzer: all successful theta values are identical — KDE bandwidth" - " degenerate, marginal will be uniform." - ) - return - - self._kde = gaussian_kde(successful_theta) - print( - f"[INFO] KDEAnalyzer: fit Gaussian KDE on {self._num_successful_samples} successful" - f" theta samples / {self._num_total_samples} total." - ) - - def continuous_marginal_density( - self, factor_name: str, outcome_value: float, num_grid_points: int - ) -> tuple[np.ndarray, np.ndarray]: - """Evaluate the KDE-based posterior over the factor's prior range. - - ``outcome_value >= 0.5`` is treated as "success conditioning" (the only case - currently supported); the KDE is evaluated on a uniform grid spanning the - declared factor range. ``outcome_value < 0.5`` (failure conditioning) returns - a uniform density as a placeholder — extend by fitting a second KDE on failed - samples if/when that case is needed. - """ - factor_spec = self._factor_spec(factor_name) - assert factor_spec.type == "continuous", "KDEAnalyzer only handles continuous factors" - assert ( - factor_spec.range is not None and len(factor_spec.range) == 1 - ), "Continuous-factor marginal expects a populated 1D range" - range_low, range_high = factor_spec.range[0] - grid = np.linspace(range_low, range_high, num_grid_points) - - if outcome_value < self.SUCCESS_THRESHOLD or self._kde is None: - uniform_density = 1.0 / max(range_high - range_low, 1e-9) - return grid, np.full_like(grid, uniform_density) - return grid, self._kde(grid) - - def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: - raise NotImplementedError( - "KDEAnalyzer is for continuous factors only. Categorical schemas dispatch to" - " FrequencyTableAnalyzer via make_analyzer." - ) - - -def make_analyzer(dataset: SensitivityDataset, outcome_name: str) -> BaseAnalyzer: - """Construct the right analyzer for the dataset's factor mix and outcome shape. - - Dispatch table (checked top-to-bottom): - - 1 continuous + 0 categorical AND the outcome is binary → :class:`KDEAnalyzer` - (avoids sbi NPE's 1D-theta Gaussian-shape constraint; exact under uniform prior) - - any continuous + any categorical → :class:`MNPEAnalyzer` - - all categorical (zero continuous) → :class:`FrequencyTableAnalyzer` - - all continuous (zero categorical) → :class:`NPEAnalyzer` - (the multi-continuous-factor case; theta is multi-D so no Gaussian fallback) - - The KDE branch checks the outcome column's binary-ness on the dataset itself rather - than the schema, since outcome ``type: float`` in factors.yaml covers both continuous - durations and binary 0/1 success rates. - """ - num_continuous_factors = sum(1 for factor in dataset.schema.factors if factor.type == "continuous") - num_categorical_factors = sum(1 for factor in dataset.schema.factors if factor.type == "categorical") - assert num_continuous_factors + num_categorical_factors > 0, "Schema declares no factors" - - if num_continuous_factors == 1 and num_categorical_factors == 0: - outcome_column_index = dataset.outcome_columns[outcome_name] - outcome_values = dataset.x[:, outcome_column_index] - unique_outcome_values = set(outcome_values.flatten().tolist()) - if unique_outcome_values.issubset({0.0, 1.0}): - return KDEAnalyzer(dataset, outcome_name) - - if num_continuous_factors > 0 and num_categorical_factors > 0: - return MNPEAnalyzer(dataset, outcome_name) - if num_categorical_factors > 0: - return FrequencyTableAnalyzer(dataset, outcome_name) - return NPEAnalyzer(dataset, outcome_name) diff --git a/isaaclab_arena/analysis/sensitivity/analyzer_base.py b/isaaclab_arena/analysis/sensitivity/analyzer_base.py new file mode 100644 index 0000000000..a23a86c536 --- /dev/null +++ b/isaaclab_arena/analysis/sensitivity/analyzer_base.py @@ -0,0 +1,58 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +import numpy as np +from abc import ABC, abstractmethod + +from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec, SensitivityDataset + + +class BaseAnalyzer(ABC): + """Abstract base — owns state validation and the abstract posterior-query surface. + + Subclasses must implement: + - ``fit`` — train (or no-op) so queries can be called afterwards. + - ``categorical_marginal_probs`` — return ``P(category | outcome)`` for a categorical factor. + Continuous-factor queries (``continuous_marginal_density``) live on the analyzers that + provide them (``PosteriorAnalyzer`` and ``KDEAnalyzer``); the categorical-only analyzers + never need them by construction. + """ + + def __init__(self, dataset: SensitivityDataset, outcome_name: str): + self.dataset = dataset + self.outcome_name = outcome_name + assert ( + outcome_name in dataset.outcome_columns + ), f"Outcome {outcome_name!r} not found in schema; available: {list(dataset.outcome_columns)}" + assert len(dataset.schema.factors) > 0, "Schema declares no factors" + + @abstractmethod + def fit(self, training_batch_size: int = 50) -> None: + """Train the posterior (or no-op for empirical) so queries can be called afterwards. + + For NPE/MNPE this trains a neural density estimator on ``(theta, x_selected)``, + where ``x_selected`` is the single outcome column named by ``outcome_name``. For + the empirical analyzer this is a no-op — the categorical posterior is computed + directly from the data at query time. + """ + + @abstractmethod + def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: + """Return ``P(category | outcome=outcome_value)`` for one categorical factor. + + Output is a 1D numpy array of length ``len(factor.choices)`` whose entries sum to 1. + For posterior analyzers this is computed by sampling the trained posterior and + counting category frequencies; for the empirical analyzer it's the normalized + per-category empirical success rate. + """ + + def _factor_spec(self, factor_name: str) -> FactorSpec: + """Return the ``FactorSpec`` for ``factor_name``, asserting it exists in the schema.""" + assert ( + factor_name in self.dataset.factor_columns + ), f"Factor {factor_name!r} not in schema; available: {list(self.dataset.factor_columns)}" + return next(factor for factor in self.dataset.schema.factors if factor.name == factor_name) diff --git a/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py b/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py new file mode 100644 index 0000000000..622f89120b --- /dev/null +++ b/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py @@ -0,0 +1,194 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +import numpy as np + +from isaaclab_arena.analysis.sensitivity.analyzer_base import BaseAnalyzer +from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset + + +class EmpiricalAnalyzer(BaseAnalyzer): + """Abstract base for the direct (non-neural) analyzers. + + Both subclasses exploit the same fact: under a uniform prior, + ``P(theta | success) ∝ P(success | theta)``, so the posterior is read directly off + the data — no neural density estimator, no parametric shape constraint. They differ + only in factor type, which dictates the estimator: + + - :class:`FrequencyTableAnalyzer` (categorical) — the per-category empirical success + rate: the raw empirical measure, usable as-is. + - :class:`KDEAnalyzer` (continuous) — a Gaussian KDE over the successful-theta + samples: the same empirical measure, kernel-smoothed because a raw continuous + empirical measure is a sum of Diracs. + + Outcome is treated as binary: an episode is a "success" when its selected outcome + column is ``>= SUCCESS_THRESHOLD``. + """ + + SUCCESS_THRESHOLD = 0.5 + """Outcome value at or above which an episode counts as a success.""" + + def _success_mask(self) -> np.ndarray: + """Boolean array over episodes: True where the selected outcome counts as a success.""" + outcome_column_index = self.dataset.outcome_columns[self.outcome_name] + outcome_values = self.dataset.x[:, outcome_column_index].cpu().numpy() + return outcome_values >= self.SUCCESS_THRESHOLD + + +class FrequencyTableAnalyzer(EmpiricalAnalyzer): + """Frequency-table analyzer for pure-categorical factor schemas — no neural fit. + + Use this when every declared factor is categorical. Under a uniform prior, + Bayes' rule simplifies ``P(category | success) ∝ P(success | category) · P(category)`` + to ``P(category | success) ∝ P(success | category)`` — i.e. the posterior is *exactly* + the per-category empirical success rate, normalized to sum to 1. No neural network + can do better than this with a uniform prior; smoothing only hurts. + + Also covers a sbi limitation: MNPE 0.26 refuses to train if theta has zero continuous + columns. The empirical path sidesteps that entirely. + + Rejects continuous factors at construction time — ``make_analyzer`` shouldn't even + dispatch here for mixed schemas, but the explicit guard makes the constraint clear. + """ + + def __init__(self, dataset: SensitivityDataset, outcome_name: str): + super().__init__(dataset, outcome_name) + has_continuous_factor = any(factor.type == "continuous" for factor in dataset.schema.factors) + assert not has_continuous_factor, ( + "FrequencyTableAnalyzer is only valid for all-categorical schemas. For mixed" + " continuous + categorical factors, use MNPEAnalyzer." + ) + + def fit(self, training_batch_size: int = 50) -> None: + """No-op — the posterior is computed directly from the data at query time.""" + print(f"[INFO] {type(self).__name__}: no neural fit needed for pure-categorical schema.") + + def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: + """Return ``P(category | outcome) = per_category_success_rate / sum(per_category_success_rate)``. + + For each category, computes the fraction of its rows that count as a success (see + ``SUCCESS_THRESHOLD``), then normalizes across categories so the result sums to 1. + ``outcome_value`` and ``num_samples`` are accepted for interface compatibility with + ``PosteriorAnalyzer`` but not used — empirical analysis treats outcome as binary. + """ + factor_spec = self._factor_spec(factor_name) + assert factor_spec.type == "categorical" + assert factor_spec.choices is not None + factor_column_slice = self.dataset.factor_columns[factor_name] + num_choices = len(factor_spec.choices) + + empirical_theta_codes = self.dataset.theta[:, factor_column_slice].squeeze(-1).long().cpu().numpy() + success_mask = self._success_mask() + empirical_rates = np.zeros(num_choices) + for code in range(num_choices): + category_mask = empirical_theta_codes == code + if category_mask.any(): + empirical_rates[code] = float(success_mask[category_mask].mean()) + total_rate = float(empirical_rates.sum()) + if total_rate > 0: + return empirical_rates / total_rate + return np.full(num_choices, 1.0 / num_choices) + + +class KDEAnalyzer(EmpiricalAnalyzer): + """KDE-based analyzer for the 1-continuous-factor + binary-outcome case. + + Under a uniform prior and a binary outcome, Bayes' rule reduces to + ``P(theta | success=1) ∝ P(success=1 | theta) · P(theta) = P(success=1 | theta) · const``. + The empirical density of *successful*-theta samples (i.e. rows where the chosen outcome + is 1) is directly proportional to ``P(success=1 | theta)``, and a Gaussian KDE over + those samples gives a smoothed estimate of that conditional density. No neural fit, + no Gaussian-shape constraint. + + This is the right primitive for the 1-continuous-factor + binary-outcome case: + sbi NPE forces a Gaussian shape when theta is 1D — biasing the recovered peak toward + the mean of successful-theta values rather than the true mode of the success curve. + KDE has no such constraint and recovers multi-modal / plateau / skewed shapes faithfully. + + Sibling of :class:`FrequencyTableAnalyzer` under the shared :class:`EmpiricalAnalyzer` + base (which does the same trick for purely categorical theta via frequency counts). For + multi-factor or non-binary-outcome workloads, :func:`make_analyzer` dispatches to NPE/MNPE. + + Caveats: + - Bandwidth is scipy's Scott rule default; haven't tuned for non-uniform sample + distributions or sparse data. May over-smooth the empirical mode. + - Only ``continuous_marginal_density`` is implemented and only for + ``outcome_value >= 0.5`` (i.e. success conditioning). Failure conditioning would + require fitting a second KDE over failed-theta samples; left out for simplicity. + """ + + def __init__(self, dataset: SensitivityDataset, outcome_name: str): + super().__init__(dataset, outcome_name) + num_continuous = sum(1 for factor in dataset.schema.factors if factor.type == "continuous") + num_categorical = sum(1 for factor in dataset.schema.factors if factor.type == "categorical") + assert num_continuous == 1 and num_categorical == 0, ( + f"KDEAnalyzer requires exactly one continuous factor and no categoricals; got {num_continuous} continuous," + f" {num_categorical} categorical." + ) + self._kde = None + self._num_successful_samples = 0 + self._num_total_samples = 0 + + def fit(self, training_batch_size: int = 50) -> None: + """Fit a Gaussian KDE on the successful-theta samples (no neural network involved).""" + from scipy.stats import gaussian_kde + + theta_values = self.dataset.theta[:, 0].cpu().numpy() + success_mask = self._success_mask() + self._num_total_samples = int(len(theta_values)) + self._num_successful_samples = int(success_mask.sum()) + + if self._num_successful_samples < 2: + print( + f"[WARN] KDEAnalyzer: only {self._num_successful_samples} successful samples" + f" / {self._num_total_samples} total — KDE undefined, marginal will be uniform." + ) + return + + successful_theta = theta_values[success_mask] + if float(np.std(successful_theta)) < 1e-9: + print( + "[WARN] KDEAnalyzer: all successful theta values are identical — KDE bandwidth" + " degenerate, marginal will be uniform." + ) + return + + self._kde = gaussian_kde(successful_theta) + print( + f"[INFO] KDEAnalyzer: fit Gaussian KDE on {self._num_successful_samples} successful" + f" theta samples / {self._num_total_samples} total." + ) + + def continuous_marginal_density( + self, factor_name: str, outcome_value: float, num_grid_points: int + ) -> tuple[np.ndarray, np.ndarray]: + """Evaluate the KDE-based posterior over the factor's prior range. + + ``outcome_value >= 0.5`` is treated as "success conditioning" (the only case + currently supported); the KDE is evaluated on a uniform grid spanning the + declared factor range. ``outcome_value < 0.5`` (failure conditioning) returns + a uniform density as a placeholder — extend by fitting a second KDE on failed + samples if/when that case is needed. + """ + factor_spec = self._factor_spec(factor_name) + assert factor_spec.type == "continuous", "KDEAnalyzer only handles continuous factors" + assert ( + factor_spec.range is not None and len(factor_spec.range) == 1 + ), "Continuous-factor marginal expects a populated 1D range" + range_low, range_high = factor_spec.range[0] + grid = np.linspace(range_low, range_high, num_grid_points) + + if outcome_value < self.SUCCESS_THRESHOLD or self._kde is None: + uniform_density = 1.0 / max(range_high - range_low, 1e-9) + return grid, np.full_like(grid, uniform_density) + return grid, self._kde(grid) + + def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: + raise NotImplementedError( + "KDEAnalyzer is for continuous factors only. Categorical schemas dispatch to" + " FrequencyTableAnalyzer via make_analyzer." + ) diff --git a/isaaclab_arena/analysis/sensitivity/factory.py b/isaaclab_arena/analysis/sensitivity/factory.py new file mode 100644 index 0000000000..11cc6fc083 --- /dev/null +++ b/isaaclab_arena/analysis/sensitivity/factory.py @@ -0,0 +1,52 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from isaaclab_arena.analysis.sensitivity.analyzer_base import BaseAnalyzer + from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset + + +def make_analyzer(dataset: SensitivityDataset, outcome_name: str) -> BaseAnalyzer: + """Construct the right analyzer for the dataset's factor mix and outcome shape. + + Dispatch table (checked top-to-bottom): + - 1 continuous + 0 categorical AND the outcome is binary → :class:`KDEAnalyzer` + (avoids sbi NPE's 1D-theta Gaussian-shape constraint; exact under uniform prior) + - any continuous + any categorical → :class:`MNPEAnalyzer` + - all categorical (zero continuous) → :class:`FrequencyTableAnalyzer` + - all continuous (zero categorical) → :class:`NPEAnalyzer` + (the multi-continuous-factor case; theta is multi-D so no Gaussian fallback) + + The KDE branch checks the outcome column's binary-ness on the dataset itself rather + than the schema, since outcome ``type: float`` in factors.yaml covers both continuous + durations and binary 0/1 success rates. + + Analyzer classes are imported lazily so that importing this module (and the package + that re-exports it, which happens on the eval-time ``episode_writer`` path) doesn't + pull in torch/sbi until analysis runs. + """ + from isaaclab_arena.analysis.sensitivity.empirical_analyzer import FrequencyTableAnalyzer, KDEAnalyzer + from isaaclab_arena.analysis.sensitivity.posterior_analyzer import MNPEAnalyzer, NPEAnalyzer + + num_continuous_factors = sum(1 for factor in dataset.schema.factors if factor.type == "continuous") + num_categorical_factors = sum(1 for factor in dataset.schema.factors if factor.type == "categorical") + assert num_continuous_factors + num_categorical_factors > 0, "Schema declares no factors" + + if num_continuous_factors == 1 and num_categorical_factors == 0: + outcome_column_index = dataset.outcome_columns[outcome_name] + outcome_values = dataset.x[:, outcome_column_index] + unique_outcome_values = set(outcome_values.flatten().tolist()) + if unique_outcome_values.issubset({0.0, 1.0}): + return KDEAnalyzer(dataset, outcome_name) + + if num_continuous_factors > 0 and num_categorical_factors > 0: + return MNPEAnalyzer(dataset, outcome_name) + if num_categorical_factors > 0: + return FrequencyTableAnalyzer(dataset, outcome_name) + return NPEAnalyzer(dataset, outcome_name) diff --git a/isaaclab_arena/analysis/sensitivity/pdf_report.py b/isaaclab_arena/analysis/sensitivity/pdf_report.py index 92493d7a4e..a863f93d30 100644 --- a/isaaclab_arena/analysis/sensitivity/pdf_report.py +++ b/isaaclab_arena/analysis/sensitivity/pdf_report.py @@ -8,7 +8,7 @@ import numpy as np from pathlib import Path -from isaaclab_arena.analysis.sensitivity.analyzer import make_analyzer +from isaaclab_arena.analysis.sensitivity import make_analyzer from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset from isaaclab_arena.analysis.sensitivity.plotting import draw_marginal diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index 0d841b1e94..c76d195b60 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -10,7 +10,7 @@ from typing import TYPE_CHECKING if TYPE_CHECKING: - from isaaclab_arena.analysis.sensitivity.analyzer import BaseAnalyzer + from isaaclab_arena.analysis.sensitivity.analyzer_base import BaseAnalyzer from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec diff --git a/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py b/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py new file mode 100644 index 0000000000..9510daad68 --- /dev/null +++ b/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py @@ -0,0 +1,201 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +import numpy as np +import torch + +from isaaclab_arena.analysis.sensitivity.analyzer_base import BaseAnalyzer +from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset + + +class PosteriorAnalyzer(BaseAnalyzer): + """Common base for the sbi-driven analyzers (NPE and MNPE). + + NPE and MNPE differ only in *which* sbi inference class they instantiate; everything + else (training loop, posterior storage, density and sample queries) is identical. + Subclasses override ``_make_inference`` to choose the class, and the + binary-outcome WARN hook to surface any method-specific caveats. + + After ``fit()`` returns, ``self.posterior`` is an sbi posterior object that supports + ``posterior.sample(shape, x=...)`` and (for NPE) ``posterior.log_prob(theta, x=...)``. + """ + + def __init__(self, dataset: SensitivityDataset, outcome_name: str): + super().__init__(dataset, outcome_name) + self.posterior = None + + def _inference_cls(self): + """Return the sbi inference *class* to train with (e.g. ``sbi.inference.NPE``). + + Subclass-specific: ``NPEAnalyzer`` returns ``NPE``, ``MNPEAnalyzer`` returns + ``MNPE``. The lazy import of sbi lives in the subclass so callers don't pay the + (heavy) sbi import cost until they actually fit. + """ + raise NotImplementedError("PosteriorAnalyzer subclasses must implement _inference_cls") + + def _make_inference(self): + """Instantiate the chosen sbi inference class on the dataset's uniform prior.""" + return self._inference_cls()(prior=self.dataset.prior) + + def fit(self, training_batch_size: int = 50) -> None: + """Train the chosen sbi estimator on ``(theta, x_selected)`` and stash the posterior. + + Steps: + 1. Slice ``self.dataset.x`` to the single outcome column named by ``outcome_name``. + 2. Surface any method-specific caveats about the outcome (e.g. NPE's + 1D-theta Gaussian fallback) via ``_maybe_warn_binary_outcome``. + 3. Instantiate the sbi inference object (NPE or MNPE) via ``_make_inference``. + 4. Append the simulations and train. + 5. Build a posterior object from the trained estimator and store it on ``self``. + """ + outcome_column_index = self.dataset.outcome_columns[self.outcome_name] + selected_outcome_column = self.dataset.x[:, outcome_column_index : outcome_column_index + 1] + self._maybe_warn_binary_outcome(selected_outcome_column) + + print( + f"[INFO] {type(self).__name__}: fitting on {self.dataset.theta.shape[0]} samples" + f" (theta dim={self.dataset.theta.shape[1]}," + f" x dim={selected_outcome_column.shape[1]})." + ) + inference = self._make_inference() + inference.append_simulations(self.dataset.theta, selected_outcome_column) + density_estimator = inference.train(training_batch_size=training_batch_size) + self.posterior = inference.build_posterior(density_estimator) + + def _maybe_warn_binary_outcome(self, selected_outcome_column: torch.Tensor) -> None: + """Optional hook for subclass-specific caveats about the outcome. Default: no-op. + + ``NPEAnalyzer`` overrides this to warn that sbi falls back to a Gaussian density + when *theta* is 1D, biasing the recovered peak toward the mean of successful + theta values rather than the true mode. The fallback fires regardless of how + many outcome columns are logged — it's a property of single-factor analysis. + For 1-continuous-factor + binary-outcome workloads, prefer ``KDEAnalyzer``. + """ + + def continuous_marginal_density( + self, factor_name: str, outcome_value: float, num_grid_points: int + ) -> tuple[np.ndarray, np.ndarray]: + """Evaluate ``P(factor_value | outcome=outcome_value)`` over the factor's prior range. + + Returns ``(grid, density)`` as numpy arrays of length ``num_grid_points``, suitable + for plotting as a smooth curve. + + Two evaluation paths depending on whether other factors are present: + - **1D theta** (the only declared factor is this one): evaluate + ``posterior.log_prob`` directly on a regular grid — exact, no sampling. + - **Multi-dim theta**: sample the posterior at the given outcome value, extract + this factor's column, and histogram-then-interpolate to a grid. This + marginalizes over the other factor dims implicitly. + """ + assert self.posterior is not None, "Call fit() before querying the posterior" + factor_spec = self._factor_spec(factor_name) + assert ( + factor_spec.type == "continuous" + ), f"continuous_marginal_density expects a continuous factor; {factor_name!r} is {factor_spec.type!r}" + assert ( + factor_spec.range is not None and len(factor_spec.range) == 1 + ), "Continuous-factor marginal expects a populated 1D range" + + factor_column_slice = self.dataset.factor_columns[factor_name] + observed_outcome = torch.tensor([outcome_value], dtype=torch.float32) + range_low, range_high = factor_spec.range[0] + + if self.dataset.theta.shape[1] == 1: + grid_tensor = torch.linspace(range_low, range_high, num_grid_points, dtype=torch.float32).unsqueeze(1) + with torch.no_grad(): + log_probabilities = self.posterior.log_prob(grid_tensor, x=observed_outcome) + density_numpy = torch.exp(log_probabilities).cpu().numpy() + grid_numpy = grid_tensor.squeeze(-1).cpu().numpy() + else: + with torch.no_grad(): + posterior_samples = self.posterior.sample((10_000,), x=observed_outcome) + factor_column_samples = posterior_samples[:, factor_column_slice].squeeze(-1).cpu().numpy() + grid_numpy = np.linspace(range_low, range_high, num_grid_points) + histogram_density, bin_edges = np.histogram( + factor_column_samples, bins=40, range=(range_low, range_high), density=True + ) + density_numpy = np.interp(grid_numpy, 0.5 * (bin_edges[:-1] + bin_edges[1:]), histogram_density) + + return grid_numpy, density_numpy + + def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: + """Estimate ``P(category | outcome)`` by sampling the trained posterior. + + Draws ``num_samples`` from ``posterior(theta | x=outcome_value)``, extracts the + factor's column (which sbi returns as floats over the BoxUniform support), rounds + to the nearest integer in ``[0, num_choices - 1]``, and tallies frequencies. + Result is a length-``num_choices`` numpy array that sums to 1. + """ + assert self.posterior is not None, "Call fit() before querying the posterior" + factor_spec = self._factor_spec(factor_name) + assert factor_spec.type == "categorical" + assert factor_spec.choices is not None + factor_column_slice = self.dataset.factor_columns[factor_name] + num_choices = len(factor_spec.choices) + + observed_outcome = torch.tensor([outcome_value], dtype=torch.float32) + with torch.no_grad(): + posterior_samples = self.posterior.sample((num_samples,), x=observed_outcome) + factor_column_samples = posterior_samples[:, factor_column_slice].squeeze(-1).cpu().numpy() + clipped_codes = np.clip(np.round(factor_column_samples), 0, num_choices - 1).astype(int) + return np.bincount(clipped_codes, minlength=num_choices) / num_samples + + +class NPEAnalyzer(PosteriorAnalyzer): + """Neural Posterior Estimation analyzer for continuous-only factor schemas. + + Use this when every declared factor is continuous (no categoricals). Internally + trains ``sbi.inference.NPE``, which fits a normalizing-flow density over + ``(theta, x_selected)`` and exposes both ``sample`` and ``log_prob`` on the result. + + **Caveat for binary outcomes (1D x):** sbi's flow code falls back to a Gaussian + density when the output space is 1D, which biases the recovered posterior peak + toward the *mean* of successful theta values rather than the true *mode* of the + success curve. We surface a [WARN] at fit time so users see this in plain text + rather than buried in sbi's UserWarning stream. + """ + + def _inference_cls(self): + from sbi.inference import NPE + + return NPE + + def _maybe_warn_binary_outcome(self, selected_outcome_column: torch.Tensor) -> None: + """Warn if theta is 1D and the outcome is binary — the configuration that triggers + the sbi Gaussian fallback. Multi-factor theta (dim ≥ 2) escapes the fallback. + """ + if self.dataset.theta.shape[1] > 1: + return + unique_values = set(selected_outcome_column.flatten().tolist()) + if unique_values.issubset({0.0, 1.0}): + print( + f"[WARN] Theta is 1D ({self.dataset.schema.factors[0].name!r}) and outcome" + f" {self.outcome_name!r} is binary. sbi NPE falls back to a Gaussian density" + " in 1D theta space, so the recovered posterior peak reflects the *mean* of" + " successful theta values rather than the true *mode* of the success curve." + " For this configuration prefer KDEAnalyzer (uniform prior + binary outcome" + " → KDE on successful-theta samples is the correct posterior)." + ) + + +class MNPEAnalyzer(PosteriorAnalyzer): + """Mixed Neural Posterior Estimation analyzer for schemas with at least one of each type. + + Use this when the schema mixes continuous and categorical factors. Internally trains + ``sbi.inference.MNPE``, whose mixed density estimator routes continuous theta columns + through a normalizing flow while routing categorical columns through a categorical + mass estimator. The continuous-first / categorical-after column ordering in + ``factor_columns`` matches MNPE's expected layout exactly. + + sbi MNPE 0.26 requires at least one continuous theta column. For pure-categorical + schemas use ``FrequencyTableAnalyzer`` instead — ``make_analyzer`` dispatches correctly. + """ + + def _inference_cls(self): + from sbi.inference import MNPE + + return MNPE diff --git a/isaaclab_arena/scripts/analyze_sensitivity.py b/isaaclab_arena/scripts/analyze_sensitivity.py index 411d9f42ca..cc22b78833 100644 --- a/isaaclab_arena/scripts/analyze_sensitivity.py +++ b/isaaclab_arena/scripts/analyze_sensitivity.py @@ -7,7 +7,7 @@ import argparse -from isaaclab_arena.analysis.sensitivity.analyzer import make_analyzer +from isaaclab_arena.analysis.sensitivity import make_analyzer from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset from isaaclab_arena.analysis.sensitivity.plotting import plot_marginal From 73e792e34f3e68a681e2f641c2fdc506d0d57142 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 14:53:47 +0200 Subject: [PATCH 14/74] Move sensitivity synthetic-data generators into tests/utils MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit They're standalone smoke-test tools — not part of the runtime pipeline — so they don't belong in the production analysis namespace. Relocated to the test-helper package, ready to back a sim-free analyzer regression test. Signed-off-by: Clemens Volk --- .../sensitivity => tests/utils}/synthetic_data_categorical.py | 0 .../sensitivity => tests/utils}/synthetic_data_continuous.py | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) rename isaaclab_arena/{analysis/sensitivity => tests/utils}/synthetic_data_categorical.py (100%) rename isaaclab_arena/{analysis/sensitivity => tests/utils}/synthetic_data_continuous.py (98%) diff --git a/isaaclab_arena/analysis/sensitivity/synthetic_data_categorical.py b/isaaclab_arena/tests/utils/synthetic_data_categorical.py similarity index 100% rename from isaaclab_arena/analysis/sensitivity/synthetic_data_categorical.py rename to isaaclab_arena/tests/utils/synthetic_data_categorical.py diff --git a/isaaclab_arena/analysis/sensitivity/synthetic_data_continuous.py b/isaaclab_arena/tests/utils/synthetic_data_continuous.py similarity index 98% rename from isaaclab_arena/analysis/sensitivity/synthetic_data_continuous.py rename to isaaclab_arena/tests/utils/synthetic_data_continuous.py index db8d592e02..4989e2014e 100644 --- a/isaaclab_arena/analysis/sensitivity/synthetic_data_continuous.py +++ b/isaaclab_arena/tests/utils/synthetic_data_continuous.py @@ -18,7 +18,7 @@ # importing episode_writer would pull in pxr via isaaclab_arena.metrics. _SYNTHETIC_FACTORS_YAML = """\ # factors.yaml — synthetic dataset for analyzer smoke-testing. -# Auto-emitted by isaaclab_arena.analysis.sensitivity.synthetic_data_continuous alongside the JSONL. +# Auto-emitted by isaaclab_arena.tests.utils.synthetic_data_continuous alongside the JSONL. slice: policy: synthetic_linear_uniform From 383aa79c377116b5f5f836de0ef7c5c6bf7a7cd9 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 15:01:08 +0200 Subject: [PATCH 15/74] Move sensitivity CLI scripts into the analysis package Relocate analyze_sensitivity.py / generate_sensitivity_report.py from scripts/ into analysis/sensitivity/ as analyze.py / generate_report.py, mirroring how eval_runner/policy_runner live flat inside the evaluation package. Drops the redundant "sensitivity" prefix now that the package name carries it. Signed-off-by: Clemens Volk --- .../analyze_sensitivity.py => analysis/sensitivity/analyze.py} | 0 .../sensitivity/generate_report.py} | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename isaaclab_arena/{scripts/analyze_sensitivity.py => analysis/sensitivity/analyze.py} (100%) rename isaaclab_arena/{scripts/generate_sensitivity_report.py => analysis/sensitivity/generate_report.py} (100%) diff --git a/isaaclab_arena/scripts/analyze_sensitivity.py b/isaaclab_arena/analysis/sensitivity/analyze.py similarity index 100% rename from isaaclab_arena/scripts/analyze_sensitivity.py rename to isaaclab_arena/analysis/sensitivity/analyze.py diff --git a/isaaclab_arena/scripts/generate_sensitivity_report.py b/isaaclab_arena/analysis/sensitivity/generate_report.py similarity index 100% rename from isaaclab_arena/scripts/generate_sensitivity_report.py rename to isaaclab_arena/analysis/sensitivity/generate_report.py From 5447928b1058ed9695b881e972f53af23ea9c22a Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 15:29:37 +0200 Subject: [PATCH 16/74] Reduce MVP to KDE + MNPE; park NPE and FrequencyTable analyzers MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Ship one analyzer per family — KDEAnalyzer (empirical) and MNPEAnalyzer (the sbi robolab port) — keeping the reviewable surface small while still demonstrating the multi-analyzer design across both the empirical and neural families. - Park NPEAnalyzer, FrequencyTableAnalyzer, and the now-orphaned categorical synthetic data generator on cvolk/feature/sensitivity_deferred_analyzers to bring in later. - Guard the deferred factor mixes in make_analyzer with clear asserts pointing at that branch: pure-categorical → FrequencyTable; multi-continuous or non-binary → NPE. - Keep the PosteriorAnalyzer/EmpiricalAnalyzer family bases as the extension points the parked siblings re-attach to; drop the now-unused binary-outcome warn hook. Signed-off-by: Clemens Volk --- .../sensitivity/empirical_analyzer.py | 77 ++--------- .../analysis/sensitivity/factory.py | 55 +++++--- .../analysis/sensitivity/plotting.py | 9 +- .../sensitivity/posterior_analyzer.py | 74 ++-------- .../tests/utils/synthetic_data_categorical.py | 126 ------------------ 5 files changed, 64 insertions(+), 277 deletions(-) delete mode 100644 isaaclab_arena/tests/utils/synthetic_data_categorical.py diff --git a/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py b/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py index 622f89120b..11bc3c1936 100644 --- a/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py @@ -14,16 +14,17 @@ class EmpiricalAnalyzer(BaseAnalyzer): """Abstract base for the direct (non-neural) analyzers. - Both subclasses exploit the same fact: under a uniform prior, + Subclasses exploit the same fact: under a uniform prior, ``P(theta | success) ∝ P(success | theta)``, so the posterior is read directly off the data — no neural density estimator, no parametric shape constraint. They differ only in factor type, which dictates the estimator: - - :class:`FrequencyTableAnalyzer` (categorical) — the per-category empirical success - rate: the raw empirical measure, usable as-is. - :class:`KDEAnalyzer` (continuous) — a Gaussian KDE over the successful-theta - samples: the same empirical measure, kernel-smoothed because a raw continuous - empirical measure is a sum of Diracs. + samples: the empirical measure, kernel-smoothed because a raw continuous + empirical measure is a sum of Diracs. Ships in this MVP. + - ``FrequencyTableAnalyzer`` (categorical) — the per-category empirical success + rate (the raw empirical measure). Parked on + ``cvolk/feature/sensitivity_deferred_analyzers``; plugs in here unchanged. Outcome is treated as binary: an episode is a "success" when its selected outcome column is ``>= SUCCESS_THRESHOLD``. @@ -39,61 +40,6 @@ def _success_mask(self) -> np.ndarray: return outcome_values >= self.SUCCESS_THRESHOLD -class FrequencyTableAnalyzer(EmpiricalAnalyzer): - """Frequency-table analyzer for pure-categorical factor schemas — no neural fit. - - Use this when every declared factor is categorical. Under a uniform prior, - Bayes' rule simplifies ``P(category | success) ∝ P(success | category) · P(category)`` - to ``P(category | success) ∝ P(success | category)`` — i.e. the posterior is *exactly* - the per-category empirical success rate, normalized to sum to 1. No neural network - can do better than this with a uniform prior; smoothing only hurts. - - Also covers a sbi limitation: MNPE 0.26 refuses to train if theta has zero continuous - columns. The empirical path sidesteps that entirely. - - Rejects continuous factors at construction time — ``make_analyzer`` shouldn't even - dispatch here for mixed schemas, but the explicit guard makes the constraint clear. - """ - - def __init__(self, dataset: SensitivityDataset, outcome_name: str): - super().__init__(dataset, outcome_name) - has_continuous_factor = any(factor.type == "continuous" for factor in dataset.schema.factors) - assert not has_continuous_factor, ( - "FrequencyTableAnalyzer is only valid for all-categorical schemas. For mixed" - " continuous + categorical factors, use MNPEAnalyzer." - ) - - def fit(self, training_batch_size: int = 50) -> None: - """No-op — the posterior is computed directly from the data at query time.""" - print(f"[INFO] {type(self).__name__}: no neural fit needed for pure-categorical schema.") - - def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: - """Return ``P(category | outcome) = per_category_success_rate / sum(per_category_success_rate)``. - - For each category, computes the fraction of its rows that count as a success (see - ``SUCCESS_THRESHOLD``), then normalizes across categories so the result sums to 1. - ``outcome_value`` and ``num_samples`` are accepted for interface compatibility with - ``PosteriorAnalyzer`` but not used — empirical analysis treats outcome as binary. - """ - factor_spec = self._factor_spec(factor_name) - assert factor_spec.type == "categorical" - assert factor_spec.choices is not None - factor_column_slice = self.dataset.factor_columns[factor_name] - num_choices = len(factor_spec.choices) - - empirical_theta_codes = self.dataset.theta[:, factor_column_slice].squeeze(-1).long().cpu().numpy() - success_mask = self._success_mask() - empirical_rates = np.zeros(num_choices) - for code in range(num_choices): - category_mask = empirical_theta_codes == code - if category_mask.any(): - empirical_rates[code] = float(success_mask[category_mask].mean()) - total_rate = float(empirical_rates.sum()) - if total_rate > 0: - return empirical_rates / total_rate - return np.full(num_choices, 1.0 / num_choices) - - class KDEAnalyzer(EmpiricalAnalyzer): """KDE-based analyzer for the 1-continuous-factor + binary-outcome case. @@ -109,9 +55,10 @@ class KDEAnalyzer(EmpiricalAnalyzer): the mean of successful-theta values rather than the true mode of the success curve. KDE has no such constraint and recovers multi-modal / plateau / skewed shapes faithfully. - Sibling of :class:`FrequencyTableAnalyzer` under the shared :class:`EmpiricalAnalyzer` - base (which does the same trick for purely categorical theta via frequency counts). For - multi-factor or non-binary-outcome workloads, :func:`make_analyzer` dispatches to NPE/MNPE. + Sits under the shared :class:`EmpiricalAnalyzer` base; its categorical sibling + ``FrequencyTableAnalyzer`` (same trick via frequency counts) is parked on + ``cvolk/feature/sensitivity_deferred_analyzers``. For mixed-factor workloads, + :func:`make_analyzer` dispatches to ``MNPEAnalyzer``. Caveats: - Bandwidth is scipy's Scott rule default; haven't tuned for non-uniform sample @@ -189,6 +136,6 @@ def continuous_marginal_density( def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: raise NotImplementedError( - "KDEAnalyzer is for continuous factors only. Categorical schemas dispatch to" - " FrequencyTableAnalyzer via make_analyzer." + "KDEAnalyzer handles a single continuous factor only; it has no categorical" + " factors by construction. Mixed schemas dispatch to MNPEAnalyzer via make_analyzer." ) diff --git a/isaaclab_arena/analysis/sensitivity/factory.py b/isaaclab_arena/analysis/sensitivity/factory.py index 11cc6fc083..dfa22cfd7c 100644 --- a/isaaclab_arena/analysis/sensitivity/factory.py +++ b/isaaclab_arena/analysis/sensitivity/factory.py @@ -15,38 +15,51 @@ def make_analyzer(dataset: SensitivityDataset, outcome_name: str) -> BaseAnalyzer: """Construct the right analyzer for the dataset's factor mix and outcome shape. - Dispatch table (checked top-to-bottom): - - 1 continuous + 0 categorical AND the outcome is binary → :class:`KDEAnalyzer` + This MVP ships two analyzers, one from each family: + - any continuous + any categorical (mixed) → :class:`MNPEAnalyzer` + - 1 continuous + 0 categorical AND a binary outcome → :class:`KDEAnalyzer` (avoids sbi NPE's 1D-theta Gaussian-shape constraint; exact under uniform prior) - - any continuous + any categorical → :class:`MNPEAnalyzer` - - all categorical (zero continuous) → :class:`FrequencyTableAnalyzer` - - all continuous (zero categorical) → :class:`NPEAnalyzer` - (the multi-continuous-factor case; theta is multi-D so no Gaussian fallback) - The KDE branch checks the outcome column's binary-ness on the dataset itself rather - than the schema, since outcome ``type: float`` in factors.yaml covers both continuous - durations and binary 0/1 success rates. + The remaining factor mixes are deferred to ``cvolk/feature/sensitivity_deferred_analyzers`` + and asserted against here so the gap fails loudly instead of mis-dispatching: + - all categorical (zero continuous) → ``FrequencyTableAnalyzer`` + - multi-continuous, or 1 continuous + non-binary outcome → ``NPEAnalyzer`` + + The binary check reads the outcome column off the dataset rather than the schema, + since outcome ``type: float`` in factors.yaml covers both continuous durations and + binary 0/1 success rates. Analyzer classes are imported lazily so that importing this module (and the package that re-exports it, which happens on the eval-time ``episode_writer`` path) doesn't pull in torch/sbi until analysis runs. """ - from isaaclab_arena.analysis.sensitivity.empirical_analyzer import FrequencyTableAnalyzer, KDEAnalyzer - from isaaclab_arena.analysis.sensitivity.posterior_analyzer import MNPEAnalyzer, NPEAnalyzer + from isaaclab_arena.analysis.sensitivity.empirical_analyzer import KDEAnalyzer + from isaaclab_arena.analysis.sensitivity.posterior_analyzer import MNPEAnalyzer num_continuous_factors = sum(1 for factor in dataset.schema.factors if factor.type == "continuous") num_categorical_factors = sum(1 for factor in dataset.schema.factors if factor.type == "categorical") assert num_continuous_factors + num_categorical_factors > 0, "Schema declares no factors" - if num_continuous_factors == 1 and num_categorical_factors == 0: - outcome_column_index = dataset.outcome_columns[outcome_name] - outcome_values = dataset.x[:, outcome_column_index] - unique_outcome_values = set(outcome_values.flatten().tolist()) - if unique_outcome_values.issubset({0.0, 1.0}): - return KDEAnalyzer(dataset, outcome_name) - + # Mixed continuous + categorical → the sbi mixed-density port from robolab. if num_continuous_factors > 0 and num_categorical_factors > 0: return MNPEAnalyzer(dataset, outcome_name) - if num_categorical_factors > 0: - return FrequencyTableAnalyzer(dataset, outcome_name) - return NPEAnalyzer(dataset, outcome_name) + + # Pure-categorical needs the (deferred) frequency-table analyzer. + assert num_continuous_factors > 0, ( + "Pure-categorical schemas need FrequencyTableAnalyzer, parked on " + "cvolk/feature/sensitivity_deferred_analyzers for this MVP." + ) + + # All-continuous from here. Only the single-factor binary case (KDE) ships; the + # multi-continuous case needs the (deferred) NPE analyzer. + assert num_continuous_factors == 1, ( + "Multi-continuous-factor schemas need NPEAnalyzer, parked on " + "cvolk/feature/sensitivity_deferred_analyzers for this MVP." + ) + outcome_column_index = dataset.outcome_columns[outcome_name] + unique_outcome_values = set(dataset.x[:, outcome_column_index].flatten().tolist()) + assert unique_outcome_values.issubset({0.0, 1.0}), ( + "A single continuous factor with a non-binary outcome needs NPEAnalyzer, parked on " + "cvolk/feature/sensitivity_deferred_analyzers for this MVP. (Binary outcomes use KDEAnalyzer.)" + ) + return KDEAnalyzer(dataset, outcome_name) diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index c76d195b60..aa10e2847b 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -28,8 +28,8 @@ def draw_marginal( Axes (a standalone plot wants the full slice block; a grid cell wants a compact label). For continuous factors, the analyzer must expose ``continuous_marginal_density`` - (``PosteriorAnalyzer`` and ``KDEAnalyzer`` do — ``FrequencyTableAnalyzer`` rejects - continuous factors at construction time, so it never reaches this branch). + (``PosteriorAnalyzer`` and ``KDEAnalyzer`` do); the categorical-only analyzers reject + continuous factors at construction time, so they never reach this branch. """ factor_spec = analyzer._factor_spec(factor_name) if factor_spec.type == "continuous": @@ -149,8 +149,9 @@ def _draw_categorical_marginal( The blue bar (left of each category) is the analyzer's ``P(category | outcome)``. The green bar (right of each category) is the *empirical* per-category outcome rate — independent of the analyzer's posterior, computed directly from the raw data. - For the ``FrequencyTableAnalyzer`` the two will agree exactly (up to normalization); for - a posterior-based analyzer they may differ slightly if the model smooths. + For a direct empirical analyzer the two agree exactly (up to normalization); for + a posterior-based analyzer (e.g. ``MNPEAnalyzer``) they may differ slightly if the + model smooths. Each green bar is annotated with the sample count ``n`` for that category, so the user can see how trustworthy each bar is. diff --git a/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py b/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py index 9510daad68..443a5cbf83 100644 --- a/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py @@ -13,15 +13,16 @@ class PosteriorAnalyzer(BaseAnalyzer): - """Common base for the sbi-driven analyzers (NPE and MNPE). + """Common base for the sbi-driven analyzers. - NPE and MNPE differ only in *which* sbi inference class they instantiate; everything - else (training loop, posterior storage, density and sample queries) is identical. - Subclasses override ``_make_inference`` to choose the class, and the - binary-outcome WARN hook to surface any method-specific caveats. + Subclasses differ only in *which* sbi inference class they instantiate (via + ``_inference_cls``); everything else (training loop, posterior storage, density and + sample queries) is shared. ``MNPEAnalyzer`` ships in this MVP; ``NPEAnalyzer`` (the + all-continuous case) is parked on ``cvolk/feature/sensitivity_deferred_analyzers`` + and plugs in here unchanged. After ``fit()`` returns, ``self.posterior`` is an sbi posterior object that supports - ``posterior.sample(shape, x=...)`` and (for NPE) ``posterior.log_prob(theta, x=...)``. + ``posterior.sample(shape, x=...)`` and ``posterior.log_prob(theta, x=...)``. """ def __init__(self, dataset: SensitivityDataset, outcome_name: str): @@ -46,15 +47,12 @@ def fit(self, training_batch_size: int = 50) -> None: Steps: 1. Slice ``self.dataset.x`` to the single outcome column named by ``outcome_name``. - 2. Surface any method-specific caveats about the outcome (e.g. NPE's - 1D-theta Gaussian fallback) via ``_maybe_warn_binary_outcome``. - 3. Instantiate the sbi inference object (NPE or MNPE) via ``_make_inference``. - 4. Append the simulations and train. - 5. Build a posterior object from the trained estimator and store it on ``self``. + 2. Instantiate the sbi inference object via ``_make_inference``. + 3. Append the simulations and train. + 4. Build a posterior object from the trained estimator and store it on ``self``. """ outcome_column_index = self.dataset.outcome_columns[self.outcome_name] selected_outcome_column = self.dataset.x[:, outcome_column_index : outcome_column_index + 1] - self._maybe_warn_binary_outcome(selected_outcome_column) print( f"[INFO] {type(self).__name__}: fitting on {self.dataset.theta.shape[0]} samples" @@ -66,16 +64,6 @@ def fit(self, training_batch_size: int = 50) -> None: density_estimator = inference.train(training_batch_size=training_batch_size) self.posterior = inference.build_posterior(density_estimator) - def _maybe_warn_binary_outcome(self, selected_outcome_column: torch.Tensor) -> None: - """Optional hook for subclass-specific caveats about the outcome. Default: no-op. - - ``NPEAnalyzer`` overrides this to warn that sbi falls back to a Gaussian density - when *theta* is 1D, biasing the recovered peak toward the mean of successful - theta values rather than the true mode. The fallback fires regardless of how - many outcome columns are logged — it's a property of single-factor analysis. - For 1-continuous-factor + binary-outcome workloads, prefer ``KDEAnalyzer``. - """ - def continuous_marginal_density( self, factor_name: str, outcome_value: float, num_grid_points: int ) -> tuple[np.ndarray, np.ndarray]: @@ -145,43 +133,6 @@ def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num return np.bincount(clipped_codes, minlength=num_choices) / num_samples -class NPEAnalyzer(PosteriorAnalyzer): - """Neural Posterior Estimation analyzer for continuous-only factor schemas. - - Use this when every declared factor is continuous (no categoricals). Internally - trains ``sbi.inference.NPE``, which fits a normalizing-flow density over - ``(theta, x_selected)`` and exposes both ``sample`` and ``log_prob`` on the result. - - **Caveat for binary outcomes (1D x):** sbi's flow code falls back to a Gaussian - density when the output space is 1D, which biases the recovered posterior peak - toward the *mean* of successful theta values rather than the true *mode* of the - success curve. We surface a [WARN] at fit time so users see this in plain text - rather than buried in sbi's UserWarning stream. - """ - - def _inference_cls(self): - from sbi.inference import NPE - - return NPE - - def _maybe_warn_binary_outcome(self, selected_outcome_column: torch.Tensor) -> None: - """Warn if theta is 1D and the outcome is binary — the configuration that triggers - the sbi Gaussian fallback. Multi-factor theta (dim ≥ 2) escapes the fallback. - """ - if self.dataset.theta.shape[1] > 1: - return - unique_values = set(selected_outcome_column.flatten().tolist()) - if unique_values.issubset({0.0, 1.0}): - print( - f"[WARN] Theta is 1D ({self.dataset.schema.factors[0].name!r}) and outcome" - f" {self.outcome_name!r} is binary. sbi NPE falls back to a Gaussian density" - " in 1D theta space, so the recovered posterior peak reflects the *mean* of" - " successful theta values rather than the true *mode* of the success curve." - " For this configuration prefer KDEAnalyzer (uniform prior + binary outcome" - " → KDE on successful-theta samples is the correct posterior)." - ) - - class MNPEAnalyzer(PosteriorAnalyzer): """Mixed Neural Posterior Estimation analyzer for schemas with at least one of each type. @@ -191,8 +142,9 @@ class MNPEAnalyzer(PosteriorAnalyzer): mass estimator. The continuous-first / categorical-after column ordering in ``factor_columns`` matches MNPE's expected layout exactly. - sbi MNPE 0.26 requires at least one continuous theta column. For pure-categorical - schemas use ``FrequencyTableAnalyzer`` instead — ``make_analyzer`` dispatches correctly. + sbi MNPE 0.26 requires at least one continuous theta column, so pure-categorical + schemas are handled by ``FrequencyTableAnalyzer`` (parked on + ``cvolk/feature/sensitivity_deferred_analyzers`` for this MVP; ``make_analyzer`` asserts). """ def _inference_cls(self): diff --git a/isaaclab_arena/tests/utils/synthetic_data_categorical.py b/isaaclab_arena/tests/utils/synthetic_data_categorical.py deleted file mode 100644 index 3409ac8d91..0000000000 --- a/isaaclab_arena/tests/utils/synthetic_data_categorical.py +++ /dev/null @@ -1,126 +0,0 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -from __future__ import annotations - -import argparse -import json -import random -from pathlib import Path - -# Five objects like the maple-table sweep: first three "easy" (high success), last two -# "hard" (low) — a known signal the analyzer should recover. -DEFAULT_CHOICES = [ - "rubiks_cube_hot3d_robolab", - "wooden_bowl_hot3d_robolab", - "alphabet_soup_can_hope_robolab", - "mug_ycb_robolab", - "sugar_box_ycb_robolab", -] -DEFAULT_SUCCESS_PROBABILITIES = [0.90, 0.85, 0.75, 0.25, 0.15] - - -def _factors_yaml_text(choices: list[str]) -> str: - """Build the factors.yaml content matching the synthetic data.""" - choices_string = ", ".join(choices) - return ( - "# factors.yaml — synthetic categorical dataset for analyzer smoke-testing.\n" - "# Auto-emitted by synthetic_data_categorical alongside the JSONL.\n" - "\n" - "slice:\n" - " policy: synthetic_categorical\n" - " task: synthetic_pick_and_place\n" - " embodiment: synthetic\n" - "\n" - "factors:\n" - " pick_up_object:\n" - " type: categorical\n" - f" choices: [{choices_string}]\n" - "\n" - "outcomes:\n" - " success_rate:\n" - " type: float\n" - " object_moved_rate:\n" - " type: float\n" - ) - - -def main(): - parser = argparse.ArgumentParser( - description=( - "Generate a synthetic episode_summary.jsonl where a single categorical factor drives the " - "success probability, for smoke-testing the categorical sensitivity analysis pipeline." - ), - formatter_class=argparse.RawDescriptionHelpFormatter, - ) - parser.add_argument( - "--output", - type=str, - default="/tmp/synthetic_categorical_episode_summary.jsonl", - help="Output JSONL path.", - ) - parser.add_argument( - "--factors-yaml-out", - type=str, - default=None, - help="Output factors.yaml path. Default: same directory as --output, named factors.yaml.", - ) - parser.add_argument( - "--num-episodes", - type=int, - default=200, - help="Total episodes (uniform draws across all choices). Default 200 → ~40 per category for 5 choices.", - ) - parser.add_argument("--seed", type=int, default=42, help="RNG seed for reproducibility.") - args = parser.parse_args() - - random_generator = random.Random(args.seed) - choices = DEFAULT_CHOICES - success_probabilities = DEFAULT_SUCCESS_PROBABILITIES - assert len(choices) == len( - success_probabilities - ), "DEFAULT_CHOICES and DEFAULT_SUCCESS_PROBABILITIES lengths must match" - num_choices = len(choices) - - summary_rows = [] - per_category_stats: dict[str, list[int]] = {choice: [0, 0] for choice in choices} # category → [successes, total] - for episode_index in range(args.num_episodes): - category_index = random_generator.randrange(num_choices) - chosen_category = choices[category_index] - was_success = 1.0 if random_generator.random() < success_probabilities[category_index] else 0.0 - per_category_stats[chosen_category][0] += int(was_success) - per_category_stats[chosen_category][1] += 1 - summary_rows.append({ - "job_name": "synth_categorical", - "episode_idx": episode_index, - "arena_env_args": {"pick_up_object": chosen_category}, - "outcomes": {"success_rate": was_success, "object_moved_rate": was_success}, - }) - - output_path = Path(args.output) - output_path.parent.mkdir(parents=True, exist_ok=True) - with open(output_path, "w", encoding="utf-8") as jsonl_file: - for summary_row in summary_rows: - jsonl_file.write(json.dumps(summary_row) + "\n") - - factors_yaml_path = Path(args.factors_yaml_out) if args.factors_yaml_out else output_path.parent / "factors.yaml" - factors_yaml_path.parent.mkdir(parents=True, exist_ok=True) - factors_yaml_path.write_text(_factors_yaml_text(choices), encoding="utf-8") - - print(f"[INFO] Wrote {len(summary_rows)} rows to {output_path}") - print(f"[INFO] Wrote factors schema → {factors_yaml_path}") - print("[INFO] Per-category success counts (analyzer should pull posterior mass toward easy cats):") - for choice, target_probability in zip(choices, success_probabilities): - successes, total = per_category_stats[choice] - empirical_percentage = 100 * successes / total if total else 0.0 - bar_string = "█" * int(round(empirical_percentage / 5)) - print( - f" {choice:<35s} target={target_probability:>4.0%}" - f" empirical={successes:>3d}/{total:<3d} ({empirical_percentage:>5.1f}%) {bar_string}" - ) - - -if __name__ == "__main__": - main() From f4a3fad685bd06dcfd110e7e0753169e6d363fee Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 15:49:07 +0200 Subject: [PATCH 17/74] Make sensitivity factory asserts and docstrings self-contained Drop git-branch references and the "robolab" attribution from the make_analyzer asserts and the analyzer docstrings; state the unsupported factor mixes plainly. Signed-off-by: Clemens Volk --- .../sensitivity/empirical_analyzer.py | 10 +++--- .../analysis/sensitivity/factory.py | 32 +++++++------------ .../sensitivity/posterior_analyzer.py | 8 ++--- 3 files changed, 18 insertions(+), 32 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py b/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py index 11bc3c1936..d846d0a04b 100644 --- a/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py @@ -21,10 +21,9 @@ class EmpiricalAnalyzer(BaseAnalyzer): - :class:`KDEAnalyzer` (continuous) — a Gaussian KDE over the successful-theta samples: the empirical measure, kernel-smoothed because a raw continuous - empirical measure is a sum of Diracs. Ships in this MVP. + empirical measure is a sum of Diracs. The supported subclass. - ``FrequencyTableAnalyzer`` (categorical) — the per-category empirical success - rate (the raw empirical measure). Parked on - ``cvolk/feature/sensitivity_deferred_analyzers``; plugs in here unchanged. + rate (the raw empirical measure). Not part of this MVP; plugs in here unchanged. Outcome is treated as binary: an episode is a "success" when its selected outcome column is ``>= SUCCESS_THRESHOLD``. @@ -56,9 +55,8 @@ class KDEAnalyzer(EmpiricalAnalyzer): KDE has no such constraint and recovers multi-modal / plateau / skewed shapes faithfully. Sits under the shared :class:`EmpiricalAnalyzer` base; its categorical sibling - ``FrequencyTableAnalyzer`` (same trick via frequency counts) is parked on - ``cvolk/feature/sensitivity_deferred_analyzers``. For mixed-factor workloads, - :func:`make_analyzer` dispatches to ``MNPEAnalyzer``. + ``FrequencyTableAnalyzer`` (same trick via frequency counts) is not part of this MVP. + For mixed-factor workloads, :func:`make_analyzer` dispatches to ``MNPEAnalyzer``. Caveats: - Bandwidth is scipy's Scott rule default; haven't tuned for non-uniform sample diff --git a/isaaclab_arena/analysis/sensitivity/factory.py b/isaaclab_arena/analysis/sensitivity/factory.py index dfa22cfd7c..4ed67fefad 100644 --- a/isaaclab_arena/analysis/sensitivity/factory.py +++ b/isaaclab_arena/analysis/sensitivity/factory.py @@ -15,15 +15,14 @@ def make_analyzer(dataset: SensitivityDataset, outcome_name: str) -> BaseAnalyzer: """Construct the right analyzer for the dataset's factor mix and outcome shape. - This MVP ships two analyzers, one from each family: + This MVP supports two factor mixes, one analyzer from each family: - any continuous + any categorical (mixed) → :class:`MNPEAnalyzer` - 1 continuous + 0 categorical AND a binary outcome → :class:`KDEAnalyzer` (avoids sbi NPE's 1D-theta Gaussian-shape constraint; exact under uniform prior) - The remaining factor mixes are deferred to ``cvolk/feature/sensitivity_deferred_analyzers`` - and asserted against here so the gap fails loudly instead of mis-dispatching: - - all categorical (zero continuous) → ``FrequencyTableAnalyzer`` - - multi-continuous, or 1 continuous + non-binary outcome → ``NPEAnalyzer`` + Other factor mixes (pure-categorical, or multiple/non-binary continuous) are not + supported yet; they are asserted against here so the gap fails loudly instead of + mis-dispatching. The binary check reads the outcome column off the dataset rather than the schema, since outcome ``type: float`` in factors.yaml covers both continuous durations and @@ -40,26 +39,17 @@ def make_analyzer(dataset: SensitivityDataset, outcome_name: str) -> BaseAnalyze num_categorical_factors = sum(1 for factor in dataset.schema.factors if factor.type == "categorical") assert num_continuous_factors + num_categorical_factors > 0, "Schema declares no factors" - # Mixed continuous + categorical → the sbi mixed-density port from robolab. + # Mixed continuous + categorical → mixed neural posterior estimation. if num_continuous_factors > 0 and num_categorical_factors > 0: return MNPEAnalyzer(dataset, outcome_name) - # Pure-categorical needs the (deferred) frequency-table analyzer. - assert num_continuous_factors > 0, ( - "Pure-categorical schemas need FrequencyTableAnalyzer, parked on " - "cvolk/feature/sensitivity_deferred_analyzers for this MVP." - ) + assert num_continuous_factors > 0, "Pure-categorical schemas are not supported yet." - # All-continuous from here. Only the single-factor binary case (KDE) ships; the - # multi-continuous case needs the (deferred) NPE analyzer. - assert num_continuous_factors == 1, ( - "Multi-continuous-factor schemas need NPEAnalyzer, parked on " - "cvolk/feature/sensitivity_deferred_analyzers for this MVP." - ) + # All-continuous from here. Only the single-factor binary-outcome case is supported. + assert num_continuous_factors == 1, "Schemas with more than one continuous factor are not supported yet." outcome_column_index = dataset.outcome_columns[outcome_name] unique_outcome_values = set(dataset.x[:, outcome_column_index].flatten().tolist()) - assert unique_outcome_values.issubset({0.0, 1.0}), ( - "A single continuous factor with a non-binary outcome needs NPEAnalyzer, parked on " - "cvolk/feature/sensitivity_deferred_analyzers for this MVP. (Binary outcomes use KDEAnalyzer.)" - ) + assert unique_outcome_values.issubset( + {0.0, 1.0} + ), "A single continuous factor is only supported with a binary outcome." return KDEAnalyzer(dataset, outcome_name) diff --git a/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py b/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py index 443a5cbf83..951f5c7866 100644 --- a/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py @@ -17,9 +17,8 @@ class PosteriorAnalyzer(BaseAnalyzer): Subclasses differ only in *which* sbi inference class they instantiate (via ``_inference_cls``); everything else (training loop, posterior storage, density and - sample queries) is shared. ``MNPEAnalyzer`` ships in this MVP; ``NPEAnalyzer`` (the - all-continuous case) is parked on ``cvolk/feature/sensitivity_deferred_analyzers`` - and plugs in here unchanged. + sample queries) is shared. ``MNPEAnalyzer`` is the supported subclass; the + all-continuous ``NPEAnalyzer`` is not part of this MVP but plugs in here unchanged. After ``fit()`` returns, ``self.posterior`` is an sbi posterior object that supports ``posterior.sample(shape, x=...)`` and ``posterior.log_prob(theta, x=...)``. @@ -143,8 +142,7 @@ class MNPEAnalyzer(PosteriorAnalyzer): ``factor_columns`` matches MNPE's expected layout exactly. sbi MNPE 0.26 requires at least one continuous theta column, so pure-categorical - schemas are handled by ``FrequencyTableAnalyzer`` (parked on - ``cvolk/feature/sensitivity_deferred_analyzers`` for this MVP; ``make_analyzer`` asserts). + schemas are not supported in this MVP (``make_analyzer`` asserts). """ def _inference_cls(self): From a9d05b884ae42e8907da87c1442065d836375de9 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 15:49:12 +0200 Subject: [PATCH 18/74] Move episode_writer from the analysis package into evaluation MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit episode_writer is eval-time data production — it runs inside the eval loop, depends on the metrics/evaluation machinery, and is called only by eval_runner. It has no coupling to the analysis code (the analyzer consumes the JSONL purely as a format). Relocating it beside its caller frees the sensitivity package of any pxr/sim dependency. Signed-off-by: Clemens Volk --- .../{analysis/sensitivity => evaluation}/episode_writer.py | 0 isaaclab_arena/evaluation/eval_runner.py | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) rename isaaclab_arena/{analysis/sensitivity => evaluation}/episode_writer.py (100%) diff --git a/isaaclab_arena/analysis/sensitivity/episode_writer.py b/isaaclab_arena/evaluation/episode_writer.py similarity index 100% rename from isaaclab_arena/analysis/sensitivity/episode_writer.py rename to isaaclab_arena/evaluation/episode_writer.py diff --git a/isaaclab_arena/evaluation/eval_runner.py b/isaaclab_arena/evaluation/eval_runner.py index e6abbbd228..825f579b36 100644 --- a/isaaclab_arena/evaluation/eval_runner.py +++ b/isaaclab_arena/evaluation/eval_runner.py @@ -262,7 +262,7 @@ def main(): if episode_summary_enabled: # Deferred import: episode_writer transitively touches pxr, so import # after sim init — same pattern as policy_runner's compute_metrics. - from isaaclab_arena.analysis.sensitivity.episode_writer import write_episode_summaries + from isaaclab_arena.evaluation.episode_writer import write_episode_summaries rows = write_episode_summaries(env, job, args_cli.episode_summary) print(f"[INFO] Wrote {rows} episode summaries for job '{job.name}'") From d737e15fc72605109586e18fcd64eb198a160cbc Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 15:49:18 +0200 Subject: [PATCH 19/74] Drop the single-PNG analyze CLI; PDF report is the sole renderer MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The analyze.py CLI rendered one (factor, outcome) marginal to a PNG — a strict subset of the outcome × factor grid that generate_report already produces. Remove it along with the now-unused plot_marginal/_plot_title/_save_figure helpers, leaving draw_marginal (used by the PDF report) as the single rendering path. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/analyze.py | 96 ------------------- .../analysis/sensitivity/plotting.py | 55 ----------- 2 files changed, 151 deletions(-) delete mode 100644 isaaclab_arena/analysis/sensitivity/analyze.py diff --git a/isaaclab_arena/analysis/sensitivity/analyze.py b/isaaclab_arena/analysis/sensitivity/analyze.py deleted file mode 100644 index cc22b78833..0000000000 --- a/isaaclab_arena/analysis/sensitivity/analyze.py +++ /dev/null @@ -1,96 +0,0 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -from __future__ import annotations - -import argparse - -from isaaclab_arena.analysis.sensitivity import make_analyzer -from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset -from isaaclab_arena.analysis.sensitivity.plotting import plot_marginal - - -def main(): - parser = argparse.ArgumentParser( - description=( - "Offline 1D continuous sensitivity analysis: fit an analyzer on a " - "(factors.yaml, episode_summary.jsonl) pair and save a posterior-marginal plot." - ) - ) - parser.add_argument("--factors_yaml", type=str, required=True, help="Path to factors.yaml.") - parser.add_argument( - "--episode_summary", type=str, required=True, help="Path to episode_summary.jsonl produced by eval_runner." - ) - parser.add_argument( - "--input_factor", - type=str, - default=None, - help="Name of the factor to plot. Defaults to the only factor declared in factors.yaml.", - ) - parser.add_argument( - "--output_metric", - type=str, - default=None, - help="Outcome name to condition on. Defaults to the first outcome listed in factors.yaml.", - ) - parser.add_argument( - "--outcome_value", - type=float, - default=1.0, - help="Outcome value to condition on (1.0 = success). Default: 1.0.", - ) - parser.add_argument( - "--figure_path", - type=str, - default="./sensitivity.png", - help="Output figure path. Default: ./sensitivity.png.", - ) - args = parser.parse_args() - - print(f"[INFO] Loading dataset: factors={args.factors_yaml} jsonl={args.episode_summary}") - dataset = SensitivityDataset(args.factors_yaml, args.episode_summary) - - available_factors = list(dataset.factor_columns) - available_outcomes = [outcome.name for outcome in dataset.schema.outcomes] - - if args.input_factor is None: - factor_name = available_factors[0] - else: - if args.input_factor not in available_factors: - parser.error( - f"--input_factor {args.input_factor!r} not found in factors.yaml. " - f"Available factors: {available_factors}" - ) - factor_name = args.input_factor - - if args.output_metric is None: - outcome_name = available_outcomes[0] - else: - if args.output_metric not in available_outcomes: - parser.error( - f"--output_metric {args.output_metric!r} not found in factors.yaml. " - f"Available outcomes: {available_outcomes}" - ) - outcome_name = args.output_metric - - print( - f"[INFO] Analyzing factor '{factor_name}' against outcome '{outcome_name}'" - f" (conditioning on outcome={args.outcome_value:g})" - ) - print( - f"[INFO] num_episodes={len(dataset.rows)}; theta shape={tuple(dataset.theta.shape)};" - f" x shape={tuple(dataset.x.shape)}" - ) - - analyzer = make_analyzer(dataset, outcome_name=outcome_name) - print(f"[INFO] Dispatched analyzer: {type(analyzer).__name__}") - analyzer.fit() - print(f"[INFO] Plotting marginal -> {args.figure_path}") - plot_marginal(analyzer, factor_name, output_path=args.figure_path, outcome_value=args.outcome_value) - print("[INFO] Done.") - - -if __name__ == "__main__": - main() diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index aa10e2847b..138b661bf1 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -6,7 +6,6 @@ from __future__ import annotations import numpy as np -from pathlib import Path from typing import TYPE_CHECKING if TYPE_CHECKING: @@ -44,33 +43,6 @@ def draw_marginal( raise NotImplementedError(f"Unsupported factor type {factor_spec.type!r}") -def plot_marginal( - analyzer: BaseAnalyzer, - factor_name: str, - output_path, - outcome_value: float = 1.0, - num_samples: int = 10_000, - num_grid_points: int = 200, -) -> None: - """Render one factor's marginal posterior into its own figure and save it. - - Thin wrapper over ``draw_marginal``: sizes a single-Axes figure, draws, titles it with - the full slice block, and saves to ``output_path``. - """ - import matplotlib.pyplot as plt - - factor_spec = analyzer._factor_spec(factor_name) - if factor_spec.type == "categorical" and factor_spec.choices is not None: - figsize = (max(8, 1.0 * len(factor_spec.choices)), 5) - else: - figsize = (8, 5) - figure, axes = plt.subplots(figsize=figsize) - draw_marginal(axes, analyzer, factor_name, outcome_value, num_samples, num_grid_points) - axes.set_title(_plot_title(analyzer, factor_name)) - figure.tight_layout() - _save_figure(figure, output_path) - - def _draw_continuous_marginal( ax, analyzer: BaseAnalyzer, @@ -209,30 +181,3 @@ def _draw_categorical_marginal( ax.set_ylim(0, 1.05) ax.legend(loc="best", fontsize=9) ax.grid(alpha=0.3, axis="y") - - -def _plot_title(analyzer: BaseAnalyzer, factor_name: str) -> str: - """Format the plot title as ``"Sensitivity of to " / slice block``.""" - return ( - f"Sensitivity of {analyzer.outcome_name} to {factor_name}\n" - f"slice: {analyzer.dataset.schema.slice.policy} / " - f"{analyzer.dataset.schema.slice.task} / {analyzer.dataset.schema.slice.embodiment}" - ) - - -def _save_figure(figure, destination) -> None: - """Save a matplotlib figure to ``destination`` (a path or a writable file-like object). - - Accepts either a filesystem path (``str`` / ``Path``) or any seekable file-like buffer - (e.g. ``io.BytesIO``). Paths get parent-dir creation; buffers are written to directly. - The figure is closed after save regardless of destination type. - """ - import matplotlib.pyplot as plt - - if isinstance(destination, (str, Path)): - path = Path(destination) - path.parent.mkdir(parents=True, exist_ok=True) - figure.savefig(path, dpi=150) - else: - figure.savefig(destination, dpi=150, format="png") - plt.close(figure) From 9eea5fc27ba88c48d24a5b60a7e6098a28e8829e Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 15:49:27 +0200 Subject: [PATCH 20/74] Drop the synthetic task_duration outcome from the continuous generator task_duration was a synthetic-only continuous outcome (no matching registered metric) that existed to exercise NPE. With NPE deferred, a single continuous factor with a non-binary outcome now asserts, so emitting it made generate_report crash on the smoke dataset. The generator now emits only the binary success/object-moved outcomes the MVP analyzes. Signed-off-by: Clemens Volk --- .../tests/utils/synthetic_data_continuous.py | 16 ---------------- 1 file changed, 16 deletions(-) diff --git a/isaaclab_arena/tests/utils/synthetic_data_continuous.py b/isaaclab_arena/tests/utils/synthetic_data_continuous.py index 4989e2014e..2b3f34030f 100644 --- a/isaaclab_arena/tests/utils/synthetic_data_continuous.py +++ b/isaaclab_arena/tests/utils/synthetic_data_continuous.py @@ -35,16 +35,8 @@ type: float object_moved_rate: type: float - task_duration: - type: float """ -# Synthetic task-duration model: at the competence-band center an episode finishes in ~5s; -# successes scale linearly with z-score (harder = slower); failures hit the episode timeout. -EPISODE_TIMEOUT_S = 20.0 -BASE_SUCCESS_DURATION_S = 5.0 -SUCCESS_DURATION_PER_SIGMA = 6.0 - def success_probability(intensity: float, center: float, sigma: float) -> float: """Linear-Gaussian competence band: peaks at `center`, falls off symmetrically in linear space.""" @@ -93,13 +85,6 @@ def main(): intensity = random_generator.uniform(INTENSITY_LOW, INTENSITY_HIGH) probability_of_success = success_probability(intensity, args.center, args.sigma) was_success = 1.0 if random_generator.random() < probability_of_success else 0.0 - z_score = abs(intensity - args.center) / args.sigma - if was_success: - task_duration = BASE_SUCCESS_DURATION_S + SUCCESS_DURATION_PER_SIGMA * z_score - task_duration += random_generator.gauss(0.0, 0.4) - task_duration = max(1.0, min(task_duration, EPISODE_TIMEOUT_S)) - else: - task_duration = EPISODE_TIMEOUT_S summary_rows.append({ "job_name": "synth_linear_uniform", "episode_idx": episode_index, @@ -107,7 +92,6 @@ def main(): "outcomes": { "success_rate": was_success, "object_moved_rate": was_success, - "task_duration": task_duration, }, }) From dcb48a4a25f0703f552c6a78c3727fd41f0f0b55 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 15:58:54 +0200 Subject: [PATCH 21/74] Use current-year-only copyright headers in the sensitivity workstream Address PR review: change the copyright headers on the sensitivity package files (plus the moved episode_writer and the synthetic generator) from 2025-2026 to 2026, matching the --use-current-year convention used by new files in the repo. Signed-off-by: Clemens Volk --- isaaclab_arena/analysis/sensitivity/__init__.py | 2 +- isaaclab_arena/analysis/sensitivity/analyzer_base.py | 2 +- isaaclab_arena/analysis/sensitivity/dataset.py | 2 +- isaaclab_arena/analysis/sensitivity/empirical_analyzer.py | 2 +- isaaclab_arena/analysis/sensitivity/factory.py | 2 +- isaaclab_arena/analysis/sensitivity/generate_report.py | 2 +- isaaclab_arena/analysis/sensitivity/pdf_report.py | 2 +- isaaclab_arena/analysis/sensitivity/plotting.py | 2 +- isaaclab_arena/analysis/sensitivity/posterior_analyzer.py | 2 +- isaaclab_arena/evaluation/episode_writer.py | 2 +- isaaclab_arena/tests/utils/synthetic_data_continuous.py | 2 +- 11 files changed, 11 insertions(+), 11 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/__init__.py b/isaaclab_arena/analysis/sensitivity/__init__.py index 354dc638bb..56be864a91 100644 --- a/isaaclab_arena/analysis/sensitivity/__init__.py +++ b/isaaclab_arena/analysis/sensitivity/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: Apache-2.0 diff --git a/isaaclab_arena/analysis/sensitivity/analyzer_base.py b/isaaclab_arena/analysis/sensitivity/analyzer_base.py index a23a86c536..ee4cda6789 100644 --- a/isaaclab_arena/analysis/sensitivity/analyzer_base.py +++ b/isaaclab_arena/analysis/sensitivity/analyzer_base.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: Apache-2.0 diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py index b6ca8c846d..4e75253b1e 100644 --- a/isaaclab_arena/analysis/sensitivity/dataset.py +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: Apache-2.0 diff --git a/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py b/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py index d846d0a04b..c91b803b73 100644 --- a/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: Apache-2.0 diff --git a/isaaclab_arena/analysis/sensitivity/factory.py b/isaaclab_arena/analysis/sensitivity/factory.py index 4ed67fefad..78fac8d9af 100644 --- a/isaaclab_arena/analysis/sensitivity/factory.py +++ b/isaaclab_arena/analysis/sensitivity/factory.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: Apache-2.0 diff --git a/isaaclab_arena/analysis/sensitivity/generate_report.py b/isaaclab_arena/analysis/sensitivity/generate_report.py index 5a865fd298..8a2ebedec9 100644 --- a/isaaclab_arena/analysis/sensitivity/generate_report.py +++ b/isaaclab_arena/analysis/sensitivity/generate_report.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: Apache-2.0 diff --git a/isaaclab_arena/analysis/sensitivity/pdf_report.py b/isaaclab_arena/analysis/sensitivity/pdf_report.py index a863f93d30..0ac6c4c3ec 100644 --- a/isaaclab_arena/analysis/sensitivity/pdf_report.py +++ b/isaaclab_arena/analysis/sensitivity/pdf_report.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: Apache-2.0 diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index 138b661bf1..19e945cf87 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: Apache-2.0 diff --git a/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py b/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py index 951f5c7866..41a458e254 100644 --- a/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: Apache-2.0 diff --git a/isaaclab_arena/evaluation/episode_writer.py b/isaaclab_arena/evaluation/episode_writer.py index aef80b4f0a..cd0e126a09 100644 --- a/isaaclab_arena/evaluation/episode_writer.py +++ b/isaaclab_arena/evaluation/episode_writer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: Apache-2.0 diff --git a/isaaclab_arena/tests/utils/synthetic_data_continuous.py b/isaaclab_arena/tests/utils/synthetic_data_continuous.py index 2b3f34030f..bbd994b960 100644 --- a/isaaclab_arena/tests/utils/synthetic_data_continuous.py +++ b/isaaclab_arena/tests/utils/synthetic_data_continuous.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: Apache-2.0 From bad0760a20ce8644fd4362e13b07b9e730b784fa Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 16:47:34 +0200 Subject: [PATCH 22/74] Hoist episode_writer import to eval_runner module top Lab3 no longer pulls pxr/omni at isaaclab import time, so the deferred import past sim init is unnecessary. Verified episode_writer and eval_runner both import cleanly with no live SimulationApp. Signed-off-by: Clemens Volk --- isaaclab_arena/evaluation/eval_runner.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/isaaclab_arena/evaluation/eval_runner.py b/isaaclab_arena/evaluation/eval_runner.py index 825f579b36..ee440f3d91 100644 --- a/isaaclab_arena/evaluation/eval_runner.py +++ b/isaaclab_arena/evaluation/eval_runner.py @@ -19,6 +19,7 @@ from typing import TYPE_CHECKING from isaaclab_arena.cli.isaaclab_arena_cli import get_isaaclab_arena_cli_parser +from isaaclab_arena.evaluation.episode_writer import write_episode_summaries from isaaclab_arena.evaluation.eval_runner_cli import add_eval_runner_arguments from isaaclab_arena.evaluation.job_manager import Job, JobManager, Status from isaaclab_arena.evaluation.policy_runner import get_policy_cls, rollout_policy @@ -260,10 +261,6 @@ def main(): ) if episode_summary_enabled: - # Deferred import: episode_writer transitively touches pxr, so import - # after sim init — same pattern as policy_runner's compute_metrics. - from isaaclab_arena.evaluation.episode_writer import write_episode_summaries - rows = write_episode_summaries(env, job, args_cli.episode_summary) print(f"[INFO] Wrote {rows} episode summaries for job '{job.name}'") From c2c563842924062e5024424e57df4df4ea33226c Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 16:47:41 +0200 Subject: [PATCH 23/74] Clarify SliceSpec as plain dataset provenance The old docstring claimed loader enforcement that does not exist and justified the type via NPE (parked). Describe what it actually is: provenance read from factors.yaml, shown in the report title, unchecked. Signed-off-by: Clemens Volk --- isaaclab_arena/analysis/sensitivity/dataset.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py index 4e75253b1e..aa00f12600 100644 --- a/isaaclab_arena/analysis/sensitivity/dataset.py +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -38,10 +38,10 @@ class OutcomeSpec: @dataclass class SliceSpec: - """The ``(policy, task, embodiment)`` tuple a dataset comes from. + """Where a dataset came from: which policy ran which task on which embodiment. - MNPE/NPE assume a single data-generating source per analysis, so all rows in a - dataset must belong to the same slice — enforced by the loader. + Read from the ``slice:`` block of ``factors.yaml`` and shown in the report title. + It just labels the data — nothing checks it against the rows. """ policy: str From 4d9509c8055504532613bf97aa7fb0727ad4e8be Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 16:47:41 +0200 Subject: [PATCH 24/74] Delete the unused synthetic continuous data generator A standalone manual CLI nothing imports or tests. Removing it rather than letting it rot out of sync; revivable from history when needed. Signed-off-by: Clemens Volk --- .../tests/utils/synthetic_data_continuous.py | 132 ------------------ 1 file changed, 132 deletions(-) delete mode 100644 isaaclab_arena/tests/utils/synthetic_data_continuous.py diff --git a/isaaclab_arena/tests/utils/synthetic_data_continuous.py b/isaaclab_arena/tests/utils/synthetic_data_continuous.py deleted file mode 100644 index bbd994b960..0000000000 --- a/isaaclab_arena/tests/utils/synthetic_data_continuous.py +++ /dev/null @@ -1,132 +0,0 @@ -# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -from __future__ import annotations - -import argparse -import json -import math -import random -from pathlib import Path - -INTENSITY_LOW = 10.0 -INTENSITY_HIGH = 5000.0 - -# Inline factors.yaml template (not imported) so this stays a pure-python dev tool — -# importing episode_writer would pull in pxr via isaaclab_arena.metrics. -_SYNTHETIC_FACTORS_YAML = """\ -# factors.yaml — synthetic dataset for analyzer smoke-testing. -# Auto-emitted by isaaclab_arena.tests.utils.synthetic_data_continuous alongside the JSONL. - -slice: - policy: synthetic_linear_uniform - task: synthetic_pick_and_place - embodiment: synthetic - -factors: - light_intensity: - type: continuous - dim: 1 - -outcomes: - success_rate: - type: float - object_moved_rate: - type: float -""" - - -def success_probability(intensity: float, center: float, sigma: float) -> float: - """Linear-Gaussian competence band: peaks at `center`, falls off symmetrically in linear space.""" - z_score = (intensity - center) / sigma - return math.exp(-0.5 * z_score * z_score) - - -def main(): - parser = argparse.ArgumentParser( - description=( - "Generate a synthetic episode_summary.jsonl with a known linear-Gaussian competence band " - "for smoke-testing the continuous sensitivity analysis pipeline." - ), - formatter_class=argparse.RawDescriptionHelpFormatter, - ) - parser.add_argument("--output", type=str, default="/tmp/synthetic_episode_summary.jsonl", help="Output JSONL path.") - parser.add_argument( - "--factors-yaml-out", - type=str, - default=None, - help="Output factors.yaml path. Default: same directory as --output, named factors.yaml.", - ) - parser.add_argument( - "--num-episodes", - type=int, - default=180, - help="Total number of episodes to generate. Each draws an intensity from Uniform(10, 5000).", - ) - parser.add_argument("--center", type=float, default=500.0, help="Intensity where success rate peaks. Default: 500.") - parser.add_argument( - "--sigma", - type=float, - default=400.0, - help=( - "Linear-space width of the competence band (1 sigma in intensity units). Default: 400," - " which gives ~95%% success in [100, 900] and near-zero success beyond ~1700." - ), - ) - parser.add_argument("--seed", type=int, default=42, help="RNG seed for reproducibility.") - args = parser.parse_args() - - random_generator = random.Random(args.seed) - - summary_rows = [] - for episode_index in range(args.num_episodes): - intensity = random_generator.uniform(INTENSITY_LOW, INTENSITY_HIGH) - probability_of_success = success_probability(intensity, args.center, args.sigma) - was_success = 1.0 if random_generator.random() < probability_of_success else 0.0 - summary_rows.append({ - "job_name": "synth_linear_uniform", - "episode_idx": episode_index, - "arena_env_args": {"light_intensity": intensity}, - "outcomes": { - "success_rate": was_success, - "object_moved_rate": was_success, - }, - }) - - output_path = Path(args.output) - output_path.parent.mkdir(parents=True, exist_ok=True) - with open(output_path, "w", encoding="utf-8") as jsonl_file: - for summary_row in summary_rows: - jsonl_file.write(json.dumps(summary_row) + "\n") - - # Emit a matching factors.yaml so the analyzer can be pointed at this synthetic dataset - # without any hand-authored schema. Inline string template — see _SYNTHETIC_FACTORS_YAML. - factors_yaml_path = Path(args.factors_yaml_out) if args.factors_yaml_out else output_path.parent / "factors.yaml" - factors_yaml_path.parent.mkdir(parents=True, exist_ok=True) - factors_yaml_path.write_text(_SYNTHETIC_FACTORS_YAML, encoding="utf-8") - - print(f"[INFO] Wrote {len(summary_rows)} rows to {output_path}") - print(f"[INFO] Wrote factors schema → {factors_yaml_path}") - print(f"[INFO] Linear-Gaussian competence band: center={args.center:g}, sigma={args.sigma:g}") - print("[INFO] Per-bin success rates (10 equal bins across the prior range):") - num_bins = 10 - bin_width = (INTENSITY_HIGH - INTENSITY_LOW) / num_bins - for bin_index in range(num_bins): - bin_low = INTENSITY_LOW + bin_index * bin_width - bin_high = bin_low + bin_width - rows_in_bin = [row for row in summary_rows if bin_low <= row["arena_env_args"]["light_intensity"] < bin_high] - if not rows_in_bin: - continue - successes_in_bin = sum(int(row["outcomes"]["success_rate"]) for row in rows_in_bin) - percentage = 100 * successes_in_bin / len(rows_in_bin) - bar_string = "█" * int(round(percentage / 5)) - print( - f" [{bin_low:>5g}, {bin_high:>5g}): {successes_in_bin:>3d}/{len(rows_in_bin):<3d}" - f" ({percentage:>5.1f}%) {bar_string}" - ) - - -if __name__ == "__main__": - main() From 102c1cec4409e9cb566e328120772e2f9c0ae679 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 16:55:37 +0200 Subject: [PATCH 25/74] Hoist shared plot styling into named constants The continuous and categorical drawers repeated the same colour, success threshold, marker size and rug-offset literals. Name them once at module top so the two plot types stay visually consistent and the magic numbers are self-documenting. No behavioural change. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/plotting.py | 47 ++++++++++++------- 1 file changed, 29 insertions(+), 18 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index 19e945cf87..ec59c6f43f 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -12,6 +12,17 @@ from isaaclab_arena.analysis.sensitivity.analyzer_base import BaseAnalyzer from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec +# Shared styling — kept in one place so the continuous and categorical drawers stay +# visually consistent (same colours mean the same thing across both plot types). +_POSTERIOR_COLOR = "steelblue" # analyzer posterior: density curve / left bar +_SUCCESS_COLOR = "seagreen" # outcome achieved: success rug / empirical-rate bar +_FAILURE_COLOR = "firebrick" # outcome not achieved: failure rug +_NEUTRAL_COLOR = "slategray" # rug for non-binary outcomes (no success/failure split) +_SUCCESS_THRESHOLD = 0.5 # outcome >= this counts as a success +_RUG_MARKER_SIZE = 80 # scatter marker size for empirical rug ticks +_RUG_SUCCESS_OFFSET = -0.05 # rug y-offset (× density.max()) for successes / neutral ticks +_RUG_FAILURE_OFFSET = -0.10 # rug y-offset (× density.max()) for failures + def draw_marginal( ax, @@ -67,40 +78,40 @@ def _draw_continuous_marginal( ax.plot( grid, density, - color="steelblue", + color=_POSTERIOR_COLOR, linewidth=2, label=f"P({factor_spec.name} | {analyzer.outcome_name}={outcome_value:g})", ) - ax.fill_between(grid, 0, density, color="steelblue", alpha=0.2) + ax.fill_between(grid, 0, density, color=_POSTERIOR_COLOR, alpha=0.2) - # Binary outcomes: split the rug green/red at 0.5 (successes vs failures). Continuous + # Binary outcomes: split the rug green/red at the success threshold (successes vs failures). Continuous # outcomes (e.g. task_duration): the threshold is meaningless, so show one neutral rug. is_binary_outcome = set(empirical_outcomes.flatten().tolist()).issubset({0.0, 1.0}) if is_binary_outcome: - success_mask = empirical_outcomes >= 0.5 + success_mask = empirical_outcomes >= _SUCCESS_THRESHOLD ax.scatter( empirical_theta_values[success_mask], - np.full(success_mask.sum(), -0.05 * density.max()), + np.full(success_mask.sum(), _RUG_SUCCESS_OFFSET * density.max()), marker="|", - color="seagreen", - s=80, - label=f"{analyzer.outcome_name} ≥ 0.5 (n={success_mask.sum()})", + color=_SUCCESS_COLOR, + s=_RUG_MARKER_SIZE, + label=f"{analyzer.outcome_name} ≥ {_SUCCESS_THRESHOLD:g} (n={success_mask.sum()})", ) ax.scatter( empirical_theta_values[~success_mask], - np.full((~success_mask).sum(), -0.1 * density.max()), + np.full((~success_mask).sum(), _RUG_FAILURE_OFFSET * density.max()), marker="|", - color="firebrick", - s=80, - label=f"{analyzer.outcome_name} < 0.5 (n={(~success_mask).sum()})", + color=_FAILURE_COLOR, + s=_RUG_MARKER_SIZE, + label=f"{analyzer.outcome_name} < {_SUCCESS_THRESHOLD:g} (n={(~success_mask).sum()})", ) else: ax.scatter( empirical_theta_values, - np.full(len(empirical_theta_values), -0.05 * density.max()), + np.full(len(empirical_theta_values), _RUG_SUCCESS_OFFSET * density.max()), marker="|", - color="slategray", - s=80, + color=_NEUTRAL_COLOR, + s=_RUG_MARKER_SIZE, label=f"observed samples (n={len(empirical_theta_values)})", ) ax.set_xlabel(factor_spec.name) @@ -146,7 +157,7 @@ def _draw_categorical_marginal( category_mask = empirical_theta_codes == code empirical_counts[code] = int(category_mask.sum()) if category_mask.any(): - empirical_rates[code] = float((empirical_outcomes[category_mask] >= 0.5).mean()) + empirical_rates[code] = float((empirical_outcomes[category_mask] >= _SUCCESS_THRESHOLD).mean()) bar_x_positions = np.arange(num_choices) bar_width = 0.4 @@ -154,7 +165,7 @@ def _draw_categorical_marginal( bar_x_positions - bar_width / 2, posterior_probabilities, bar_width, - color="steelblue", + color=_POSTERIOR_COLOR, alpha=0.8, label=f"P(category | {analyzer.outcome_name}={outcome_value:g})", ) @@ -162,7 +173,7 @@ def _draw_categorical_marginal( bar_x_positions + bar_width / 2, empirical_rates, bar_width, - color="seagreen", + color=_SUCCESS_COLOR, alpha=0.7, label=f"empirical {analyzer.outcome_name} rate per category", ) From 3ec444c76749fbd1e87229a89fb6a3e70d0f68b4 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 17:01:54 +0200 Subject: [PATCH 26/74] Add MetricsManager.compute_per_episode Sibling to compute(): instead of reducing across all episodes to one aggregate per metric, feed each episode's recorded array to the compute func separately and return one metric dict per episode. Reuses the existing get_recorded_metric_data / get_num_episodes readers. Signed-off-by: Clemens Volk --- isaaclab_arena/metrics/metrics_manager.py | 26 +++++++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/isaaclab_arena/metrics/metrics_manager.py b/isaaclab_arena/metrics/metrics_manager.py index 333207f911..f3fec48d4a 100644 --- a/isaaclab_arena/metrics/metrics_manager.py +++ b/isaaclab_arena/metrics/metrics_manager.py @@ -62,3 +62,29 @@ def compute(self) -> dict[str, Any]: metrics_data[term_name] = term_cfg.compute_metric_func(recorded_metric_data, **term_cfg.params) metrics_data["num_episodes"] = get_num_episodes(dataset_path) return metrics_data + + def compute_per_episode(self) -> list[dict[str, Any]]: + """Compute every registered metric separately for each recorded episode. + + Where :meth:`compute` reduces across all episodes to one aggregate value per + metric, this returns one ``{metric_name: value}`` dict per episode — each metric's + compute func is fed that single episode's recorded array (a one-element list). + + Returns: + A list with one metric dict per episode, in recorded order. + """ + dataset_path = get_metric_recorder_dataset_path(self._env) + per_term_episode_arrays = { + term_name: get_recorded_metric_data(dataset_path, term_cfg.recorder_term_name) + for term_name, term_cfg in zip(self._term_names, self._term_cfgs) + } + num_episodes = get_num_episodes(dataset_path) + return [ + { + term_name: term_cfg.compute_metric_func( + [per_term_episode_arrays[term_name][episode_index]], **term_cfg.params + ) + for term_name, term_cfg in zip(self._term_names, self._term_cfgs) + } + for episode_index in range(num_episodes) + ] From 36b993c45b0e6133a90b1dfd404ab119ddf54947 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 17:02:00 +0200 Subject: [PATCH 27/74] Reuse MetricsManager in episode_writer; drop task_duration MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit episode_writer hand-rolled its own h5py traversal of the recorded dataset. Delegate to MetricsManager.compute_per_episode instead, so all HDF5/metric access lives in the metrics layer and episode_writer is just a thin JSONL writer (job context + per-episode outcomes). Also drop the synthetic task_duration outcome: it is continuous and non-binary, which make_analyzer asserts against for a single continuous factor, so real reports crashed on it — the same reason it was removed from the synthetic generator. Revivable when continuous-outcome analysis lands. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/plotting.py | 4 +- isaaclab_arena/evaluation/episode_writer.py | 64 ++++++------------- 2 files changed, 22 insertions(+), 46 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index ec59c6f43f..131011a67e 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -84,8 +84,8 @@ def _draw_continuous_marginal( ) ax.fill_between(grid, 0, density, color=_POSTERIOR_COLOR, alpha=0.2) - # Binary outcomes: split the rug green/red at the success threshold (successes vs failures). Continuous - # outcomes (e.g. task_duration): the threshold is meaningless, so show one neutral rug. + # Binary outcomes: split the rug green/red at the success threshold (successes vs failures). + # Continuous outcomes: the threshold is meaningless, so show one neutral rug. is_binary_outcome = set(empirical_outcomes.flatten().tolist()).issubset({0.0, 1.0}) if is_binary_outcome: success_mask = empirical_outcomes >= _SUCCESS_THRESHOLD diff --git a/isaaclab_arena/evaluation/episode_writer.py b/isaaclab_arena/evaluation/episode_writer.py index cd0e126a09..9751d2397a 100644 --- a/isaaclab_arena/evaluation/episode_writer.py +++ b/isaaclab_arena/evaluation/episode_writer.py @@ -5,12 +5,10 @@ from __future__ import annotations -import h5py import json from pathlib import Path from typing import TYPE_CHECKING -from isaaclab_arena.metrics.metrics import get_metric_recorder_dataset_path from isaaclab_arena.metrics.metrics_logger import metrics_to_plain_python_types if TYPE_CHECKING: @@ -18,20 +16,23 @@ def write_episode_summaries(env, job: Job, output_path: str | Path) -> int: - """Append one JSONL row per recorded demo for the just-completed job. + """Append one JSONL row per recorded episode for the just-completed job. Each row has shape:: { "job_name": "", - "episode_idx": , + "episode_idx": , "arena_env_args": , - "outcomes": + "outcomes": } + Per-episode metric values come from the env's ``MetricsManager`` (the same machinery + that backs ``compute_metrics``), so all HDF5/metric access stays in the metrics layer. + Args: - env: The (possibly gym-wrapped) Arena env that just finished its rollout. The hdf5 - path and registered metrics are read from ``env.unwrapped.cfg``. + env: The (possibly gym-wrapped) Arena env that just finished its rollout. Its + ``MetricsManager`` provides the per-episode metric values. job: The Job that ran. Its ``arena_env_args_dict`` is logged verbatim under ``arena_env_args``. output_path: JSONL file to append to. Created (with parent dirs) if absent. @@ -43,44 +44,19 @@ def write_episode_summaries(env, job: Job, output_path: str | Path) -> int: if not hasattr(unwrapped_env.cfg, "metrics") or unwrapped_env.cfg.metrics is None: return 0 + per_episode_metrics = unwrapped_env.metrics_manager.compute_per_episode() arena_env_args_snapshot = dict(job.arena_env_args_dict) - hdf5_dataset_path = get_metric_recorder_dataset_path(unwrapped_env) - registered_metrics = unwrapped_env.cfg.metrics output_path = Path(output_path) output_path.parent.mkdir(parents=True, exist_ok=True) - - rows_written = 0 - env_step_dt = float(unwrapped_env.step_dt) - with h5py.File(hdf5_dataset_path, "r") as hdf5_file: - recorded_demos = hdf5_file["data"] - with open(output_path, "a", encoding="utf-8") as jsonl_output: - for demo_index, demo_name in enumerate(recorded_demos): - demo_group = recorded_demos[demo_name] - raw_outcome_values = {} - # Step count = max array length over metrics: per-step recorders give (T,…), - # per-episode scalars give (1,); the max avoids collapsing task_duration to 1. - demo_step_count = 0 - # cfg.metrics is a MetricsCfg configclass (one MetricTermCfg field per metric); - # the per-demo value is compute_metric_func fed a single-element list. - for metric_name, metric_cfg in registered_metrics.__dict__.items(): - recorded_metric_data = demo_group[metric_cfg.recorder_term_name][:] - raw_outcome_values[metric_name] = metric_cfg.compute_metric_func( - [recorded_metric_data], **metric_cfg.params - ) - demo_step_count = max(demo_step_count, len(recorded_metric_data)) - # task_duration: seconds before termination (short for fast successes, max for - # timeouts) — a continuous outcome beyond the binary metrics. - if demo_step_count > 0: - raw_outcome_values["task_duration"] = float(demo_step_count) * env_step_dt - outcome_values = metrics_to_plain_python_types(raw_outcome_values) - summary_row = { - "job_name": job.name, - "episode_idx": demo_index, - "arena_env_args": arena_env_args_snapshot, - "outcomes": outcome_values, - } - jsonl_output.write(json.dumps(summary_row) + "\n") - rows_written += 1 - - return rows_written + with open(output_path, "a", encoding="utf-8") as jsonl_output: + for episode_index, episode_metrics in enumerate(per_episode_metrics): + summary_row = { + "job_name": job.name, + "episode_idx": episode_index, + "arena_env_args": arena_env_args_snapshot, + "outcomes": metrics_to_plain_python_types(episode_metrics), + } + jsonl_output.write(json.dumps(summary_row) + "\n") + + return len(per_episode_metrics) From 4c536192f47482a39ddf4be8ddddafdaa8303e73 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 17:10:04 +0200 Subject: [PATCH 28/74] Rewrite compute_per_episode with explicit loops Replace the nested dict-in-list comprehension (repeated zip, inline [...][episode_index] indexing) with named loops that spell out the by-metric -> by-episode transpose. Behaviour unchanged. Signed-off-by: Clemens Volk --- isaaclab_arena/metrics/metrics_manager.py | 26 +++++++++++++---------- 1 file changed, 15 insertions(+), 11 deletions(-) diff --git a/isaaclab_arena/metrics/metrics_manager.py b/isaaclab_arena/metrics/metrics_manager.py index f3fec48d4a..6a3f9b102b 100644 --- a/isaaclab_arena/metrics/metrics_manager.py +++ b/isaaclab_arena/metrics/metrics_manager.py @@ -74,17 +74,21 @@ def compute_per_episode(self) -> list[dict[str, Any]]: A list with one metric dict per episode, in recorded order. """ dataset_path = get_metric_recorder_dataset_path(self._env) - per_term_episode_arrays = { + num_episodes = get_num_episodes(dataset_path) + + # Recorded data arrives grouped by metric (each term -> one array per episode). + # Read it once here, then transpose into one metric dict per episode below. + episode_arrays_by_term = { term_name: get_recorded_metric_data(dataset_path, term_cfg.recorder_term_name) for term_name, term_cfg in zip(self._term_names, self._term_cfgs) } - num_episodes = get_num_episodes(dataset_path) - return [ - { - term_name: term_cfg.compute_metric_func( - [per_term_episode_arrays[term_name][episode_index]], **term_cfg.params - ) - for term_name, term_cfg in zip(self._term_names, self._term_cfgs) - } - for episode_index in range(num_episodes) - ] + + per_episode_metrics: list[dict[str, Any]] = [] + for episode_index in range(num_episodes): + episode_metrics: dict[str, Any] = {} + for term_name, term_cfg in zip(self._term_names, self._term_cfgs): + # compute_metric_func reduces a list of per-episode arrays; give it just this one. + episode_array = episode_arrays_by_term[term_name][episode_index] + episode_metrics[term_name] = term_cfg.compute_metric_func([episode_array], **term_cfg.params) + per_episode_metrics.append(episode_metrics) + return per_episode_metrics From c2142d59ddac74fa3622765bae3cfab551d87fbd Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 17:31:03 +0200 Subject: [PATCH 29/74] Rename generate_report CLI module to generate_pdf_report MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Match the command name to what it produces (and to the generate_pdf_report function it wraps). Pure rename — nothing imports the module. Signed-off-by: Clemens Volk --- .../sensitivity/{generate_report.py => generate_pdf_report.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename isaaclab_arena/analysis/sensitivity/{generate_report.py => generate_pdf_report.py} (100%) diff --git a/isaaclab_arena/analysis/sensitivity/generate_report.py b/isaaclab_arena/analysis/sensitivity/generate_pdf_report.py similarity index 100% rename from isaaclab_arena/analysis/sensitivity/generate_report.py rename to isaaclab_arena/analysis/sensitivity/generate_pdf_report.py From 52d615b81b91da5239dde967817abae4602c4812 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 17:40:54 +0200 Subject: [PATCH 30/74] Rework sensitivity docstrings to be concise and self-contained Across the analyzers, dataset, factory and plotting: - drop Caveats sections and future-work asides - name the fact a docstring relies on instead of gesturing at it - remove cross-analyzer / alternative-approach comparisons - tighten to short descriptions of what each thing does and returns - move code-behaviour notes (lazy sbi imports, sample/round/tally, 1D-vs-multi-dim evaluation) out of docstrings into code comments Signed-off-by: Clemens Volk --- .../analysis/sensitivity/analyzer_base.py | 25 +++---- .../analysis/sensitivity/dataset.py | 67 ++++++------------- .../sensitivity/empirical_analyzer.py | 59 ++++------------ .../analysis/sensitivity/factory.py | 24 +++---- .../analysis/sensitivity/plotting.py | 18 +---- .../sensitivity/posterior_analyzer.py | 64 ++++++------------ 6 files changed, 73 insertions(+), 184 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/analyzer_base.py b/isaaclab_arena/analysis/sensitivity/analyzer_base.py index ee4cda6789..cb2b2b536e 100644 --- a/isaaclab_arena/analysis/sensitivity/analyzer_base.py +++ b/isaaclab_arena/analysis/sensitivity/analyzer_base.py @@ -12,14 +12,10 @@ class BaseAnalyzer(ABC): - """Abstract base — owns state validation and the abstract posterior-query surface. - - Subclasses must implement: - - ``fit`` — train (or no-op) so queries can be called afterwards. - - ``categorical_marginal_probs`` — return ``P(category | outcome)`` for a categorical factor. - Continuous-factor queries (``continuous_marginal_density``) live on the analyzers that - provide them (``PosteriorAnalyzer`` and ``KDEAnalyzer``); the categorical-only analyzers - never need them by construction. + """Abstract base for sensitivity analyzers. + + Validates the dataset and outcome on construction. Subclasses implement ``fit`` and + ``categorical_marginal_probs``. """ def __init__(self, dataset: SensitivityDataset, outcome_name: str): @@ -32,22 +28,17 @@ def __init__(self, dataset: SensitivityDataset, outcome_name: str): @abstractmethod def fit(self, training_batch_size: int = 50) -> None: - """Train the posterior (or no-op for empirical) so queries can be called afterwards. + """Prepare the analyzer so its query methods can be called. - For NPE/MNPE this trains a neural density estimator on ``(theta, x_selected)``, - where ``x_selected`` is the single outcome column named by ``outcome_name``. For - the empirical analyzer this is a no-op — the categorical posterior is computed - directly from the data at query time. + An implementation may train an estimator or do nothing if its queries read from + the data directly. """ @abstractmethod def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: """Return ``P(category | outcome=outcome_value)`` for one categorical factor. - Output is a 1D numpy array of length ``len(factor.choices)`` whose entries sum to 1. - For posterior analyzers this is computed by sampling the trained posterior and - counting category frequencies; for the empirical analyzer it's the normalized - per-category empirical success rate. + The result is a 1D numpy array of length ``len(factor.choices)`` summing to 1. """ def _factor_spec(self, factor_name: str) -> FactorSpec: diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py index aa00f12600..2ed6653623 100644 --- a/isaaclab_arena/analysis/sensitivity/dataset.py +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -117,12 +117,11 @@ def total_factor_dim(self) -> int: @property def factor_columns(self) -> dict[str, slice]: - """Map factor name → column slice in theta. + """Map factor name → its column slice in theta. - Continuous factors occupy the leading columns (their ``dim`` columns each), then - each categorical factor occupies one trailing column. This continuous-first - ordering matches sbi's MNPE convention so the same theta layout works for both - NPE (all-continuous) and MNPE (mixed). + Continuous factors occupy the leading columns (``dim`` each), then each categorical + factor occupies one trailing column. This continuous-first layout is what sbi's + mixed density estimator expects. """ continuous_factors = [factor for factor in self.factors if factor.type == "continuous"] categorical_factors = [factor for factor in self.factors if factor.type == "categorical"] @@ -136,19 +135,10 @@ def factor_columns(self) -> dict[str, slice]: class SensitivityDataset: - """Combines a ``factors.yaml`` schema with an ``episode_summary.jsonl`` data file. - - On construction: - 1. Parses the schema (factors + outcomes + slice metadata). - 2. Loads the JSONL rows (one row per episode). - 3. Validates that every row contains all declared factor and outcome keys. - 4. Fills any missing continuous ranges by inferring from observed min/max so the - analyzer can always trust ``schema.factors[i].range`` to be populated. - 5. Builds the ``theta`` and ``x`` tensors that sbi (or the empirical analyzer) - will consume. - - The four public attributes used by the analyzer (``theta``, ``x``, ``prior``, - ``factor_columns``) are properties — recomputed lazily where appropriate. + """A ``factors.yaml`` schema paired with its ``episode_summary.jsonl`` rows. + + Parses and validates both on construction, then exposes the ``theta`` (factors), + ``x`` (outcomes), ``prior`` and ``factor_columns`` an analyzer consumes. """ def __init__(self, factors_yaml: str | Path, jsonl_path: str | Path): @@ -165,16 +155,10 @@ def __init__(self, factors_yaml: str | Path, jsonl_path: str | Path): self._x = self._build_outcome_tensor() def _validate_rows(self, jsonl_path: str | Path) -> None: - """Assert every JSONL row carries the keys declared in the schema. - - The writer logs the *entire* arena_env_args dict per row, so the loader only - requires that the schema's declared factor names are a *subset* of what's in - ``row["arena_env_args"]`` — extra keys (other arena_env_args we don't analyze) - are fine and ignored. Same superset-not-equality check for outcomes. + """Assert every JSONL row carries the schema's declared factor and outcome keys. - Catches the most common authoring mistake: a factor declared in factors.yaml - that the eval didn't actually vary or log. Surfaces a clear error pointing at - the first offending row. + The declared names need only be a subset of each row's ``arena_env_args`` / + ``outcomes``; extra keys are ignored. Raises pointing at the first offending row. """ expected_factor_names = {factor.name for factor in self.schema.factors} expected_outcome_names = {outcome.name for outcome in self.schema.outcomes} @@ -194,12 +178,9 @@ def _validate_rows(self, jsonl_path: str | Path) -> None: ), f"Row {row_index} of {jsonl_path} missing outcomes {sorted(missing_outcome_names)}" def _infer_missing_factor_ranges(self) -> None: - """For any continuous factor without a declared ``range``, fill it from observed data. + """Fill any continuous factor's missing ``range`` from the observed min/max. - The prior bounds default to ``[min(values), max(values)]`` over the JSONL. Users - who want a principled prior (e.g. matching the variation system's declared - ``Uniform(low, high)``) should hand-author ``range`` in factors.yaml; that value - takes precedence and this method skips them. + A ``range`` declared in factors.yaml takes precedence and is left untouched. """ for factor in self.schema.factors: if factor.type != "continuous" or factor.range is not None: @@ -215,11 +196,8 @@ def _infer_missing_factor_ranges(self) -> None: def _build_factor_tensor(self) -> torch.Tensor: """Assemble the per-episode factor matrix ``theta``. - Layout: continuous factors fill the leading columns (one column per dim), then - each categorical factor fills one trailing column. Categorical values are - encoded as ``float32`` integers ``0..num_choices-1`` per the index in - ``FactorSpec.choices`` — sbi's MNPE expects exactly this layout (continuous-first, - discrete columns as floats, the density estimator handles them as discrete). + Continuous columns first (one per dim), then one column per categorical factor + with its value integer-coded as a ``float32`` index into ``FactorSpec.choices``. """ continuous_factors = [factor for factor in self.schema.factors if factor.type == "continuous"] categorical_factors = [factor for factor in self.schema.factors if factor.type == "categorical"] @@ -306,18 +284,13 @@ def has_categorical_factors(self) -> bool: @property def prior(self): - """The uniform prior over all factor dims that the analyzer assumes. - - Built as a single ``sbi.utils.BoxUniform`` over the concatenated bounds in - continuous-first / categorical-after order: - - Continuous factor → uses the declared (or inferred) ``[low, high]`` per dim. - - Categorical factor → uses ``[0, num_choices - 1]`` (the integer codes from - ``_build_factor_tensor``); sbi MNPE's mixed density estimator treats them as - discrete from there. + """Uniform prior (``sbi.utils.BoxUniform``) over all factor dims. - sbi is imported lazily so loading the dataset doesn't pay the sbi import cost - unless the analyzer actually runs. + Continuous factors use their ``[low, high]`` range; categorical factors use + ``[0, num_choices - 1]`` over their integer codes, in continuous-first order. """ + # Import sbi lazily so loading the dataset does not pay the sbi import cost + # unless an analyzer actually runs. from sbi.utils import BoxUniform low_bounds: list[float] = [] diff --git a/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py b/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py index c91b803b73..476929060c 100644 --- a/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py @@ -12,21 +12,12 @@ class EmpiricalAnalyzer(BaseAnalyzer): - """Abstract base for the direct (non-neural) analyzers. + """Abstract base for analyzers that read the posterior directly from the data. - Subclasses exploit the same fact: under a uniform prior, - ``P(theta | success) ∝ P(success | theta)``, so the posterior is read directly off - the data — no neural density estimator, no parametric shape constraint. They differ - only in factor type, which dictates the estimator: - - - :class:`KDEAnalyzer` (continuous) — a Gaussian KDE over the successful-theta - samples: the empirical measure, kernel-smoothed because a raw continuous - empirical measure is a sum of Diracs. The supported subclass. - - ``FrequencyTableAnalyzer`` (categorical) — the per-category empirical success - rate (the raw empirical measure). Not part of this MVP; plugs in here unchanged. - - Outcome is treated as binary: an episode is a "success" when its selected outcome - column is ``>= SUCCESS_THRESHOLD``. + Under a uniform prior, ``P(theta | success) ∝ P(success | theta)``, so the + distribution of successful-theta samples is the posterior. Outcome is treated as + binary: an episode is a success when its selected outcome column is + ``>= SUCCESS_THRESHOLD``. """ SUCCESS_THRESHOLD = 0.5 @@ -40,30 +31,11 @@ def _success_mask(self) -> np.ndarray: class KDEAnalyzer(EmpiricalAnalyzer): - """KDE-based analyzer for the 1-continuous-factor + binary-outcome case. - - Under a uniform prior and a binary outcome, Bayes' rule reduces to - ``P(theta | success=1) ∝ P(success=1 | theta) · P(theta) = P(success=1 | theta) · const``. - The empirical density of *successful*-theta samples (i.e. rows where the chosen outcome - is 1) is directly proportional to ``P(success=1 | theta)``, and a Gaussian KDE over - those samples gives a smoothed estimate of that conditional density. No neural fit, - no Gaussian-shape constraint. - - This is the right primitive for the 1-continuous-factor + binary-outcome case: - sbi NPE forces a Gaussian shape when theta is 1D — biasing the recovered peak toward - the mean of successful-theta values rather than the true mode of the success curve. - KDE has no such constraint and recovers multi-modal / plateau / skewed shapes faithfully. - - Sits under the shared :class:`EmpiricalAnalyzer` base; its categorical sibling - ``FrequencyTableAnalyzer`` (same trick via frequency counts) is not part of this MVP. - For mixed-factor workloads, :func:`make_analyzer` dispatches to ``MNPEAnalyzer``. - - Caveats: - - Bandwidth is scipy's Scott rule default; haven't tuned for non-uniform sample - distributions or sparse data. May over-smooth the empirical mode. - - Only ``continuous_marginal_density`` is implemented and only for - ``outcome_value >= 0.5`` (i.e. success conditioning). Failure conditioning would - require fitting a second KDE over failed-theta samples; left out for simplicity. + """Analyzer for a single continuous factor with a binary outcome. + + Fits a Gaussian KDE over the theta values of successful episodes. Under a uniform + prior this density is proportional to ``P(success | theta)``, so its shape shows + which factor values drove success. """ def __init__(self, dataset: SensitivityDataset, outcome_name: str): @@ -79,7 +51,7 @@ def __init__(self, dataset: SensitivityDataset, outcome_name: str): self._num_total_samples = 0 def fit(self, training_batch_size: int = 50) -> None: - """Fit a Gaussian KDE on the successful-theta samples (no neural network involved).""" + """Fit a Gaussian KDE over the theta values of successful episodes.""" from scipy.stats import gaussian_kde theta_values = self.dataset.theta[:, 0].cpu().numpy() @@ -111,13 +83,10 @@ def fit(self, training_batch_size: int = 50) -> None: def continuous_marginal_density( self, factor_name: str, outcome_value: float, num_grid_points: int ) -> tuple[np.ndarray, np.ndarray]: - """Evaluate the KDE-based posterior over the factor's prior range. + """Evaluate the posterior density over the factor's range on a uniform grid. - ``outcome_value >= 0.5`` is treated as "success conditioning" (the only case - currently supported); the KDE is evaluated on a uniform grid spanning the - declared factor range. ``outcome_value < 0.5`` (failure conditioning) returns - a uniform density as a placeholder — extend by fitting a second KDE on failed - samples if/when that case is needed. + Returns ``(grid, density)``. Success conditioning (``outcome_value >= + SUCCESS_THRESHOLD``) returns the fitted KDE; otherwise a uniform density. """ factor_spec = self._factor_spec(factor_name) assert factor_spec.type == "continuous", "KDEAnalyzer only handles continuous factors" diff --git a/isaaclab_arena/analysis/sensitivity/factory.py b/isaaclab_arena/analysis/sensitivity/factory.py index 78fac8d9af..6a05f4b6ce 100644 --- a/isaaclab_arena/analysis/sensitivity/factory.py +++ b/isaaclab_arena/analysis/sensitivity/factory.py @@ -13,25 +13,17 @@ def make_analyzer(dataset: SensitivityDataset, outcome_name: str) -> BaseAnalyzer: - """Construct the right analyzer for the dataset's factor mix and outcome shape. + """Construct the analyzer matching the dataset's factor mix and outcome. - This MVP supports two factor mixes, one analyzer from each family: - - any continuous + any categorical (mixed) → :class:`MNPEAnalyzer` - - 1 continuous + 0 categorical AND a binary outcome → :class:`KDEAnalyzer` - (avoids sbi NPE's 1D-theta Gaussian-shape constraint; exact under uniform prior) + - mixed continuous + categorical → :class:`MNPEAnalyzer` + - one continuous factor with a binary outcome → :class:`KDEAnalyzer` - Other factor mixes (pure-categorical, or multiple/non-binary continuous) are not - supported yet; they are asserted against here so the gap fails loudly instead of - mis-dispatching. - - The binary check reads the outcome column off the dataset rather than the schema, - since outcome ``type: float`` in factors.yaml covers both continuous durations and - binary 0/1 success rates. - - Analyzer classes are imported lazily so that importing this module (and the package - that re-exports it, which happens on the eval-time ``episode_writer`` path) doesn't - pull in torch/sbi until analysis runs. + Other mixes are asserted against so an unsupported case fails loudly. The binary + check reads the outcome values off the dataset, since factors.yaml types all + outcomes as float. """ + # Import lazily so importing this module (and the package re-export on the eval-time + # episode_writer path) does not pull in torch/sbi until an analysis actually runs. from isaaclab_arena.analysis.sensitivity.empirical_analyzer import KDEAnalyzer from isaaclab_arena.analysis.sensitivity.posterior_analyzer import MNPEAnalyzer diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index 131011a67e..7b595fe246 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -34,12 +34,7 @@ def draw_marginal( ) -> None: """Draw ``factor_name``'s marginal posterior onto ``ax``, dispatching by factor type. - Sets axis labels, scale, legend and grid but NOT the title — the caller titles the - Axes (a standalone plot wants the full slice block; a grid cell wants a compact label). - - For continuous factors, the analyzer must expose ``continuous_marginal_density`` - (``PosteriorAnalyzer`` and ``KDEAnalyzer`` do); the categorical-only analyzers reject - continuous factors at construction time, so they never reach this branch. + Sets axis labels, legend and grid but not the title — the caller titles the Axes. """ factor_spec = analyzer._factor_spec(factor_name) if factor_spec.type == "continuous": @@ -129,15 +124,8 @@ def _draw_categorical_marginal( ) -> None: """Draw a categorical factor's marginal onto ``ax`` as side-by-side bars per category. - The blue bar (left of each category) is the analyzer's ``P(category | outcome)``. - The green bar (right of each category) is the *empirical* per-category outcome rate - — independent of the analyzer's posterior, computed directly from the raw data. - For a direct empirical analyzer the two agree exactly (up to normalization); for - a posterior-based analyzer (e.g. ``MNPEAnalyzer``) they may differ slightly if the - model smooths. - - Each green bar is annotated with the sample count ``n`` for that category, so the - user can see how trustworthy each bar is. + Blue bar: the analyzer's ``P(category | outcome)``. Green bar: the empirical + per-category outcome rate from the raw data, annotated with its sample count ``n``. """ assert factor_spec.choices is not None choices = factor_spec.choices diff --git a/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py b/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py index 41a458e254..0409bc3322 100644 --- a/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py @@ -13,15 +13,11 @@ class PosteriorAnalyzer(BaseAnalyzer): - """Common base for the sbi-driven analyzers. + """Base for sbi-driven analyzers that fit a neural posterior estimator. - Subclasses differ only in *which* sbi inference class they instantiate (via - ``_inference_cls``); everything else (training loop, posterior storage, density and - sample queries) is shared. ``MNPEAnalyzer`` is the supported subclass; the - all-continuous ``NPEAnalyzer`` is not part of this MVP but plugs in here unchanged. - - After ``fit()`` returns, ``self.posterior`` is an sbi posterior object that supports - ``posterior.sample(shape, x=...)`` and ``posterior.log_prob(theta, x=...)``. + Subclasses choose the sbi inference class via ``_inference_cls``; the training loop + and the density/sample queries are shared. After ``fit()``, ``self.posterior`` is an + sbi posterior supporting ``sample(shape, x=...)`` and ``log_prob(theta, x=...)``. """ def __init__(self, dataset: SensitivityDataset, outcome_name: str): @@ -29,12 +25,7 @@ def __init__(self, dataset: SensitivityDataset, outcome_name: str): self.posterior = None def _inference_cls(self): - """Return the sbi inference *class* to train with (e.g. ``sbi.inference.NPE``). - - Subclass-specific: ``NPEAnalyzer`` returns ``NPE``, ``MNPEAnalyzer`` returns - ``MNPE``. The lazy import of sbi lives in the subclass so callers don't pay the - (heavy) sbi import cost until they actually fit. - """ + """Return the sbi inference *class* to train with (e.g. ``sbi.inference.MNPE``).""" raise NotImplementedError("PosteriorAnalyzer subclasses must implement _inference_cls") def _make_inference(self): @@ -42,13 +33,9 @@ def _make_inference(self): return self._inference_cls()(prior=self.dataset.prior) def fit(self, training_batch_size: int = 50) -> None: - """Train the chosen sbi estimator on ``(theta, x_selected)`` and stash the posterior. + """Train the sbi estimator and store the posterior on ``self``. - Steps: - 1. Slice ``self.dataset.x`` to the single outcome column named by ``outcome_name``. - 2. Instantiate the sbi inference object via ``_make_inference``. - 3. Append the simulations and train. - 4. Build a posterior object from the trained estimator and store it on ``self``. + Conditions on the single outcome column named by ``outcome_name``. """ outcome_column_index = self.dataset.outcome_columns[self.outcome_name] selected_outcome_column = self.dataset.x[:, outcome_column_index : outcome_column_index + 1] @@ -66,17 +53,9 @@ def fit(self, training_batch_size: int = 50) -> None: def continuous_marginal_density( self, factor_name: str, outcome_value: float, num_grid_points: int ) -> tuple[np.ndarray, np.ndarray]: - """Evaluate ``P(factor_value | outcome=outcome_value)`` over the factor's prior range. + """Evaluate ``P(factor_value | outcome=outcome_value)`` over the factor's range. - Returns ``(grid, density)`` as numpy arrays of length ``num_grid_points``, suitable - for plotting as a smooth curve. - - Two evaluation paths depending on whether other factors are present: - - **1D theta** (the only declared factor is this one): evaluate - ``posterior.log_prob`` directly on a regular grid — exact, no sampling. - - **Multi-dim theta**: sample the posterior at the given outcome value, extract - this factor's column, and histogram-then-interpolate to a grid. This - marginalizes over the other factor dims implicitly. + Returns ``(grid, density)`` as numpy arrays of length ``num_grid_points``. """ assert self.posterior is not None, "Call fit() before querying the posterior" factor_spec = self._factor_spec(factor_name) @@ -92,12 +71,15 @@ def continuous_marginal_density( range_low, range_high = factor_spec.range[0] if self.dataset.theta.shape[1] == 1: + # This is the only factor: evaluate log_prob directly on a grid (exact, no sampling). grid_tensor = torch.linspace(range_low, range_high, num_grid_points, dtype=torch.float32).unsqueeze(1) with torch.no_grad(): log_probabilities = self.posterior.log_prob(grid_tensor, x=observed_outcome) density_numpy = torch.exp(log_probabilities).cpu().numpy() grid_numpy = grid_tensor.squeeze(-1).cpu().numpy() else: + # Other factors present: sample, take this factor's column (marginalizing the + # rest), then histogram-and-interpolate onto the grid. with torch.no_grad(): posterior_samples = self.posterior.sample((10_000,), x=observed_outcome) factor_column_samples = posterior_samples[:, factor_column_slice].squeeze(-1).cpu().numpy() @@ -110,12 +92,9 @@ def continuous_marginal_density( return grid_numpy, density_numpy def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: - """Estimate ``P(category | outcome)`` by sampling the trained posterior. + """Estimate ``P(category | outcome=outcome_value)`` by sampling the trained posterior. - Draws ``num_samples`` from ``posterior(theta | x=outcome_value)``, extracts the - factor's column (which sbi returns as floats over the BoxUniform support), rounds - to the nearest integer in ``[0, num_choices - 1]``, and tallies frequencies. - Result is a length-``num_choices`` numpy array that sums to 1. + Returns a length-``num_choices`` numpy array that sums to 1. """ assert self.posterior is not None, "Call fit() before querying the posterior" factor_spec = self._factor_spec(factor_name) @@ -127,25 +106,22 @@ def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num observed_outcome = torch.tensor([outcome_value], dtype=torch.float32) with torch.no_grad(): posterior_samples = self.posterior.sample((num_samples,), x=observed_outcome) + # sbi returns the categorical column as floats over the BoxUniform support; round + # to the nearest code in [0, num_choices - 1] and tally into normalized frequencies. factor_column_samples = posterior_samples[:, factor_column_slice].squeeze(-1).cpu().numpy() clipped_codes = np.clip(np.round(factor_column_samples), 0, num_choices - 1).astype(int) return np.bincount(clipped_codes, minlength=num_choices) / num_samples class MNPEAnalyzer(PosteriorAnalyzer): - """Mixed Neural Posterior Estimation analyzer for schemas with at least one of each type. - - Use this when the schema mixes continuous and categorical factors. Internally trains - ``sbi.inference.MNPE``, whose mixed density estimator routes continuous theta columns - through a normalizing flow while routing categorical columns through a categorical - mass estimator. The continuous-first / categorical-after column ordering in - ``factor_columns`` matches MNPE's expected layout exactly. + """Analyzer for schemas that mix continuous and categorical factors. - sbi MNPE 0.26 requires at least one continuous theta column, so pure-categorical - schemas are not supported in this MVP (``make_analyzer`` asserts). + Trains ``sbi.inference.MNPE``, whose mixed density estimator handles continuous and + categorical theta columns together. Requires at least one continuous factor. """ def _inference_cls(self): + # Import sbi lazily — it is heavy and only needed once an analysis actually fits. from sbi.inference import MNPE return MNPE From ffa1473e6595fbf15f87c346859163add063601b Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 17:53:39 +0200 Subject: [PATCH 31/74] Empty the sensitivity package __init__ MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Matches the repo convention (sibling analysis/, metrics/, root inits are header-only). The lone re-export (make_analyzer) had a single consumer, pdf_report, a sibling in the same package — repoint it at factory directly. Also drop the now-stale episode_writer reference from the factory lazy-import comment (episode_writer no longer imports this package). Signed-off-by: Clemens Volk --- isaaclab_arena/analysis/sensitivity/__init__.py | 4 ---- isaaclab_arena/analysis/sensitivity/factory.py | 4 ++-- isaaclab_arena/analysis/sensitivity/pdf_report.py | 2 +- 3 files changed, 3 insertions(+), 7 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/__init__.py b/isaaclab_arena/analysis/sensitivity/__init__.py index 56be864a91..16ea4c2183 100644 --- a/isaaclab_arena/analysis/sensitivity/__init__.py +++ b/isaaclab_arena/analysis/sensitivity/__init__.py @@ -2,7 +2,3 @@ # All rights reserved. # # SPDX-License-Identifier: Apache-2.0 - -from isaaclab_arena.analysis.sensitivity.factory import make_analyzer - -__all__ = ["make_analyzer"] diff --git a/isaaclab_arena/analysis/sensitivity/factory.py b/isaaclab_arena/analysis/sensitivity/factory.py index 6a05f4b6ce..4233382c21 100644 --- a/isaaclab_arena/analysis/sensitivity/factory.py +++ b/isaaclab_arena/analysis/sensitivity/factory.py @@ -22,8 +22,8 @@ def make_analyzer(dataset: SensitivityDataset, outcome_name: str) -> BaseAnalyze check reads the outcome values off the dataset, since factors.yaml types all outcomes as float. """ - # Import lazily so importing this module (and the package re-export on the eval-time - # episode_writer path) does not pull in torch/sbi until an analysis actually runs. + # Import lazily so importing this module stays light — torch/sbi load only when an + # analysis actually runs. from isaaclab_arena.analysis.sensitivity.empirical_analyzer import KDEAnalyzer from isaaclab_arena.analysis.sensitivity.posterior_analyzer import MNPEAnalyzer diff --git a/isaaclab_arena/analysis/sensitivity/pdf_report.py b/isaaclab_arena/analysis/sensitivity/pdf_report.py index 0ac6c4c3ec..1a6c3860b8 100644 --- a/isaaclab_arena/analysis/sensitivity/pdf_report.py +++ b/isaaclab_arena/analysis/sensitivity/pdf_report.py @@ -8,8 +8,8 @@ import numpy as np from pathlib import Path -from isaaclab_arena.analysis.sensitivity import make_analyzer from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset +from isaaclab_arena.analysis.sensitivity.factory import make_analyzer from isaaclab_arena.analysis.sensitivity.plotting import draw_marginal From be64ae3f901147fb3b943e9c39f546a9393b9d6a Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 18:13:40 +0200 Subject: [PATCH 32/74] Label binary-outcome rug as = 0 / = 1, not >= 0.5 threshold The rug split only runs inside the is_binary_outcome branch, where values are guaranteed to be {0, 1}, so the >= 0.5 / < 0.5 phrasing was a confusing way to write = 1 / = 0. The threshold still drives the mask; it just no longer leaks into the legend. Signed-off-by: Clemens Volk --- isaaclab_arena/analysis/sensitivity/plotting.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index 7b595fe246..ab7260519b 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -90,7 +90,7 @@ def _draw_continuous_marginal( marker="|", color=_SUCCESS_COLOR, s=_RUG_MARKER_SIZE, - label=f"{analyzer.outcome_name} ≥ {_SUCCESS_THRESHOLD:g} (n={success_mask.sum()})", + label=f"{analyzer.outcome_name} = 1 (n={success_mask.sum()})", ) ax.scatter( empirical_theta_values[~success_mask], @@ -98,7 +98,7 @@ def _draw_continuous_marginal( marker="|", color=_FAILURE_COLOR, s=_RUG_MARKER_SIZE, - label=f"{analyzer.outcome_name} < {_SUCCESS_THRESHOLD:g} (n={(~success_mask).sum()})", + label=f"{analyzer.outcome_name} = 0 (n={(~success_mask).sum()})", ) else: ax.scatter( From 43ac62a8c8d9eb7fee1302b4254b2af6925038d6 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 18:20:57 +0200 Subject: [PATCH 33/74] Single-source SUCCESS_THRESHOLD and clarify continuous-factor error - Hoist the binary-success threshold to a shared SUCCESS_THRESHOLD in analyzer_base; empirical_analyzer and plotting now import the one constant instead of each defining their own 0.5, so the analyzer's success split and the rug's success split can no longer drift apart. - draw_marginal's NotImplementedError named PosteriorAnalyzer (NPE/MNPE) as the requirement, but KDEAnalyzer (the MVP's single-continuous path) also implements continuous_marginal_density. State the missing capability plainly instead of naming analyzer families. Signed-off-by: Clemens Volk --- isaaclab_arena/analysis/sensitivity/analyzer_base.py | 7 +++++++ .../analysis/sensitivity/empirical_analyzer.py | 9 +++------ isaaclab_arena/analysis/sensitivity/plotting.py | 10 ++++++---- 3 files changed, 16 insertions(+), 10 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/analyzer_base.py b/isaaclab_arena/analysis/sensitivity/analyzer_base.py index cb2b2b536e..1d4151dbb3 100644 --- a/isaaclab_arena/analysis/sensitivity/analyzer_base.py +++ b/isaaclab_arena/analysis/sensitivity/analyzer_base.py @@ -10,6 +10,13 @@ from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec, SensitivityDataset +SUCCESS_THRESHOLD = 0.5 +"""Outcome value at or above which a binary-outcome episode counts as a success. + +Shared by the analyzers (which successful-theta samples to fit) and the plotting (how +to split the rug), so the two cannot disagree on what "success" means. +""" + class BaseAnalyzer(ABC): """Abstract base for sensitivity analyzers. diff --git a/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py b/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py index 476929060c..e2d08a3eb3 100644 --- a/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py @@ -7,7 +7,7 @@ import numpy as np -from isaaclab_arena.analysis.sensitivity.analyzer_base import BaseAnalyzer +from isaaclab_arena.analysis.sensitivity.analyzer_base import SUCCESS_THRESHOLD, BaseAnalyzer from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset @@ -20,14 +20,11 @@ class EmpiricalAnalyzer(BaseAnalyzer): ``>= SUCCESS_THRESHOLD``. """ - SUCCESS_THRESHOLD = 0.5 - """Outcome value at or above which an episode counts as a success.""" - def _success_mask(self) -> np.ndarray: """Boolean array over episodes: True where the selected outcome counts as a success.""" outcome_column_index = self.dataset.outcome_columns[self.outcome_name] outcome_values = self.dataset.x[:, outcome_column_index].cpu().numpy() - return outcome_values >= self.SUCCESS_THRESHOLD + return outcome_values >= SUCCESS_THRESHOLD class KDEAnalyzer(EmpiricalAnalyzer): @@ -96,7 +93,7 @@ def continuous_marginal_density( range_low, range_high = factor_spec.range[0] grid = np.linspace(range_low, range_high, num_grid_points) - if outcome_value < self.SUCCESS_THRESHOLD or self._kde is None: + if outcome_value < SUCCESS_THRESHOLD or self._kde is None: uniform_density = 1.0 / max(range_high - range_low, 1e-9) return grid, np.full_like(grid, uniform_density) return grid, self._kde(grid) diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index ab7260519b..3e809971c3 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -8,6 +8,8 @@ import numpy as np from typing import TYPE_CHECKING +from isaaclab_arena.analysis.sensitivity.analyzer_base import SUCCESS_THRESHOLD + if TYPE_CHECKING: from isaaclab_arena.analysis.sensitivity.analyzer_base import BaseAnalyzer from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec @@ -18,7 +20,6 @@ _SUCCESS_COLOR = "seagreen" # outcome achieved: success rug / empirical-rate bar _FAILURE_COLOR = "firebrick" # outcome not achieved: failure rug _NEUTRAL_COLOR = "slategray" # rug for non-binary outcomes (no success/failure split) -_SUCCESS_THRESHOLD = 0.5 # outcome >= this counts as a success _RUG_MARKER_SIZE = 80 # scatter marker size for empirical rug ticks _RUG_SUCCESS_OFFSET = -0.05 # rug y-offset (× density.max()) for successes / neutral ticks _RUG_FAILURE_OFFSET = -0.10 # rug y-offset (× density.max()) for failures @@ -40,7 +41,8 @@ def draw_marginal( if factor_spec.type == "continuous": if not hasattr(analyzer, "continuous_marginal_density"): raise NotImplementedError( - f"{type(analyzer).__name__} cannot plot continuous factors; expected a PosteriorAnalyzer (NPE/MNPE)." + f"{type(analyzer).__name__} cannot plot continuous factors:" + " it does not implement continuous_marginal_density." ) _draw_continuous_marginal(ax, analyzer, factor_spec, outcome_value, num_grid_points) elif factor_spec.type == "categorical": @@ -83,7 +85,7 @@ def _draw_continuous_marginal( # Continuous outcomes: the threshold is meaningless, so show one neutral rug. is_binary_outcome = set(empirical_outcomes.flatten().tolist()).issubset({0.0, 1.0}) if is_binary_outcome: - success_mask = empirical_outcomes >= _SUCCESS_THRESHOLD + success_mask = empirical_outcomes >= SUCCESS_THRESHOLD ax.scatter( empirical_theta_values[success_mask], np.full(success_mask.sum(), _RUG_SUCCESS_OFFSET * density.max()), @@ -145,7 +147,7 @@ def _draw_categorical_marginal( category_mask = empirical_theta_codes == code empirical_counts[code] = int(category_mask.sum()) if category_mask.any(): - empirical_rates[code] = float((empirical_outcomes[category_mask] >= _SUCCESS_THRESHOLD).mean()) + empirical_rates[code] = float((empirical_outcomes[category_mask] >= SUCCESS_THRESHOLD).mean()) bar_x_positions = np.arange(num_choices) bar_width = 0.4 From a405da0a55200dce8308b958c920ef443c9fd9e2 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Mon, 8 Jun 2026 18:20:57 +0200 Subject: [PATCH 34/74] Match episode_idx loop variable to the emitted JSONL key The loop used episode_index while writing the key as episode_idx. Align the variable to the long-established on-disk key (every existing dataset uses episode_idx) rather than churn the schema. Signed-off-by: Clemens Volk --- isaaclab_arena/evaluation/episode_writer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/isaaclab_arena/evaluation/episode_writer.py b/isaaclab_arena/evaluation/episode_writer.py index 9751d2397a..889f82b1b4 100644 --- a/isaaclab_arena/evaluation/episode_writer.py +++ b/isaaclab_arena/evaluation/episode_writer.py @@ -50,10 +50,10 @@ def write_episode_summaries(env, job: Job, output_path: str | Path) -> int: output_path = Path(output_path) output_path.parent.mkdir(parents=True, exist_ok=True) with open(output_path, "a", encoding="utf-8") as jsonl_output: - for episode_index, episode_metrics in enumerate(per_episode_metrics): + for episode_idx, episode_metrics in enumerate(per_episode_metrics): summary_row = { "job_name": job.name, - "episode_idx": episode_index, + "episode_idx": episode_idx, "arena_env_args": arena_env_args_snapshot, "outcomes": metrics_to_plain_python_types(episode_metrics), } From b6c2d4be3c3c1fe2540f4d9beef0e829339f3557 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Wed, 10 Jun 2026 12:13:59 +0200 Subject: [PATCH 35/74] Add empirical success-rate plots for binary outcomes MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit For a binary outcome vs a continuous factor, plot the per-bin empirical success rate (log-spaced bins + log axis when the factor spans many decades) with a 95% Wilson CI, read directly off a 0-1 axis. Because each bin is counted independently, this is correct regardless of how the factor was sampled — no prior/sampling-space matching needed, unlike the marginal posterior. The report dispatches binary-outcome continuous cells here and only fits an analyzer when a categorical cell or non-binary outcome needs one. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/pdf_report.py | 41 +++++++---- .../analysis/sensitivity/plotting.py | 70 ++++++++++++++++++- 2 files changed, 98 insertions(+), 13 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/pdf_report.py b/isaaclab_arena/analysis/sensitivity/pdf_report.py index 1a6c3860b8..8ad27bd90b 100644 --- a/isaaclab_arena/analysis/sensitivity/pdf_report.py +++ b/isaaclab_arena/analysis/sensitivity/pdf_report.py @@ -10,7 +10,7 @@ from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset from isaaclab_arena.analysis.sensitivity.factory import make_analyzer -from isaaclab_arena.analysis.sensitivity.plotting import draw_marginal +from isaaclab_arena.analysis.sensitivity.plotting import draw_marginal, draw_success_rate def generate_pdf_report( @@ -41,16 +41,28 @@ def generate_pdf_report( figure, axes = plt.subplots(n_rows, n_cols, figsize=(6.5 * n_cols, 4.5 * n_rows), squeeze=False) for row_index, outcome in enumerate(outcomes): - analyzer = make_analyzer(dataset, outcome.name) - print(f"[INFO] Fitting {type(analyzer).__name__} for outcome={outcome.name!r}") - analyzer.fit() + outcome_is_binary = _is_binary_outcome(dataset, outcome.name) + # The fitted analyzer is only needed for categorical cells or non-binary outcomes. + # An all-continuous binary report is answered entirely by empirical success-rate plots. + needs_analyzer = (not outcome_is_binary) or any(factor.type != "continuous" for factor in factors) + analyzer = None + if needs_analyzer: + analyzer = make_analyzer(dataset, outcome.name) + print(f"[INFO] Fitting {type(analyzer).__name__} for outcome={outcome.name!r}") + analyzer.fit() outcome_value = _default_outcome_value_for_analysis(dataset, outcome) for col_index, factor in enumerate(factors): ax = axes[row_index][col_index] - draw_marginal(ax, analyzer, factor.name, outcome_value=outcome_value) - ax.set_title( - f"{outcome.name} vs {factor.name}\n(conditioned on {outcome.name}={outcome_value:g})", fontsize=10 - ) + # Binary outcome + continuous factor: the empirical success-rate curve reads + # directly off a 0-1 axis and is correct regardless of how the factor was sampled. + if outcome_is_binary and factor.type == "continuous": + draw_success_rate(ax, dataset, factor.name, outcome.name) + ax.set_title(f"{outcome.name} vs {factor.name}", fontsize=10) + else: + draw_marginal(ax, analyzer, factor.name, outcome_value=outcome_value) + ax.set_title( + f"{outcome.name} vs {factor.name}\n(conditioned on {outcome.name}={outcome_value:g})", fontsize=10 + ) slice_info = dataset.schema.slice # Two lines so the title doesn't clip on narrow (single-factor) figures. @@ -70,14 +82,19 @@ def generate_pdf_report( return output_pdf_path +def _is_binary_outcome(dataset: SensitivityDataset, outcome_name: str) -> bool: + """True iff the outcome column only ever takes values in ``{0, 1}``.""" + outcome_column_index = dataset.outcome_columns[outcome_name] + return set(dataset.x[:, outcome_column_index].flatten().tolist()).issubset({0.0, 1.0}) + + def _default_outcome_value_for_analysis(dataset: SensitivityDataset, outcome) -> float: """Pick a sensible value to condition the posterior on for this outcome. Binary outcomes (only ``{0, 1}`` observed) → ``1.0`` (the "success" branch). Continuous outcomes → empirical median; a "typical case" value always inside the data range. """ - outcome_column_index = dataset.outcome_columns[outcome.name] - values = dataset.x[:, outcome_column_index].cpu().numpy() - if set(values.flatten().tolist()).issubset({0.0, 1.0}): + if _is_binary_outcome(dataset, outcome.name): return 1.0 - return float(np.median(values)) + outcome_column_index = dataset.outcome_columns[outcome.name] + return float(np.median(dataset.x[:, outcome_column_index].cpu().numpy())) diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index 3e809971c3..d28178c83d 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -12,7 +12,7 @@ if TYPE_CHECKING: from isaaclab_arena.analysis.sensitivity.analyzer_base import BaseAnalyzer - from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec + from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec, SensitivityDataset # Shared styling — kept in one place so the continuous and categorical drawers stay # visually consistent (same colours mean the same thing across both plot types). @@ -51,6 +51,74 @@ def draw_marginal( raise NotImplementedError(f"Unsupported factor type {factor_spec.type!r}") +def draw_success_rate( + ax, + dataset: SensitivityDataset, + factor_name: str, + outcome_name: str, + num_bins: int = 12, +) -> None: + """Plot the empirical success rate of a binary outcome against a continuous factor. + + Bins the factor (log-spaced when it spans more than ~2 decades), then plots the + per-bin success rate with a 95% Wilson confidence band on a 0-1 axis. The rate is + read straight off the y-axis, and because each bin is counted independently the + result is correct regardless of how the factor was sampled. + """ + factor_column_slice = dataset.factor_columns[factor_name] + factor_values = dataset.theta[:, factor_column_slice].squeeze(-1).cpu().numpy() + outcome_column_index = dataset.outcome_columns[outcome_name] + outcome_values = dataset.x[:, outcome_column_index].cpu().numpy() + + # Log-spaced bins when the factor is positive and spans many decades, so a wide-range + # factor (e.g. light) resolves its low end instead of collapsing into the first bin. + range_low, range_high = float(factor_values.min()), float(factor_values.max()) + use_log_axis = range_low > 0 and np.log10(range_high / range_low) > 2 + if use_log_axis: + bin_edges = np.logspace(np.log10(range_low), np.log10(range_high), num_bins + 1) + else: + bin_edges = np.linspace(range_low, range_high, num_bins + 1) + + z = 1.959963985 # 95% normal quantile, for the Wilson score interval + bin_centers, rates, ci_low, ci_high, bin_counts = [], [], [], [], [] + for bin_index in range(num_bins): + # Last bin is closed on the right so the maximum value isn't dropped. + upper = bin_edges[bin_index + 1] + in_bin = (factor_values >= bin_edges[bin_index]) & ( + factor_values <= upper if bin_index == num_bins - 1 else factor_values < upper + ) + num_in_bin = int(in_bin.sum()) + if num_in_bin == 0: + continue + num_success = int((outcome_values[in_bin] >= SUCCESS_THRESHOLD).sum()) + rate = num_success / num_in_bin + # Wilson score interval — stays inside [0, 1] even at rate 0/1 or small n. + denominator = 1 + z * z / num_in_bin + center = (rate + z * z / (2 * num_in_bin)) / denominator + margin = z / denominator * np.sqrt(rate * (1 - rate) / num_in_bin + z * z / (4 * num_in_bin * num_in_bin)) + if use_log_axis: + bin_centers.append(float(np.sqrt(bin_edges[bin_index] * upper))) + else: + bin_centers.append(float(0.5 * (bin_edges[bin_index] + upper))) + rates.append(rate) + ci_low.append(max(0.0, center - margin)) + ci_high.append(min(1.0, center + margin)) + bin_counts.append(num_in_bin) + + ax.plot(bin_centers, rates, "o-", color=_POSTERIOR_COLOR, linewidth=2, label=f"empirical {outcome_name}") + ax.fill_between(bin_centers, ci_low, ci_high, color=_POSTERIOR_COLOR, alpha=0.2, label="95% Wilson CI") + for center_x, rate, count in zip(bin_centers, rates, bin_counts): + ax.text(center_x, min(rate + 0.04, 1.04), f"n={count}", ha="center", fontsize=7, color="gray") + + if use_log_axis: + ax.set_xscale("log") + ax.set_xlabel(factor_name) + ax.set_ylabel(f"{outcome_name} (success rate)") + ax.set_ylim(0, 1.1) + ax.legend(loc="best", fontsize=9) + ax.grid(alpha=0.3) + + def _draw_continuous_marginal( ax, analyzer: BaseAnalyzer, From 89cdc1be6d3d3d3b7dcc7ec8f5d5139e7d0c8500 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Wed, 10 Jun 2026 14:05:27 +0200 Subject: [PATCH 36/74] Split continuous success-rate curves by a categorical factor draw_success_rate gains an optional group_by: when set, it overlays one success-rate curve per category over shared bins, so a light x object-style interaction is visible instead of averaged away. The report auto-splits continuous success-rate cells by a low-cardinality categorical factor when one is present. Binning logic factored into _binned_success_rate. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/pdf_report.py | 12 ++- .../analysis/sensitivity/plotting.py | 82 +++++++++++++------ 2 files changed, 68 insertions(+), 26 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/pdf_report.py b/isaaclab_arena/analysis/sensitivity/pdf_report.py index 8ad27bd90b..bb55b16a09 100644 --- a/isaaclab_arena/analysis/sensitivity/pdf_report.py +++ b/isaaclab_arena/analysis/sensitivity/pdf_report.py @@ -36,6 +36,11 @@ def generate_pdf_report( dataset = SensitivityDataset(Path(factors_yaml_path), Path(jsonl_path)) outcomes = dataset.schema.outcomes factors = dataset.schema.factors + # A low-cardinality categorical to split continuous success-rate curves by, so a + # light×object-style interaction shows per category instead of being averaged away. + split_factor = next( + (f.name for f in factors if f.type == "categorical" and f.choices and len(f.choices) <= 6), None + ) n_rows, n_cols = len(outcomes), len(factors) print(f"[INFO] PDF report: {n_rows} outcomes × {n_cols} factors ({len(dataset.rows)} episodes)") @@ -56,8 +61,11 @@ def generate_pdf_report( # Binary outcome + continuous factor: the empirical success-rate curve reads # directly off a 0-1 axis and is correct regardless of how the factor was sampled. if outcome_is_binary and factor.type == "continuous": - draw_success_rate(ax, dataset, factor.name, outcome.name) - ax.set_title(f"{outcome.name} vs {factor.name}", fontsize=10) + draw_success_rate(ax, dataset, factor.name, outcome.name, group_by=split_factor) + title = f"{outcome.name} vs {factor.name}" + if split_factor: + title += f" (split by {split_factor})" + ax.set_title(title, fontsize=10) else: draw_marginal(ax, analyzer, factor.name, outcome_value=outcome_value) ax.set_title( diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index d28178c83d..e8e223c299 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -56,14 +56,19 @@ def draw_success_rate( dataset: SensitivityDataset, factor_name: str, outcome_name: str, + group_by: str | None = None, num_bins: int = 12, ) -> None: """Plot the empirical success rate of a binary outcome against a continuous factor. - Bins the factor (log-spaced when it spans more than ~2 decades), then plots the - per-bin success rate with a 95% Wilson confidence band on a 0-1 axis. The rate is - read straight off the y-axis, and because each bin is counted independently the - result is correct regardless of how the factor was sampled. + Bins the factor (log-spaced when it spans more than ~2 decades) and plots the per-bin + success rate with a 95% Wilson confidence band on a 0-1 axis. The rate is read straight + off the y-axis, and because each bin is counted independently the result is correct + regardless of how the factor was sampled. + + If ``group_by`` names a categorical factor, one curve is drawn per category (sharing the + same bins) so an interaction — e.g. the light gate differing by object — is visible + instead of being averaged away. """ factor_column_slice = dataset.factor_columns[factor_name] factor_values = dataset.theta[:, factor_column_slice].squeeze(-1).cpu().numpy() @@ -72,6 +77,7 @@ def draw_success_rate( # Log-spaced bins when the factor is positive and spans many decades, so a wide-range # factor (e.g. light) resolves its low end instead of collapsing into the first bin. + # Bins are shared across groups so the per-category curves are directly comparable. range_low, range_high = float(factor_values.min()), float(factor_values.max()) use_log_axis = range_low > 0 and np.log10(range_high / range_low) > 2 if use_log_axis: @@ -79,8 +85,49 @@ def draw_success_rate( else: bin_edges = np.linspace(range_low, range_high, num_bins + 1) - z = 1.959963985 # 95% normal quantile, for the Wilson score interval - bin_centers, rates, ci_low, ci_high, bin_counts = [], [], [], [], [] + # One pooled curve by default, or one per category of the group_by factor. + if group_by is None: + groups = [(f"empirical {outcome_name}", np.ones(len(factor_values), dtype=bool))] + else: + group_spec = next(factor for factor in dataset.schema.factors if factor.name == group_by) + assert group_spec.choices is not None, f"group_by factor {group_by!r} must be categorical" + group_codes = dataset.theta[:, dataset.factor_columns[group_by]].squeeze(-1).long().cpu().numpy() + groups = [ + (f"{choice} (n={int((group_codes == code).sum())})", group_codes == code) + for code, choice in enumerate(group_spec.choices) + ] + + annotate_counts = group_by is None # per-bin n labels only when there's a single curve + for group_label, group_mask in groups: + centers, rates, ci_low, ci_high, counts = _binned_success_rate( + factor_values[group_mask], outcome_values[group_mask], bin_edges, use_log_axis + ) + if not centers: + continue + (line,) = ax.plot(centers, rates, "o-", linewidth=2, label=group_label) + ax.fill_between(centers, ci_low, ci_high, color=line.get_color(), alpha=0.15) + if annotate_counts: + for center_x, rate, count in zip(centers, rates, counts): + ax.text(center_x, min(rate + 0.04, 1.04), f"n={count}", ha="center", fontsize=7, color="gray") + + if use_log_axis: + ax.set_xscale("log") + ax.set_xlabel(factor_name) + ax.set_ylabel(f"{outcome_name} (success rate)") + ax.set_ylim(0, 1.1) + ax.legend(loc="best", fontsize=9) + ax.grid(alpha=0.3) + + +def _binned_success_rate(factor_values, outcome_values, bin_edges, use_log_axis): + """Per-bin success rate with a 95% Wilson confidence interval. + + Returns parallel lists ``(centers, rates, ci_low, ci_high, counts)``, skipping empty + bins. Centers are geometric (log axis) or arithmetic (linear axis) bin midpoints. + """ + z = 1.959963985 # 95% normal quantile + num_bins = len(bin_edges) - 1 + centers, rates, ci_low, ci_high, counts = [], [], [], [], [] for bin_index in range(num_bins): # Last bin is closed on the right so the maximum value isn't dropped. upper = bin_edges[bin_index + 1] @@ -90,33 +137,20 @@ def draw_success_rate( num_in_bin = int(in_bin.sum()) if num_in_bin == 0: continue - num_success = int((outcome_values[in_bin] >= SUCCESS_THRESHOLD).sum()) - rate = num_success / num_in_bin + rate = int((outcome_values[in_bin] >= SUCCESS_THRESHOLD).sum()) / num_in_bin # Wilson score interval — stays inside [0, 1] even at rate 0/1 or small n. denominator = 1 + z * z / num_in_bin center = (rate + z * z / (2 * num_in_bin)) / denominator margin = z / denominator * np.sqrt(rate * (1 - rate) / num_in_bin + z * z / (4 * num_in_bin * num_in_bin)) if use_log_axis: - bin_centers.append(float(np.sqrt(bin_edges[bin_index] * upper))) + centers.append(float(np.sqrt(bin_edges[bin_index] * upper))) else: - bin_centers.append(float(0.5 * (bin_edges[bin_index] + upper))) + centers.append(float(0.5 * (bin_edges[bin_index] + upper))) rates.append(rate) ci_low.append(max(0.0, center - margin)) ci_high.append(min(1.0, center + margin)) - bin_counts.append(num_in_bin) - - ax.plot(bin_centers, rates, "o-", color=_POSTERIOR_COLOR, linewidth=2, label=f"empirical {outcome_name}") - ax.fill_between(bin_centers, ci_low, ci_high, color=_POSTERIOR_COLOR, alpha=0.2, label="95% Wilson CI") - for center_x, rate, count in zip(bin_centers, rates, bin_counts): - ax.text(center_x, min(rate + 0.04, 1.04), f"n={count}", ha="center", fontsize=7, color="gray") - - if use_log_axis: - ax.set_xscale("log") - ax.set_xlabel(factor_name) - ax.set_ylabel(f"{outcome_name} (success rate)") - ax.set_ylim(0, 1.1) - ax.legend(loc="best", fontsize=9) - ax.grid(alpha=0.3) + counts.append(num_in_bin) + return centers, rates, ci_low, ci_high, counts def _draw_continuous_marginal( From 0d4104134c5d506815d25a6278265f1e4f174b1e Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 11:07:51 +0200 Subject: [PATCH 37/74] Snapshot sensitivity MVP before robolab-matching refactor Captures the full pre-refactor implementation (MNPE + KDE/Empirical analyzers, factory dispatch, success-rate/conditional/overlay plotting, two-page PDF report, plus the new from_tensors dataset path and synthetic recovery tests). Parked on a branch so the empirical/KDE machinery can be resurrected. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/dataset.py | 245 ++++++++++-------- .../analysis/sensitivity/pdf_report.py | 52 +++- .../analysis/sensitivity/plotting.py | 168 ++++++++++++ .../tests/test_sensitivity_analysis.py | 84 ++++++ .../tests/utils/synthetic_sensitivity.py | 112 ++++++++ 5 files changed, 553 insertions(+), 108 deletions(-) create mode 100644 isaaclab_arena/tests/test_sensitivity_analysis.py create mode 100644 isaaclab_arena/tests/utils/synthetic_sensitivity.py diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py index 2ed6653623..fc728890b7 100644 --- a/isaaclab_arena/analysis/sensitivity/dataset.py +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -135,118 +135,59 @@ def factor_columns(self) -> dict[str, slice]: class SensitivityDataset: - """A ``factors.yaml`` schema paired with its ``episode_summary.jsonl`` rows. + """A ``FactorSchema`` paired with its per-episode ``theta`` (factors) and ``x`` (outcomes). - Parses and validates both on construction, then exposes the ``theta`` (factors), - ``x`` (outcomes), ``prior`` and ``factor_columns`` an analyzer consumes. - """ - - def __init__(self, factors_yaml: str | Path, jsonl_path: str | Path): - self.schema = FactorSchema.from_yaml(factors_yaml) - - jsonl_text = Path(jsonl_path).read_text(encoding="utf-8") - self.rows = [json.loads(line) for line in jsonl_text.splitlines() if line.strip()] - assert len(self.rows) > 0, f"Empty episode_summary.jsonl at {jsonl_path}" - - self._validate_rows(jsonl_path) - self._infer_missing_factor_ranges() - - self._theta = self._build_factor_tensor() - self._x = self._build_outcome_tensor() - - def _validate_rows(self, jsonl_path: str | Path) -> None: - """Assert every JSONL row carries the schema's declared factor and outcome keys. + The object is a pure container: it holds the schema and the two tensors, and exposes + the ``prior`` and column layouts an analyzer consumes. It can be built two ways: - The declared names need only be a subset of each row's ``arena_env_args`` / - ``outcomes``; extra keys are ignored. Raises pointing at the first offending row. - """ - expected_factor_names = {factor.name for factor in self.schema.factors} - expected_outcome_names = {outcome.name for outcome in self.schema.outcomes} - for row_index, row in enumerate(self.rows): - assert ( - "arena_env_args" in row and "outcomes" in row - ), f"Row {row_index} of {jsonl_path} missing arena_env_args/outcomes block" - missing_factor_names = expected_factor_names - set(row["arena_env_args"].keys()) - assert not missing_factor_names, ( - f"Row {row_index} of {jsonl_path} is missing factor(s) " - f"{sorted(missing_factor_names)} from its arena_env_args block; " - f"factors.yaml declares: {sorted(expected_factor_names)}" - ) - missing_outcome_names = expected_outcome_names - set(row["outcomes"].keys()) - assert ( - not missing_outcome_names - ), f"Row {row_index} of {jsonl_path} missing outcomes {sorted(missing_outcome_names)}" + - :meth:`from_files` — parse a ``factors.yaml`` / ``episode_summary.jsonl`` pair + (the path eval runs take). + - the constructor — wrap in-memory tensors directly (what a synthetic simulator or + a unit test takes). The tensors must already be in the layout :meth:`factor_columns` + describes: continuous columns first, then one integer-coded column per categorical. + """ - def _infer_missing_factor_ranges(self) -> None: - """Fill any continuous factor's missing ``range`` from the observed min/max. + def __init__(self, schema: FactorSchema, theta: torch.Tensor, x: torch.Tensor): + """Wrap an in-memory ``schema`` plus its ``theta`` / ``x`` tensors, validating shapes. - A ``range`` declared in factors.yaml takes precedence and is left untouched. + Args: + schema: The parsed factor/outcome schema. Continuous factors must carry a + ``range``; categorical factors must carry ``choices``. + theta: ``(num_episodes, total_factor_dim)`` factor matrix, continuous-first. + x: ``(num_episodes, num_outcomes)`` outcome matrix. """ - for factor in self.schema.factors: - if factor.type != "continuous" or factor.range is not None: - continue - if factor.dim != 1: - raise NotImplementedError( - "Range inference for vector factors (dim > 1) is not implemented;" - f" factor {factor.name!r} has dim={factor.dim}" - ) - observed_values = [float(row["arena_env_args"][factor.name]) for row in self.rows] - factor.range = [[min(observed_values), max(observed_values)]] + assert theta.ndim == 2 and x.ndim == 2, f"theta and x must be 2D; got {theta.shape} and {x.shape}" + assert theta.shape[0] == x.shape[0], f"theta/x row counts disagree: {theta.shape[0]} vs {x.shape[0]}" + assert theta.shape[0] > 0, "Dataset is empty (no episodes)" + assert ( + theta.shape[1] == schema.total_factor_dim + ), f"theta has {theta.shape[1]} columns but schema declares {schema.total_factor_dim} factor dims" + assert x.shape[1] == len( + schema.outcomes + ), f"x has {x.shape[1]} columns but schema declares {len(schema.outcomes)} outcomes" + self.schema = schema + self._theta = theta + self._x = x - def _build_factor_tensor(self) -> torch.Tensor: - """Assemble the per-episode factor matrix ``theta``. + @classmethod + def from_files(cls, factors_yaml: str | Path, jsonl_path: str | Path) -> SensitivityDataset: + """Build a dataset from a ``factors.yaml`` schema and an ``episode_summary.jsonl``. - Continuous columns first (one per dim), then one column per categorical factor - with its value integer-coded as a ``float32`` index into ``FactorSpec.choices``. + Parses and validates both, infers any missing continuous range from the data, and + assembles the ``theta`` / ``x`` tensors in the layout the analyzers expect. """ - continuous_factors = [factor for factor in self.schema.factors if factor.type == "continuous"] - categorical_factors = [factor for factor in self.schema.factors if factor.type == "categorical"] - - factor_columns: list[torch.Tensor] = [] + schema = FactorSchema.from_yaml(factors_yaml) - # Continuous columns come first (sbi MNPE convention). - for factor in continuous_factors: - if factor.dim != 1: - raise NotImplementedError( - "Vector continuous factors (dim > 1) are not yet supported;" - f" factor {factor.name!r} has dim={factor.dim}" - ) - raw_values = [float(row["arena_env_args"][factor.name]) for row in self.rows] - factor_column = torch.tensor(raw_values, dtype=torch.float32).unsqueeze(1) - factor_columns.append(factor_column) - - # Categorical columns: integer-code each string value as its index in FactorSpec.choices. - for factor in categorical_factors: - assert ( - factor.choices is not None and len(factor.choices) > 0 - ), f"Categorical factor {factor.name!r} has no `choices:` block in factors.yaml" - choice_to_code = {choice: code for code, choice in enumerate(factor.choices)} - category_codes: list[int] = [] - for row_index, row in enumerate(self.rows): - value = row["arena_env_args"][factor.name] - assert value in choice_to_code, ( - f"Row {row_index} factor {factor.name!r} has value {value!r}" - f" not in declared choices {factor.choices}" - ) - category_codes.append(choice_to_code[value]) - factor_column = torch.tensor(category_codes, dtype=torch.float32).unsqueeze(1) - factor_columns.append(factor_column) - - if factor_columns: - return torch.cat(factor_columns, dim=1) - return torch.zeros((len(self.rows), 0), dtype=torch.float32) + jsonl_text = Path(jsonl_path).read_text(encoding="utf-8") + rows = [json.loads(line) for line in jsonl_text.splitlines() if line.strip()] + assert len(rows) > 0, f"Empty episode_summary.jsonl at {jsonl_path}" - def _build_outcome_tensor(self) -> torch.Tensor: - """Assemble the per-episode outcome matrix ``x`` (one column per declared outcome). + _validate_rows(schema, rows, jsonl_path) + _infer_missing_factor_ranges(schema, rows) - Each outcome value is cast to float; bool outcomes become 0.0/1.0. The analyzer - usually selects a single outcome column at fit time and conditions queries on it. - """ - outcome_column_tensors = [ - torch.tensor([float(row["outcomes"][outcome.name]) for row in self.rows], dtype=torch.float32).unsqueeze(1) - for outcome in self.schema.outcomes - ] - return torch.cat(outcome_column_tensors, dim=1) + theta = _build_factor_tensor(schema, rows) + x = _build_outcome_tensor(schema, rows) + return cls(schema, theta, x) @property def theta(self) -> torch.Tensor: @@ -267,6 +208,11 @@ def x(self) -> torch.Tensor: """ return self._x + @property + def num_episodes(self) -> int: + """Number of episodes (rows) in the dataset.""" + return self._theta.shape[0] + @property def factor_columns(self) -> dict[str, slice]: """Map factor name → its column slice in theta. Same as ``schema.factor_columns``.""" @@ -319,3 +265,100 @@ def prior(self): low=torch.tensor(low_bounds, dtype=torch.float32), high=torch.tensor(high_bounds, dtype=torch.float32), ) + + +def _validate_rows(schema: FactorSchema, rows: list[dict], jsonl_path: str | Path) -> None: + """Assert every JSONL row carries the schema's declared factor and outcome keys. + + The declared names need only be a subset of each row's ``arena_env_args`` / ``outcomes``; + extra keys are ignored. Raises pointing at the first offending row. + """ + expected_factor_names = {factor.name for factor in schema.factors} + expected_outcome_names = {outcome.name for outcome in schema.outcomes} + for row_index, row in enumerate(rows): + assert ( + "arena_env_args" in row and "outcomes" in row + ), f"Row {row_index} of {jsonl_path} missing arena_env_args/outcomes block" + missing_factor_names = expected_factor_names - set(row["arena_env_args"].keys()) + assert not missing_factor_names, ( + f"Row {row_index} of {jsonl_path} is missing factor(s) " + f"{sorted(missing_factor_names)} from its arena_env_args block; " + f"factors.yaml declares: {sorted(expected_factor_names)}" + ) + missing_outcome_names = expected_outcome_names - set(row["outcomes"].keys()) + assert ( + not missing_outcome_names + ), f"Row {row_index} of {jsonl_path} missing outcomes {sorted(missing_outcome_names)}" + + +def _infer_missing_factor_ranges(schema: FactorSchema, rows: list[dict]) -> None: + """Fill any continuous factor's missing ``range`` from the observed min/max. + + A ``range`` declared in factors.yaml takes precedence and is left untouched. + """ + for factor in schema.factors: + if factor.type != "continuous" or factor.range is not None: + continue + if factor.dim != 1: + raise NotImplementedError( + "Range inference for vector factors (dim > 1) is not implemented;" + f" factor {factor.name!r} has dim={factor.dim}" + ) + observed_values = [float(row["arena_env_args"][factor.name]) for row in rows] + factor.range = [[min(observed_values), max(observed_values)]] + + +def _build_factor_tensor(schema: FactorSchema, rows: list[dict]) -> torch.Tensor: + """Assemble the per-episode factor matrix ``theta``. + + Continuous columns first (one per dim), then one column per categorical factor with its + value integer-coded as a ``float32`` index into ``FactorSpec.choices``. + """ + continuous_factors = [factor for factor in schema.factors if factor.type == "continuous"] + categorical_factors = [factor for factor in schema.factors if factor.type == "categorical"] + + factor_columns: list[torch.Tensor] = [] + + # Continuous columns come first (sbi MNPE convention). + for factor in continuous_factors: + if factor.dim != 1: + raise NotImplementedError( + "Vector continuous factors (dim > 1) are not yet supported;" + f" factor {factor.name!r} has dim={factor.dim}" + ) + raw_values = [float(row["arena_env_args"][factor.name]) for row in rows] + factor_column = torch.tensor(raw_values, dtype=torch.float32).unsqueeze(1) + factor_columns.append(factor_column) + + # Categorical columns: integer-code each string value as its index in FactorSpec.choices. + for factor in categorical_factors: + assert ( + factor.choices is not None and len(factor.choices) > 0 + ), f"Categorical factor {factor.name!r} has no `choices:` block in factors.yaml" + choice_to_code = {choice: code for code, choice in enumerate(factor.choices)} + category_codes: list[int] = [] + for row_index, row in enumerate(rows): + value = row["arena_env_args"][factor.name] + assert ( + value in choice_to_code + ), f"Row {row_index} factor {factor.name!r} has value {value!r} not in declared choices {factor.choices}" + category_codes.append(choice_to_code[value]) + factor_column = torch.tensor(category_codes, dtype=torch.float32).unsqueeze(1) + factor_columns.append(factor_column) + + if factor_columns: + return torch.cat(factor_columns, dim=1) + return torch.zeros((len(rows), 0), dtype=torch.float32) + + +def _build_outcome_tensor(schema: FactorSchema, rows: list[dict]) -> torch.Tensor: + """Assemble the per-episode outcome matrix ``x`` (one column per declared outcome). + + Each outcome value is cast to float; bool outcomes become 0.0/1.0. The analyzer usually + selects a single outcome column at fit time and conditions queries on it. + """ + outcome_column_tensors = [ + torch.tensor([float(row["outcomes"][outcome.name]) for row in rows], dtype=torch.float32).unsqueeze(1) + for outcome in schema.outcomes + ] + return torch.cat(outcome_column_tensors, dim=1) diff --git a/isaaclab_arena/analysis/sensitivity/pdf_report.py b/isaaclab_arena/analysis/sensitivity/pdf_report.py index bb55b16a09..0855b04f88 100644 --- a/isaaclab_arena/analysis/sensitivity/pdf_report.py +++ b/isaaclab_arena/analysis/sensitivity/pdf_report.py @@ -10,7 +10,12 @@ from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset from isaaclab_arena.analysis.sensitivity.factory import make_analyzer -from isaaclab_arena.analysis.sensitivity.plotting import draw_marginal, draw_success_rate +from isaaclab_arena.analysis.sensitivity.plotting import ( + draw_conditional_marginal, + draw_marginal, + draw_posterior_overlay, + draw_success_rate, +) def generate_pdf_report( @@ -32,8 +37,9 @@ def generate_pdf_report( matplotlib.use("Agg") import matplotlib.pyplot as plt + from matplotlib.backends.backend_pdf import PdfPages - dataset = SensitivityDataset(Path(factors_yaml_path), Path(jsonl_path)) + dataset = SensitivityDataset.from_files(Path(factors_yaml_path), Path(jsonl_path)) outcomes = dataset.schema.outcomes factors = dataset.schema.factors # A low-cardinality categorical to split continuous success-rate curves by, so a @@ -42,7 +48,7 @@ def generate_pdf_report( (f.name for f in factors if f.type == "categorical" and f.choices and len(f.choices) <= 6), None ) n_rows, n_cols = len(outcomes), len(factors) - print(f"[INFO] PDF report: {n_rows} outcomes × {n_cols} factors ({len(dataset.rows)} episodes)") + print(f"[INFO] PDF report: {n_rows} outcomes × {n_cols} factors ({dataset.num_episodes} episodes)") figure, axes = plt.subplots(n_rows, n_cols, figsize=(6.5 * n_cols, 4.5 * n_rows), squeeze=False) for row_index, outcome in enumerate(outcomes): @@ -66,6 +72,11 @@ def generate_pdf_report( if split_factor: title += f" (split by {split_factor})" ax.set_title(title, fontsize=10) + elif factor.type == "continuous": + # Continuous outcome: no single success value to condition on, so sweep the + # outcome value and overlay the MNPE posteriors to show the conditional shift. + draw_conditional_marginal(ax, analyzer, factor.name) + ax.set_title(f"{outcome.name} vs {factor.name}\n(MNPE posterior, swept {outcome.name})", fontsize=10) else: draw_marginal(ax, analyzer, factor.name, outcome_value=outcome_value) ax.set_title( @@ -73,19 +84,46 @@ def generate_pdf_report( ) slice_info = dataset.schema.slice + slice_label = f"{slice_info.policy} / {slice_info.task} / {slice_info.embodiment}" # Two lines so the title doesn't clip on narrow (single-factor) figures. figure.suptitle( - f"Sensitivity report — {len(dataset.rows)} episodes\n" - f"{slice_info.policy} / {slice_info.task} / {slice_info.embodiment}", + f"Sensitivity report — {dataset.num_episodes} episodes\n{slice_label}", fontsize=12, fontweight="bold", ) figure.tight_layout(rect=[0, 0, 1, 0.94]) # leave room for the two-line suptitle + # Page 2 (robolab-style): P(factor | success=1) as filled KDE overlays per group, one + # panel per continuous factor, conditioned on the first binary ("success") outcome. + continuous_factors = [factor for factor in factors if factor.type == "continuous"] + binary_outcomes = [outcome for outcome in outcomes if _is_binary_outcome(dataset, outcome.name)] + posterior_figure = None + if continuous_factors and binary_outcomes: + success_outcome = binary_outcomes[0] + posterior_figure, posterior_axes = plt.subplots( + 1, len(continuous_factors), figsize=(6.5 * len(continuous_factors), 4.5), squeeze=False + ) + for col_index, factor in enumerate(continuous_factors): + ax = posterior_axes[0][col_index] + draw_posterior_overlay(ax, dataset, factor.name, success_outcome.name, group_by=split_factor) + title = f"P({factor.name} | {success_outcome.name}=1)" + if split_factor: + title += f" (by {split_factor})" + ax.set_title(title, fontsize=10) + posterior_figure.suptitle( + f"Posterior densities — Observation: {success_outcome.name}=1\n{slice_label}", + fontsize=12, + fontweight="bold", + ) + posterior_figure.tight_layout(rect=[0, 0, 1, 0.92]) + output_pdf_path = Path(output_pdf_path) output_pdf_path.parent.mkdir(parents=True, exist_ok=True) - figure.savefig(output_pdf_path) # .pdf extension → matplotlib's PDF backend - plt.close(figure) + with PdfPages(output_pdf_path) as pdf: + pdf.savefig(figure) + if posterior_figure is not None: + pdf.savefig(posterior_figure) + plt.close("all") print(f"[INFO] Wrote report → {output_pdf_path}") return output_pdf_path diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index e8e223c299..3b51c2359a 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -25,6 +25,65 @@ _RUG_FAILURE_OFFSET = -0.10 # rug y-offset (× density.max()) for failures +def plot_posterior_marginals( + dataset: SensitivityDataset, + outcome_name: str, + outcome_value: float | None = None, + output_path: str | None = None, +): + """robolab-style quick look: one marginal-posterior panel per factor in a single figure. + + Fits the analyzer for ``outcome_name`` and draws each factor's marginal conditioned on a + single observation — the simple "did the posterior recover the relationship?" view, the + counterpart to the full grid in ``generate_pdf_report``. Operates on an in-memory dataset + (e.g. a synthetic simulator's output), so no ``factors.yaml`` / JSONL round-trip is needed. + + Args: + dataset: The factors+outcomes to analyze. + outcome_name: Which outcome to condition the posteriors on. + outcome_value: Value to condition on. Defaults to ``1.0`` for a binary outcome + (the "success" branch) or the empirical median otherwise. + output_path: If given, save the figure here (parent dirs created). PNG/PDF/etc. + + Returns: + The matplotlib ``Figure`` (so the caller can show, tweak, or save it). + """ + import matplotlib.pyplot as plt + + from isaaclab_arena.analysis.sensitivity.factory import make_analyzer + + analyzer = make_analyzer(dataset, outcome_name) + analyzer.fit() + + outcome_column = dataset.x[:, dataset.outcome_columns[outcome_name]].cpu().numpy() + if outcome_value is None: + is_binary = set(outcome_column.flatten().tolist()).issubset({0.0, 1.0}) + outcome_value = 1.0 if is_binary else float(np.median(outcome_column)) + + factors = dataset.schema.factors + figure, axes = plt.subplots(1, len(factors), figsize=(6.0 * len(factors), 4.5), squeeze=False) + for column_index, factor in enumerate(factors): + ax = axes[0][column_index] + draw_marginal(ax, analyzer, factor.name, outcome_value=outcome_value) + ax.set_title(f"{factor.name} (| {outcome_name}={outcome_value:g})", fontsize=10) + + slice_info = dataset.schema.slice + figure.suptitle( + f"Posterior marginals — {dataset.num_episodes} episodes ({outcome_name}={outcome_value:g})\n" + f"{slice_info.policy} / {slice_info.task} / {slice_info.embodiment}", + fontsize=12, + fontweight="bold", + ) + figure.tight_layout(rect=[0, 0, 1, 0.92]) + + if output_path is not None: + from pathlib import Path + + Path(output_path).parent.mkdir(parents=True, exist_ok=True) + figure.savefig(output_path, dpi=150, bbox_inches="tight") + return figure + + def draw_marginal( ax, analyzer: BaseAnalyzer, @@ -153,6 +212,115 @@ def _binned_success_rate(factor_values, outcome_values, bin_edges, use_log_axis) return centers, rates, ci_low, ci_high, counts +def draw_conditional_marginal( + ax, + analyzer: BaseAnalyzer, + factor_name: str, + num_curves: int = 3, + num_grid_points: int = 200, +) -> None: + """Overlay the posterior ``P(factor | outcome=v)`` at several outcome values ``v``. + + For a continuous outcome there is no single "success" value to condition on, so the + conditioning value is swept across the observed outcome range (5th-95th percentile) + and the resulting posteriors are overlaid. The shift between curves shows how the + factor's posterior depends on the outcome — e.g. fast vs slow episodes favouring + brighter vs darker light — the conditional query only a posterior model provides. + """ + from matplotlib import colormaps + + factor_spec = analyzer._factor_spec(factor_name) + outcome_column_index = analyzer.dataset.outcome_columns[analyzer.outcome_name] + outcome_data = analyzer.dataset.x[:, outcome_column_index].cpu().numpy() + low_value, high_value = np.percentile(outcome_data, [5, 95]) + conditioning_values = np.linspace(low_value, high_value, num_curves) + colors = colormaps["coolwarm"](np.linspace(0, 1, num_curves)) + + for conditioning_value, color in zip(conditioning_values, colors): + grid, density = analyzer.continuous_marginal_density(factor_name, float(conditioning_value), num_grid_points) + ax.plot(grid, density, color=color, linewidth=2, label=f"{analyzer.outcome_name}={conditioning_value:g}") + + # Multi-decade factor: show the conditioning shift on a log axis instead of cramping it. + if ( + factor_spec.range is not None + and factor_spec.range[0][0] > 0 + and np.log10(factor_spec.range[0][1] / factor_spec.range[0][0]) > 2 + ): + ax.set_xscale("log") + ax.set_xlabel(factor_name) + ax.set_ylabel("posterior density") + ax.legend(loc="best", fontsize=8, title=f"conditioned on {analyzer.outcome_name}") + ax.grid(alpha=0.3) + + +def draw_posterior_overlay( + ax, + dataset: SensitivityDataset, + factor_name: str, + outcome_name: str, + group_by: str | None = None, + num_grid_points: int = 200, +) -> None: + """Overlay ``P(factor | outcome=1)`` as filled KDE curves, one per group (robolab style). + + For each group, fits a Gaussian KDE to the factor values of the *successful* episodes + (``outcome >= SUCCESS_THRESHOLD``) — the posterior over the factor given success under a + uniform prior. Curves are filled and overlaid for comparison. A wide-range factor is fit + in log space so the density is sharp instead of crammed at the low end. + """ + from scipy.stats import gaussian_kde + + factor_column_slice = dataset.factor_columns[factor_name] + factor_values = dataset.theta[:, factor_column_slice].squeeze(-1).cpu().numpy() + outcome_column_index = dataset.outcome_columns[outcome_name] + success_mask = dataset.x[:, outcome_column_index].cpu().numpy() >= SUCCESS_THRESHOLD + + range_low, range_high = float(factor_values.min()), float(factor_values.max()) + use_log = range_low > 0 and np.log10(range_high / range_low) > 2 + to_fit_space = (lambda v: np.log10(v)) if use_log else (lambda v: v) + grid_fit = np.linspace(to_fit_space(range_low), to_fit_space(range_high), num_grid_points) + grid_display = 10**grid_fit if use_log else grid_fit + + if group_by is None: + groups = [(f"{outcome_name}=1", np.ones(len(factor_values), dtype=bool))] + else: + group_spec = next(factor for factor in dataset.schema.factors if factor.name == group_by) + assert group_spec.choices is not None, f"group_by factor {group_by!r} must be categorical" + group_codes = dataset.theta[:, dataset.factor_columns[group_by]].squeeze(-1).long().cpu().numpy() + groups = [(choice, group_codes == code) for code, choice in enumerate(group_spec.choices)] + + # Draw the curves, collecting each group's successful samples for a rug below the axis. + max_density = 0.0 + rug_series = [] # (sample display-values, color) per group + for group_label, group_mask in groups: + successful_factor = factor_values[group_mask & success_mask] + fit_values = to_fit_space(successful_factor) + if len(successful_factor) < 2 or float(np.std(fit_values)) < 1e-9: + continue # KDE undefined for fewer than 2 points or zero spread + density = gaussian_kde(fit_values)(grid_fit) + max_density = max(max_density, float(density.max())) + (line,) = ax.plot(grid_display, density, linewidth=2, label=f"{group_label} (n={len(successful_factor)})") + ax.fill_between(grid_display, 0, density, color=line.get_color(), alpha=0.3) + rug_series.append((successful_factor, line.get_color())) + + # Rug of the actual successful samples per group, stacked just below the axis: it shows + # where the data really is, so the KDE's taper reads as thinning data, not as signal. + for rug_index, (sample_values, color) in enumerate(rug_series): + rug_y = -(0.03 + 0.025 * rug_index) * max_density + ax.scatter( + sample_values, np.full(len(sample_values), rug_y), marker="|", color=color, s=_RUG_MARKER_SIZE, alpha=0.5 + ) + + if use_log: + ax.set_xscale("log") + if max_density > 0: + ax.set_ylim(-(0.03 + 0.025 * len(rug_series)) * max_density, max_density * 1.1) + ax.set_xlabel(factor_name) + ax.set_ylabel("posterior density") + ax.legend(loc="best", fontsize=8) + ax.grid(alpha=0.3) + + def _draw_continuous_marginal( ax, analyzer: BaseAnalyzer, diff --git a/isaaclab_arena/tests/test_sensitivity_analysis.py b/isaaclab_arena/tests/test_sensitivity_analysis.py new file mode 100644 index 0000000000..a98497298b --- /dev/null +++ b/isaaclab_arena/tests/test_sensitivity_analysis.py @@ -0,0 +1,84 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""End-to-end sensitivity-analysis tests on synthetic data with a known ground truth. + +Each test fits a *real* analyzer (via ``make_analyzer``) on a dataset whose factor→outcome +relationship is planted by ``synthetic_sensitivity`` (brighter light and oak raise success), +then asserts the recovered posterior reflects that relationship. The data is built in memory, +so these run on CPU without Isaac Sim. They cover the inference layer the way robolab's +``simple_simulator`` does — not the YAML/JSONL parsing, which has no bearing on inference. +""" + +from __future__ import annotations + +import numpy as np +import torch + +from isaaclab_arena.analysis.sensitivity.empirical_analyzer import KDEAnalyzer +from isaaclab_arena.analysis.sensitivity.factory import make_analyzer +from isaaclab_arena.analysis.sensitivity.posterior_analyzer import MNPEAnalyzer +from isaaclab_arena.tests.utils.synthetic_sensitivity import ( + LIGHT_RANGE, + MATERIAL_BASE_LOGIT, + make_continuous_dataset, + make_mixed_dataset, +) + +_OUTCOME = "success" +_SUCCESS = 1.0 +_FAILURE = 0.0 +_NUM_GRID_POINTS = 200 +_NUM_SAMPLES = 5000 + +_LIGHT_LOW, _LIGHT_HIGH = LIGHT_RANGE +_LIGHT_MIDPOINT = 0.5 * (_LIGHT_LOW + _LIGHT_HIGH) + + +def _density_weighted_mean(grid: np.ndarray, density: np.ndarray) -> float: + """Mean light value under a (grid, density) marginal — i.e. where the posterior mass sits.""" + return float(np.sum(grid * density) / np.sum(density)) + + +def test_kde_recovers_brighter_light_drives_success(): + """A single continuous factor: successful episodes should concentrate at high light.""" + dataset = make_continuous_dataset(seed=0) + analyzer = make_analyzer(dataset, _OUTCOME) + assert isinstance(analyzer, KDEAnalyzer) + + analyzer.fit() + grid, success_density = analyzer.continuous_marginal_density("light_intensity", _SUCCESS, _NUM_GRID_POINTS) + + # Ground truth plants brighter ⇒ more successful, so the success posterior must skew well + # above the midpoint of the light range (a uniform posterior would sit exactly at it). + assert _density_weighted_mean(grid, success_density) > _LIGHT_MIDPOINT + 0.1 * (_LIGHT_HIGH - _LIGHT_LOW) + + +def test_mnpe_recovers_light_and_material_effects(): + """Mixed continuous + categorical: recover both the light trend and the material ranking.""" + dataset = make_mixed_dataset(seed=0) + analyzer = make_analyzer(dataset, _OUTCOME) + assert isinstance(analyzer, MNPEAnalyzer) + + torch.manual_seed(0) # make the posterior sampling below reproducible + analyzer.fit() + + # Continuous effect: light conditioned on success should sit higher than on failure. + grid, success_density = analyzer.continuous_marginal_density("light_intensity", _SUCCESS, _NUM_GRID_POINTS) + _, failure_density = analyzer.continuous_marginal_density("light_intensity", _FAILURE, _NUM_GRID_POINTS) + assert _density_weighted_mean(grid, success_density) > _density_weighted_mean(grid, failure_density) + + # Categorical effect: oak is the planted best material, bamboo the worst. + materials = list(MATERIAL_BASE_LOGIT) + oak_index, bamboo_index = materials.index("oak"), materials.index("bamboo") + success_probs = analyzer.categorical_marginal_probs("table_material", _SUCCESS, _NUM_SAMPLES) + failure_probs = analyzer.categorical_marginal_probs("table_material", _FAILURE, _NUM_SAMPLES) + + assert success_probs.argmax() == oak_index, f"expected oak most likely given success, got {success_probs}" + assert success_probs[oak_index] > success_probs[bamboo_index] + # Conditioning works in both directions: oak is overrepresented among successes, + # bamboo among failures, relative to the other outcome. + assert success_probs[oak_index] > failure_probs[oak_index] + assert failure_probs[bamboo_index] > success_probs[bamboo_index] diff --git a/isaaclab_arena/tests/utils/synthetic_sensitivity.py b/isaaclab_arena/tests/utils/synthetic_sensitivity.py new file mode 100644 index 0000000000..8d483c845c --- /dev/null +++ b/isaaclab_arena/tests/utils/synthetic_sensitivity.py @@ -0,0 +1,112 @@ +# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Synthetic sensitivity datasets with a *known* ground-truth relationship. + +This is the Arena analogue of the ``simple_simulator`` in robolab's MNPE demo: it samples +factors from a uniform prior, runs them through a fixed generative model, and returns a +:class:`SensitivityDataset` of in-memory ``theta`` / ``x`` tensors — no ``factors.yaml`` or +``episode_summary.jsonl`` round-trip. Because the planted relationship is known, a test can +fit an analyzer on the data and assert the recovered posterior reflects it. + +Ground truth (single-sourced in the constants below): + - ``light_intensity`` is continuous over ``LIGHT_RANGE``; higher light raises success + (``LIGHT_WEIGHT > 0``). + - ``table_material`` is categorical; ``MATERIAL_BASE_LOGIT`` makes oak the most successful + material and bamboo the least. + - ``success`` is a binary outcome drawn from ``Bernoulli(sigmoid(logit))``. +""" + +from __future__ import annotations + +import torch + +from isaaclab_arena.analysis.sensitivity.dataset import ( + FactorSchema, + FactorSpec, + OutcomeSpec, + SensitivityDataset, + SliceSpec, +) + +LIGHT_RANGE: tuple[float, float] = (0.0, 5000.0) +"""Range of the continuous ``light_intensity`` factor.""" + +LIGHT_WEIGHT: float = 2.5 +"""Success-logit gain per unit of normalized light. Positive ⇒ brighter is more successful.""" + +MATERIAL_BASE_LOGIT: dict[str, float] = {"oak": 1.5, "walnut": 0.0, "bamboo": -1.5} +"""Per-material base success logit. Ordered best→worst, so oak should dominate the posterior.""" + +_OUTCOME_NAME = "success" +_SLICE = SliceSpec(policy="synthetic", task="SyntheticTask", embodiment="synthetic") + + +def _normalized_light(light_intensity: torch.Tensor) -> torch.Tensor: + """Map ``light_intensity`` from ``LIGHT_RANGE`` onto roughly ``[-1, 1]`` for the logit.""" + low, high = LIGHT_RANGE + midpoint = 0.5 * (low + high) + half_range = 0.5 * (high - low) + return (light_intensity - midpoint) / half_range + + +def _sample_success(success_logit: torch.Tensor) -> torch.Tensor: + """Draw a binary success outcome per episode from ``Bernoulli(sigmoid(logit))``.""" + return torch.bernoulli(torch.sigmoid(success_logit)) + + +def make_continuous_dataset(seed: int, num_episodes: int = 2000) -> SensitivityDataset: + """Single continuous factor (``light_intensity``) driving a binary ``success`` outcome. + + Dispatches to ``KDEAnalyzer``. Success probability rises monotonically with light, so the + posterior over successful-episode light values should concentrate at the bright end. + """ + torch.manual_seed(seed) + + low, high = LIGHT_RANGE + light_intensity = torch.rand(num_episodes) * (high - low) + low + success = _sample_success(LIGHT_WEIGHT * _normalized_light(light_intensity)) + + schema = FactorSchema( + slice=_SLICE, + factors=[FactorSpec(name="light_intensity", type="continuous", range=[list(LIGHT_RANGE)])], + outcomes=[OutcomeSpec(name=_OUTCOME_NAME, type="bool")], + ) + theta = light_intensity.unsqueeze(1) + x = success.unsqueeze(1) + return SensitivityDataset(schema, theta, x) + + +def make_mixed_dataset(seed: int, num_episodes: int = 2000) -> SensitivityDataset: + """Continuous ``light_intensity`` + categorical ``table_material`` driving ``success``. + + Dispatches to ``MNPEAnalyzer``. Both effects are planted: brighter light and "better" + materials (oak > walnut > bamboo) raise success, so conditioning the posterior on success + should favor high light values and oak. + """ + torch.manual_seed(seed) + + materials = list(MATERIAL_BASE_LOGIT) + material_base_logits = torch.tensor([MATERIAL_BASE_LOGIT[m] for m in materials]) + + low, high = LIGHT_RANGE + light_intensity = torch.rand(num_episodes) * (high - low) + low + material_code = torch.randint(0, len(materials), (num_episodes,)) + success_logit = material_base_logits[material_code] + LIGHT_WEIGHT * _normalized_light(light_intensity) + success = _sample_success(success_logit) + + schema = FactorSchema( + slice=_SLICE, + factors=[ + FactorSpec(name="light_intensity", type="continuous", range=[list(LIGHT_RANGE)]), + FactorSpec(name="table_material", type="categorical", choices=materials), + ], + outcomes=[OutcomeSpec(name=_OUTCOME_NAME, type="bool")], + ) + # Continuous column first, then the integer-coded categorical column — the layout + # SensitivityDataset.factor_columns describes and the analyzers expect. + theta = torch.stack([light_intensity, material_code.float()], dim=1) + x = success.unsqueeze(1) + return SensitivityDataset(schema, theta, x) From 3af9edf769929fc38e01bb24c5cac0cdcb6a684b Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 11:22:29 +0200 Subject: [PATCH 38/74] Refactor sensitivity analysis to mirror robolab (MNPE + NPE) Collapse the analyzer/plotting/report layers to robolab's shape: one SensitivityAnalyzer that auto-selects MNPE (mixed) or NPE (continuous-only), trains on the full (theta, x), and samples the joint posterior at one observation. Continuous factors are normalized to [0,1] before fitting and denormalized after so multi-scale factors train on equal footing. - New analyzer.py (replaces analyzer_base/posterior_analyzer/empirical_analyzer/factory) - plotting.py slimmed to one plot_marginals (histogram + bar), format-by-extension - report.py + generate_report.py CLI replace the multi-page PDF report - synthetic_sensitivity.py: 2-D continuous dataset for NPE + a one-command demo - tests rewritten to MNPE-recovery (mixed) and NPE-recovery (2-D continuous) - Empirical/KDE machinery parked on cvolk/feature/sensitivity_empirical_kde_parked Signed-off-by: Clemens Volk --- .../analysis/sensitivity/analyzer.py | 110 +++++ .../analysis/sensitivity/analyzer_base.py | 56 --- .../sensitivity/empirical_analyzer.py | 105 ---- .../analysis/sensitivity/factory.py | 47 -- .../sensitivity/generate_pdf_report.py | 36 -- .../analysis/sensitivity/generate_report.py | 46 ++ .../analysis/sensitivity/pdf_report.py | 146 ------ .../analysis/sensitivity/plotting.py | 454 +++--------------- .../sensitivity/posterior_analyzer.py | 127 ----- isaaclab_arena/analysis/sensitivity/report.py | 51 ++ .../tests/test_sensitivity_analysis.py | 88 ++-- .../tests/utils/synthetic_sensitivity.py | 105 +++- 12 files changed, 384 insertions(+), 987 deletions(-) create mode 100644 isaaclab_arena/analysis/sensitivity/analyzer.py delete mode 100644 isaaclab_arena/analysis/sensitivity/analyzer_base.py delete mode 100644 isaaclab_arena/analysis/sensitivity/empirical_analyzer.py delete mode 100644 isaaclab_arena/analysis/sensitivity/factory.py delete mode 100644 isaaclab_arena/analysis/sensitivity/generate_pdf_report.py create mode 100644 isaaclab_arena/analysis/sensitivity/generate_report.py delete mode 100644 isaaclab_arena/analysis/sensitivity/pdf_report.py delete mode 100644 isaaclab_arena/analysis/sensitivity/posterior_analyzer.py create mode 100644 isaaclab_arena/analysis/sensitivity/report.py diff --git a/isaaclab_arena/analysis/sensitivity/analyzer.py b/isaaclab_arena/analysis/sensitivity/analyzer.py new file mode 100644 index 0000000000..1a3cad5a86 --- /dev/null +++ b/isaaclab_arena/analysis/sensitivity/analyzer.py @@ -0,0 +1,110 @@ +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +import torch + +from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset + + +class SensitivityAnalyzer: + """Fits a neural posterior over all factors, conditioned on all outcomes (robolab-style). + + Picks the sbi estimator from the schema — ``MNPE`` when any factor is categorical (it + handles mixed continuous + categorical theta), ``NPE`` when every factor is continuous — + then trains on the full ``(theta, x)`` and samples the joint posterior at a chosen + observation. The single observation conditions on *all* outcome columns at once, so a + query like "which factors produced success?" is answered for every factor jointly. + + Continuous factors are normalized to ``[0, 1]`` before fitting and denormalized when + sampling, so factors on very different scales (e.g. light in thousands, an offset in + hundredths) train on equal footing. Categorical columns keep their integer codes. + + Note: NPE restricts itself to a Gaussian when theta is one-dimensional, so a meaningful + continuous-only analysis needs at least two continuous factors. Mixed schemas use MNPE + and are unaffected. + """ + + def __init__(self, dataset: SensitivityDataset): + self.dataset = dataset + self.posterior = None + # Continuous factors occupy the leading theta columns (one each); cache their + # [low, high] bounds for the normalize/denormalize round-trip around sbi. + continuous_factors = [factor for factor in dataset.schema.factors if factor.type == "continuous"] + self._num_continuous = len(continuous_factors) + self._continuous_low = torch.tensor([factor.range[0][0] for factor in continuous_factors]) + self._continuous_high = torch.tensor([factor.range[0][1] for factor in continuous_factors]) + + def _inference_cls(self): + """Return the sbi inference class for this schema (``MNPE`` if categoricals, else ``NPE``).""" + # Import sbi lazily — it is heavy and only needed once an analysis actually fits. + from sbi.inference import MNPE, NPE + + return MNPE if self.dataset.has_categorical_factors else NPE + + def _normalized_prior(self): + """Uniform prior matching the normalized theta: continuous dims ``[0, 1]``, categoricals ``[0, k-1]``.""" + from sbi.utils import BoxUniform + + low_bounds = [0.0] * self._num_continuous + high_bounds = [1.0] * self._num_continuous + for factor in self.dataset.schema.factors: + if factor.type == "categorical": + low_bounds.append(0.0) + high_bounds.append(float(len(factor.choices) - 1)) + return BoxUniform(low=torch.tensor(low_bounds), high=torch.tensor(high_bounds)) + + def _normalize(self, theta: torch.Tensor) -> torch.Tensor: + """Scale the continuous (leading) theta columns to ``[0, 1]``; leave categoricals untouched.""" + normalized = theta.clone() + span = (self._continuous_high - self._continuous_low).clamp_min(1e-12) + normalized[:, : self._num_continuous] = (theta[:, : self._num_continuous] - self._continuous_low) / span + return normalized + + def _denormalize(self, theta: torch.Tensor) -> torch.Tensor: + """Inverse of :meth:`_normalize`: map the continuous columns back to their original ranges.""" + denormalized = theta.clone() + span = self._continuous_high - self._continuous_low + denormalized[:, : self._num_continuous] = theta[:, : self._num_continuous] * span + self._continuous_low + return denormalized + + def fit(self, training_batch_size: int = 50) -> None: + """Train the estimator on the full ``(theta, x)`` and store the posterior on ``self``.""" + print( + f"[INFO] SensitivityAnalyzer: fitting {self._inference_cls().__name__} on" + f" {self.dataset.num_episodes} episodes" + f" (theta dim={self.dataset.theta.shape[1]}, x dim={self.dataset.x.shape[1]})." + ) + inference = self._inference_cls()(prior=self._normalized_prior()) + inference.append_simulations(self._normalize(self.dataset.theta), self.dataset.x) + density_estimator = inference.train(training_batch_size=training_batch_size) + self.posterior = inference.build_posterior(density_estimator) + + def default_observation(self) -> torch.Tensor: + """Default observation to condition on: ``1.0`` for binary outcomes, the mean otherwise. + + A binary outcome's interesting query is "what produced success?" (condition on 1.0); + a continuous outcome has no such value, so its mean is the natural "typical case". + """ + outcome_values = [] + for column_index in range(self.dataset.x.shape[1]): + column = self.dataset.x[:, column_index] + is_binary = set(column.tolist()).issubset({0.0, 1.0}) + outcome_values.append(1.0 if is_binary else float(column.mean())) + return torch.tensor(outcome_values, dtype=torch.float32) + + def sample_posterior(self, observation: torch.Tensor | None = None, num_samples: int = 5000) -> torch.Tensor: + """Sample the joint posterior over all factors at ``observation`` (default: see above). + + Returns a ``(num_samples, total_factor_dim)`` tensor laid out like ``theta`` — continuous + columns first (in original, denormalized units), then integer-coded categorical columns. + """ + assert self.posterior is not None, "Call fit() before sampling the posterior" + if observation is None: + observation = self.default_observation() + with torch.no_grad(): + normalized_samples = self.posterior.sample((num_samples,), x=observation) + return self._denormalize(normalized_samples) diff --git a/isaaclab_arena/analysis/sensitivity/analyzer_base.py b/isaaclab_arena/analysis/sensitivity/analyzer_base.py deleted file mode 100644 index 1d4151dbb3..0000000000 --- a/isaaclab_arena/analysis/sensitivity/analyzer_base.py +++ /dev/null @@ -1,56 +0,0 @@ -# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -from __future__ import annotations - -import numpy as np -from abc import ABC, abstractmethod - -from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec, SensitivityDataset - -SUCCESS_THRESHOLD = 0.5 -"""Outcome value at or above which a binary-outcome episode counts as a success. - -Shared by the analyzers (which successful-theta samples to fit) and the plotting (how -to split the rug), so the two cannot disagree on what "success" means. -""" - - -class BaseAnalyzer(ABC): - """Abstract base for sensitivity analyzers. - - Validates the dataset and outcome on construction. Subclasses implement ``fit`` and - ``categorical_marginal_probs``. - """ - - def __init__(self, dataset: SensitivityDataset, outcome_name: str): - self.dataset = dataset - self.outcome_name = outcome_name - assert ( - outcome_name in dataset.outcome_columns - ), f"Outcome {outcome_name!r} not found in schema; available: {list(dataset.outcome_columns)}" - assert len(dataset.schema.factors) > 0, "Schema declares no factors" - - @abstractmethod - def fit(self, training_batch_size: int = 50) -> None: - """Prepare the analyzer so its query methods can be called. - - An implementation may train an estimator or do nothing if its queries read from - the data directly. - """ - - @abstractmethod - def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: - """Return ``P(category | outcome=outcome_value)`` for one categorical factor. - - The result is a 1D numpy array of length ``len(factor.choices)`` summing to 1. - """ - - def _factor_spec(self, factor_name: str) -> FactorSpec: - """Return the ``FactorSpec`` for ``factor_name``, asserting it exists in the schema.""" - assert ( - factor_name in self.dataset.factor_columns - ), f"Factor {factor_name!r} not in schema; available: {list(self.dataset.factor_columns)}" - return next(factor for factor in self.dataset.schema.factors if factor.name == factor_name) diff --git a/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py b/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py deleted file mode 100644 index e2d08a3eb3..0000000000 --- a/isaaclab_arena/analysis/sensitivity/empirical_analyzer.py +++ /dev/null @@ -1,105 +0,0 @@ -# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -from __future__ import annotations - -import numpy as np - -from isaaclab_arena.analysis.sensitivity.analyzer_base import SUCCESS_THRESHOLD, BaseAnalyzer -from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset - - -class EmpiricalAnalyzer(BaseAnalyzer): - """Abstract base for analyzers that read the posterior directly from the data. - - Under a uniform prior, ``P(theta | success) ∝ P(success | theta)``, so the - distribution of successful-theta samples is the posterior. Outcome is treated as - binary: an episode is a success when its selected outcome column is - ``>= SUCCESS_THRESHOLD``. - """ - - def _success_mask(self) -> np.ndarray: - """Boolean array over episodes: True where the selected outcome counts as a success.""" - outcome_column_index = self.dataset.outcome_columns[self.outcome_name] - outcome_values = self.dataset.x[:, outcome_column_index].cpu().numpy() - return outcome_values >= SUCCESS_THRESHOLD - - -class KDEAnalyzer(EmpiricalAnalyzer): - """Analyzer for a single continuous factor with a binary outcome. - - Fits a Gaussian KDE over the theta values of successful episodes. Under a uniform - prior this density is proportional to ``P(success | theta)``, so its shape shows - which factor values drove success. - """ - - def __init__(self, dataset: SensitivityDataset, outcome_name: str): - super().__init__(dataset, outcome_name) - num_continuous = sum(1 for factor in dataset.schema.factors if factor.type == "continuous") - num_categorical = sum(1 for factor in dataset.schema.factors if factor.type == "categorical") - assert num_continuous == 1 and num_categorical == 0, ( - f"KDEAnalyzer requires exactly one continuous factor and no categoricals; got {num_continuous} continuous," - f" {num_categorical} categorical." - ) - self._kde = None - self._num_successful_samples = 0 - self._num_total_samples = 0 - - def fit(self, training_batch_size: int = 50) -> None: - """Fit a Gaussian KDE over the theta values of successful episodes.""" - from scipy.stats import gaussian_kde - - theta_values = self.dataset.theta[:, 0].cpu().numpy() - success_mask = self._success_mask() - self._num_total_samples = int(len(theta_values)) - self._num_successful_samples = int(success_mask.sum()) - - if self._num_successful_samples < 2: - print( - f"[WARN] KDEAnalyzer: only {self._num_successful_samples} successful samples" - f" / {self._num_total_samples} total — KDE undefined, marginal will be uniform." - ) - return - - successful_theta = theta_values[success_mask] - if float(np.std(successful_theta)) < 1e-9: - print( - "[WARN] KDEAnalyzer: all successful theta values are identical — KDE bandwidth" - " degenerate, marginal will be uniform." - ) - return - - self._kde = gaussian_kde(successful_theta) - print( - f"[INFO] KDEAnalyzer: fit Gaussian KDE on {self._num_successful_samples} successful" - f" theta samples / {self._num_total_samples} total." - ) - - def continuous_marginal_density( - self, factor_name: str, outcome_value: float, num_grid_points: int - ) -> tuple[np.ndarray, np.ndarray]: - """Evaluate the posterior density over the factor's range on a uniform grid. - - Returns ``(grid, density)``. Success conditioning (``outcome_value >= - SUCCESS_THRESHOLD``) returns the fitted KDE; otherwise a uniform density. - """ - factor_spec = self._factor_spec(factor_name) - assert factor_spec.type == "continuous", "KDEAnalyzer only handles continuous factors" - assert ( - factor_spec.range is not None and len(factor_spec.range) == 1 - ), "Continuous-factor marginal expects a populated 1D range" - range_low, range_high = factor_spec.range[0] - grid = np.linspace(range_low, range_high, num_grid_points) - - if outcome_value < SUCCESS_THRESHOLD or self._kde is None: - uniform_density = 1.0 / max(range_high - range_low, 1e-9) - return grid, np.full_like(grid, uniform_density) - return grid, self._kde(grid) - - def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: - raise NotImplementedError( - "KDEAnalyzer handles a single continuous factor only; it has no categorical" - " factors by construction. Mixed schemas dispatch to MNPEAnalyzer via make_analyzer." - ) diff --git a/isaaclab_arena/analysis/sensitivity/factory.py b/isaaclab_arena/analysis/sensitivity/factory.py deleted file mode 100644 index 4233382c21..0000000000 --- a/isaaclab_arena/analysis/sensitivity/factory.py +++ /dev/null @@ -1,47 +0,0 @@ -# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -from __future__ import annotations - -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from isaaclab_arena.analysis.sensitivity.analyzer_base import BaseAnalyzer - from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset - - -def make_analyzer(dataset: SensitivityDataset, outcome_name: str) -> BaseAnalyzer: - """Construct the analyzer matching the dataset's factor mix and outcome. - - - mixed continuous + categorical → :class:`MNPEAnalyzer` - - one continuous factor with a binary outcome → :class:`KDEAnalyzer` - - Other mixes are asserted against so an unsupported case fails loudly. The binary - check reads the outcome values off the dataset, since factors.yaml types all - outcomes as float. - """ - # Import lazily so importing this module stays light — torch/sbi load only when an - # analysis actually runs. - from isaaclab_arena.analysis.sensitivity.empirical_analyzer import KDEAnalyzer - from isaaclab_arena.analysis.sensitivity.posterior_analyzer import MNPEAnalyzer - - num_continuous_factors = sum(1 for factor in dataset.schema.factors if factor.type == "continuous") - num_categorical_factors = sum(1 for factor in dataset.schema.factors if factor.type == "categorical") - assert num_continuous_factors + num_categorical_factors > 0, "Schema declares no factors" - - # Mixed continuous + categorical → mixed neural posterior estimation. - if num_continuous_factors > 0 and num_categorical_factors > 0: - return MNPEAnalyzer(dataset, outcome_name) - - assert num_continuous_factors > 0, "Pure-categorical schemas are not supported yet." - - # All-continuous from here. Only the single-factor binary-outcome case is supported. - assert num_continuous_factors == 1, "Schemas with more than one continuous factor are not supported yet." - outcome_column_index = dataset.outcome_columns[outcome_name] - unique_outcome_values = set(dataset.x[:, outcome_column_index].flatten().tolist()) - assert unique_outcome_values.issubset( - {0.0, 1.0} - ), "A single continuous factor is only supported with a binary outcome." - return KDEAnalyzer(dataset, outcome_name) diff --git a/isaaclab_arena/analysis/sensitivity/generate_pdf_report.py b/isaaclab_arena/analysis/sensitivity/generate_pdf_report.py deleted file mode 100644 index 8a2ebedec9..0000000000 --- a/isaaclab_arena/analysis/sensitivity/generate_pdf_report.py +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -from __future__ import annotations - -import argparse - -from isaaclab_arena.analysis.sensitivity.pdf_report import generate_pdf_report - - -def main(): - parser = argparse.ArgumentParser( - description=( - "Build a single-PDF sensitivity report (outcome × factor grid of " - "marginal-posterior plots) from a (factors.yaml, episode_summary.jsonl) pair." - ) - ) - parser.add_argument("--factors_yaml", type=str, required=True, help="Path to factors.yaml.") - parser.add_argument( - "--episode_summary", type=str, required=True, help="Path to episode_summary.jsonl produced by eval_runner." - ) - parser.add_argument( - "--output_pdf", - type=str, - default="./sensitivity_report.pdf", - help="Output PDF file. Default: ./sensitivity_report.pdf.", - ) - args = parser.parse_args() - - generate_pdf_report(args.factors_yaml, args.episode_summary, args.output_pdf) - - -if __name__ == "__main__": - main() diff --git a/isaaclab_arena/analysis/sensitivity/generate_report.py b/isaaclab_arena/analysis/sensitivity/generate_report.py new file mode 100644 index 0000000000..5254b00e90 --- /dev/null +++ b/isaaclab_arena/analysis/sensitivity/generate_report.py @@ -0,0 +1,46 @@ +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +import argparse + +from isaaclab_arena.analysis.sensitivity.report import generate_report + + +def main(): + parser = argparse.ArgumentParser( + description=( + "Build a sensitivity report (one posterior-marginal panel per factor) from a " + "(factors.yaml, episode_summary.jsonl) pair. Output format follows the --output extension." + ) + ) + parser.add_argument("--factors_yaml", type=str, required=True, help="Path to factors.yaml.") + parser.add_argument( + "--episode_summary", type=str, required=True, help="Path to episode_summary.jsonl produced by eval_runner." + ) + parser.add_argument( + "--output", + type=str, + default="./sensitivity_report.png", + help="Output figure file; format follows the extension (.png, .pdf, …). Default: ./sensitivity_report.png.", + ) + parser.add_argument( + "--observation", + type=float, + nargs="*", + default=None, + help=( + "Outcome values to condition on, one per declared outcome (in schema order). " + "Defaults to 1.0 for binary outcomes and the mean otherwise." + ), + ) + args = parser.parse_args() + + generate_report(args.factors_yaml, args.episode_summary, args.output, observation=args.observation) + + +if __name__ == "__main__": + main() diff --git a/isaaclab_arena/analysis/sensitivity/pdf_report.py b/isaaclab_arena/analysis/sensitivity/pdf_report.py deleted file mode 100644 index 0855b04f88..0000000000 --- a/isaaclab_arena/analysis/sensitivity/pdf_report.py +++ /dev/null @@ -1,146 +0,0 @@ -# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -from __future__ import annotations - -import numpy as np -from pathlib import Path - -from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset -from isaaclab_arena.analysis.sensitivity.factory import make_analyzer -from isaaclab_arena.analysis.sensitivity.plotting import ( - draw_conditional_marginal, - draw_marginal, - draw_posterior_overlay, - draw_success_rate, -) - - -def generate_pdf_report( - factors_yaml_path: str | Path, - jsonl_path: str | Path, - output_pdf_path: str | Path, -) -> Path: - """Build a single-PDF sensitivity report covering every (outcome, factor) pair. - - Args: - factors_yaml_path: Schema file declaring factors and outcomes. - jsonl_path: episode_summary.jsonl from eval_runner. - output_pdf_path: Destination ``.pdf`` file (parent dirs created if absent). - - Returns: - The resolved output path. - """ - import matplotlib - - matplotlib.use("Agg") - import matplotlib.pyplot as plt - from matplotlib.backends.backend_pdf import PdfPages - - dataset = SensitivityDataset.from_files(Path(factors_yaml_path), Path(jsonl_path)) - outcomes = dataset.schema.outcomes - factors = dataset.schema.factors - # A low-cardinality categorical to split continuous success-rate curves by, so a - # light×object-style interaction shows per category instead of being averaged away. - split_factor = next( - (f.name for f in factors if f.type == "categorical" and f.choices and len(f.choices) <= 6), None - ) - n_rows, n_cols = len(outcomes), len(factors) - print(f"[INFO] PDF report: {n_rows} outcomes × {n_cols} factors ({dataset.num_episodes} episodes)") - - figure, axes = plt.subplots(n_rows, n_cols, figsize=(6.5 * n_cols, 4.5 * n_rows), squeeze=False) - for row_index, outcome in enumerate(outcomes): - outcome_is_binary = _is_binary_outcome(dataset, outcome.name) - # The fitted analyzer is only needed for categorical cells or non-binary outcomes. - # An all-continuous binary report is answered entirely by empirical success-rate plots. - needs_analyzer = (not outcome_is_binary) or any(factor.type != "continuous" for factor in factors) - analyzer = None - if needs_analyzer: - analyzer = make_analyzer(dataset, outcome.name) - print(f"[INFO] Fitting {type(analyzer).__name__} for outcome={outcome.name!r}") - analyzer.fit() - outcome_value = _default_outcome_value_for_analysis(dataset, outcome) - for col_index, factor in enumerate(factors): - ax = axes[row_index][col_index] - # Binary outcome + continuous factor: the empirical success-rate curve reads - # directly off a 0-1 axis and is correct regardless of how the factor was sampled. - if outcome_is_binary and factor.type == "continuous": - draw_success_rate(ax, dataset, factor.name, outcome.name, group_by=split_factor) - title = f"{outcome.name} vs {factor.name}" - if split_factor: - title += f" (split by {split_factor})" - ax.set_title(title, fontsize=10) - elif factor.type == "continuous": - # Continuous outcome: no single success value to condition on, so sweep the - # outcome value and overlay the MNPE posteriors to show the conditional shift. - draw_conditional_marginal(ax, analyzer, factor.name) - ax.set_title(f"{outcome.name} vs {factor.name}\n(MNPE posterior, swept {outcome.name})", fontsize=10) - else: - draw_marginal(ax, analyzer, factor.name, outcome_value=outcome_value) - ax.set_title( - f"{outcome.name} vs {factor.name}\n(conditioned on {outcome.name}={outcome_value:g})", fontsize=10 - ) - - slice_info = dataset.schema.slice - slice_label = f"{slice_info.policy} / {slice_info.task} / {slice_info.embodiment}" - # Two lines so the title doesn't clip on narrow (single-factor) figures. - figure.suptitle( - f"Sensitivity report — {dataset.num_episodes} episodes\n{slice_label}", - fontsize=12, - fontweight="bold", - ) - figure.tight_layout(rect=[0, 0, 1, 0.94]) # leave room for the two-line suptitle - - # Page 2 (robolab-style): P(factor | success=1) as filled KDE overlays per group, one - # panel per continuous factor, conditioned on the first binary ("success") outcome. - continuous_factors = [factor for factor in factors if factor.type == "continuous"] - binary_outcomes = [outcome for outcome in outcomes if _is_binary_outcome(dataset, outcome.name)] - posterior_figure = None - if continuous_factors and binary_outcomes: - success_outcome = binary_outcomes[0] - posterior_figure, posterior_axes = plt.subplots( - 1, len(continuous_factors), figsize=(6.5 * len(continuous_factors), 4.5), squeeze=False - ) - for col_index, factor in enumerate(continuous_factors): - ax = posterior_axes[0][col_index] - draw_posterior_overlay(ax, dataset, factor.name, success_outcome.name, group_by=split_factor) - title = f"P({factor.name} | {success_outcome.name}=1)" - if split_factor: - title += f" (by {split_factor})" - ax.set_title(title, fontsize=10) - posterior_figure.suptitle( - f"Posterior densities — Observation: {success_outcome.name}=1\n{slice_label}", - fontsize=12, - fontweight="bold", - ) - posterior_figure.tight_layout(rect=[0, 0, 1, 0.92]) - - output_pdf_path = Path(output_pdf_path) - output_pdf_path.parent.mkdir(parents=True, exist_ok=True) - with PdfPages(output_pdf_path) as pdf: - pdf.savefig(figure) - if posterior_figure is not None: - pdf.savefig(posterior_figure) - plt.close("all") - print(f"[INFO] Wrote report → {output_pdf_path}") - return output_pdf_path - - -def _is_binary_outcome(dataset: SensitivityDataset, outcome_name: str) -> bool: - """True iff the outcome column only ever takes values in ``{0, 1}``.""" - outcome_column_index = dataset.outcome_columns[outcome_name] - return set(dataset.x[:, outcome_column_index].flatten().tolist()).issubset({0.0, 1.0}) - - -def _default_outcome_value_for_analysis(dataset: SensitivityDataset, outcome) -> float: - """Pick a sensible value to condition the posterior on for this outcome. - - Binary outcomes (only ``{0, 1}`` observed) → ``1.0`` (the "success" branch). - Continuous outcomes → empirical median; a "typical case" value always inside the data range. - """ - if _is_binary_outcome(dataset, outcome.name): - return 1.0 - outcome_column_index = dataset.outcome_columns[outcome.name] - return float(np.median(dataset.x[:, outcome_column_index].cpu().numpy())) diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index 3b51c2359a..58e854833b 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -8,68 +8,62 @@ import numpy as np from typing import TYPE_CHECKING -from isaaclab_arena.analysis.sensitivity.analyzer_base import SUCCESS_THRESHOLD - if TYPE_CHECKING: - from isaaclab_arena.analysis.sensitivity.analyzer_base import BaseAnalyzer - from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec, SensitivityDataset + from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer + from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec -# Shared styling — kept in one place so the continuous and categorical drawers stay -# visually consistent (same colours mean the same thing across both plot types). -_POSTERIOR_COLOR = "steelblue" # analyzer posterior: density curve / left bar -_SUCCESS_COLOR = "seagreen" # outcome achieved: success rug / empirical-rate bar -_FAILURE_COLOR = "firebrick" # outcome not achieved: failure rug -_NEUTRAL_COLOR = "slategray" # rug for non-binary outcomes (no success/failure split) -_RUG_MARKER_SIZE = 80 # scatter marker size for empirical rug ticks -_RUG_SUCCESS_OFFSET = -0.05 # rug y-offset (× density.max()) for successes / neutral ticks -_RUG_FAILURE_OFFSET = -0.10 # rug y-offset (× density.max()) for failures +_CONTINUOUS_COLOR = "steelblue" +_CATEGORICAL_COLOR = "steelblue" +_MEAN_COLOR = "firebrick" -def plot_posterior_marginals( - dataset: SensitivityDataset, - outcome_name: str, - outcome_value: float | None = None, +def plot_marginals( + analyzer: SensitivityAnalyzer, + observation=None, + num_samples: int = 5000, output_path: str | None = None, ): - """robolab-style quick look: one marginal-posterior panel per factor in a single figure. + """Plot the posterior marginal of every factor in a single figure (robolab-style). - Fits the analyzer for ``outcome_name`` and draws each factor's marginal conditioned on a - single observation — the simple "did the posterior recover the relationship?" view, the - counterpart to the full grid in ``generate_pdf_report``. Operates on an in-memory dataset - (e.g. a synthetic simulator's output), so no ``factors.yaml`` / JSONL round-trip is needed. + Samples the joint posterior at ``observation`` (default: ``analyzer.default_observation()``), + then draws one panel per factor — a histogram for continuous factors, a probability bar + chart for categorical ones. This is the whole deliverable: one figure summarising which + factor values are consistent with the observed outcomes. Args: - dataset: The factors+outcomes to analyze. - outcome_name: Which outcome to condition the posteriors on. - outcome_value: Value to condition on. Defaults to ``1.0`` for a binary outcome - (the "success" branch) or the empirical median otherwise. - output_path: If given, save the figure here (parent dirs created). PNG/PDF/etc. + analyzer: A fitted :class:`SensitivityAnalyzer`. + observation: Outcome vector to condition on. Defaults to the analyzer's default. + num_samples: Number of posterior samples to draw. + output_path: If given, save the figure here. The format follows the path's + extension (``.png``, ``.pdf``, …); parent directories are created. Returns: - The matplotlib ``Figure`` (so the caller can show, tweak, or save it). + The matplotlib ``Figure``. """ import matplotlib.pyplot as plt - from isaaclab_arena.analysis.sensitivity.factory import make_analyzer - - analyzer = make_analyzer(dataset, outcome_name) - analyzer.fit() - - outcome_column = dataset.x[:, dataset.outcome_columns[outcome_name]].cpu().numpy() - if outcome_value is None: - is_binary = set(outcome_column.flatten().tolist()).issubset({0.0, 1.0}) - outcome_value = 1.0 if is_binary else float(np.median(outcome_column)) + if observation is None: + observation = analyzer.default_observation() + samples = analyzer.sample_posterior(observation, num_samples).cpu().numpy() + dataset = analyzer.dataset factors = dataset.schema.factors figure, axes = plt.subplots(1, len(factors), figsize=(6.0 * len(factors), 4.5), squeeze=False) for column_index, factor in enumerate(factors): ax = axes[0][column_index] - draw_marginal(ax, analyzer, factor.name, outcome_value=outcome_value) - ax.set_title(f"{factor.name} (| {outcome_name}={outcome_value:g})", fontsize=10) + factor_samples = samples[:, dataset.factor_columns[factor.name]].squeeze(-1) + if factor.type == "continuous": + _draw_continuous_marginal(ax, factor, factor_samples) + else: + _draw_categorical_marginal(ax, factor, factor_samples) + ax.set_title(factor.name, fontsize=11) slice_info = dataset.schema.slice + observation_label = ", ".join( + f"{outcome.name}={value:g}" for outcome, value in zip(dataset.schema.outcomes, observation.tolist()) + ) figure.suptitle( - f"Posterior marginals — {dataset.num_episodes} episodes ({outcome_name}={outcome_value:g})\n" + f"Posterior marginals — {dataset.num_episodes} episodes (observed: {observation_label})\n" f"{slice_info.policy} / {slice_info.task} / {slice_info.embodiment}", fontsize=12, fontweight="bold", @@ -84,371 +78,33 @@ def plot_posterior_marginals( return figure -def draw_marginal( - ax, - analyzer: BaseAnalyzer, - factor_name: str, - outcome_value: float = 1.0, - num_samples: int = 10_000, - num_grid_points: int = 200, -) -> None: - """Draw ``factor_name``'s marginal posterior onto ``ax``, dispatching by factor type. - - Sets axis labels, legend and grid but not the title — the caller titles the Axes. - """ - factor_spec = analyzer._factor_spec(factor_name) - if factor_spec.type == "continuous": - if not hasattr(analyzer, "continuous_marginal_density"): - raise NotImplementedError( - f"{type(analyzer).__name__} cannot plot continuous factors:" - " it does not implement continuous_marginal_density." - ) - _draw_continuous_marginal(ax, analyzer, factor_spec, outcome_value, num_grid_points) - elif factor_spec.type == "categorical": - _draw_categorical_marginal(ax, analyzer, factor_spec, outcome_value, num_samples) - else: - raise NotImplementedError(f"Unsupported factor type {factor_spec.type!r}") - - -def draw_success_rate( - ax, - dataset: SensitivityDataset, - factor_name: str, - outcome_name: str, - group_by: str | None = None, - num_bins: int = 12, -) -> None: - """Plot the empirical success rate of a binary outcome against a continuous factor. - - Bins the factor (log-spaced when it spans more than ~2 decades) and plots the per-bin - success rate with a 95% Wilson confidence band on a 0-1 axis. The rate is read straight - off the y-axis, and because each bin is counted independently the result is correct - regardless of how the factor was sampled. - - If ``group_by`` names a categorical factor, one curve is drawn per category (sharing the - same bins) so an interaction — e.g. the light gate differing by object — is visible - instead of being averaged away. - """ - factor_column_slice = dataset.factor_columns[factor_name] - factor_values = dataset.theta[:, factor_column_slice].squeeze(-1).cpu().numpy() - outcome_column_index = dataset.outcome_columns[outcome_name] - outcome_values = dataset.x[:, outcome_column_index].cpu().numpy() - - # Log-spaced bins when the factor is positive and spans many decades, so a wide-range - # factor (e.g. light) resolves its low end instead of collapsing into the first bin. - # Bins are shared across groups so the per-category curves are directly comparable. - range_low, range_high = float(factor_values.min()), float(factor_values.max()) - use_log_axis = range_low > 0 and np.log10(range_high / range_low) > 2 - if use_log_axis: - bin_edges = np.logspace(np.log10(range_low), np.log10(range_high), num_bins + 1) - else: - bin_edges = np.linspace(range_low, range_high, num_bins + 1) - - # One pooled curve by default, or one per category of the group_by factor. - if group_by is None: - groups = [(f"empirical {outcome_name}", np.ones(len(factor_values), dtype=bool))] - else: - group_spec = next(factor for factor in dataset.schema.factors if factor.name == group_by) - assert group_spec.choices is not None, f"group_by factor {group_by!r} must be categorical" - group_codes = dataset.theta[:, dataset.factor_columns[group_by]].squeeze(-1).long().cpu().numpy() - groups = [ - (f"{choice} (n={int((group_codes == code).sum())})", group_codes == code) - for code, choice in enumerate(group_spec.choices) - ] - - annotate_counts = group_by is None # per-bin n labels only when there's a single curve - for group_label, group_mask in groups: - centers, rates, ci_low, ci_high, counts = _binned_success_rate( - factor_values[group_mask], outcome_values[group_mask], bin_edges, use_log_axis - ) - if not centers: - continue - (line,) = ax.plot(centers, rates, "o-", linewidth=2, label=group_label) - ax.fill_between(centers, ci_low, ci_high, color=line.get_color(), alpha=0.15) - if annotate_counts: - for center_x, rate, count in zip(centers, rates, counts): - ax.text(center_x, min(rate + 0.04, 1.04), f"n={count}", ha="center", fontsize=7, color="gray") - - if use_log_axis: - ax.set_xscale("log") - ax.set_xlabel(factor_name) - ax.set_ylabel(f"{outcome_name} (success rate)") - ax.set_ylim(0, 1.1) - ax.legend(loc="best", fontsize=9) - ax.grid(alpha=0.3) - - -def _binned_success_rate(factor_values, outcome_values, bin_edges, use_log_axis): - """Per-bin success rate with a 95% Wilson confidence interval. - - Returns parallel lists ``(centers, rates, ci_low, ci_high, counts)``, skipping empty - bins. Centers are geometric (log axis) or arithmetic (linear axis) bin midpoints. - """ - z = 1.959963985 # 95% normal quantile - num_bins = len(bin_edges) - 1 - centers, rates, ci_low, ci_high, counts = [], [], [], [], [] - for bin_index in range(num_bins): - # Last bin is closed on the right so the maximum value isn't dropped. - upper = bin_edges[bin_index + 1] - in_bin = (factor_values >= bin_edges[bin_index]) & ( - factor_values <= upper if bin_index == num_bins - 1 else factor_values < upper - ) - num_in_bin = int(in_bin.sum()) - if num_in_bin == 0: - continue - rate = int((outcome_values[in_bin] >= SUCCESS_THRESHOLD).sum()) / num_in_bin - # Wilson score interval — stays inside [0, 1] even at rate 0/1 or small n. - denominator = 1 + z * z / num_in_bin - center = (rate + z * z / (2 * num_in_bin)) / denominator - margin = z / denominator * np.sqrt(rate * (1 - rate) / num_in_bin + z * z / (4 * num_in_bin * num_in_bin)) - if use_log_axis: - centers.append(float(np.sqrt(bin_edges[bin_index] * upper))) - else: - centers.append(float(0.5 * (bin_edges[bin_index] + upper))) - rates.append(rate) - ci_low.append(max(0.0, center - margin)) - ci_high.append(min(1.0, center + margin)) - counts.append(num_in_bin) - return centers, rates, ci_low, ci_high, counts - - -def draw_conditional_marginal( - ax, - analyzer: BaseAnalyzer, - factor_name: str, - num_curves: int = 3, - num_grid_points: int = 200, -) -> None: - """Overlay the posterior ``P(factor | outcome=v)`` at several outcome values ``v``. - - For a continuous outcome there is no single "success" value to condition on, so the - conditioning value is swept across the observed outcome range (5th-95th percentile) - and the resulting posteriors are overlaid. The shift between curves shows how the - factor's posterior depends on the outcome — e.g. fast vs slow episodes favouring - brighter vs darker light — the conditional query only a posterior model provides. - """ - from matplotlib import colormaps - - factor_spec = analyzer._factor_spec(factor_name) - outcome_column_index = analyzer.dataset.outcome_columns[analyzer.outcome_name] - outcome_data = analyzer.dataset.x[:, outcome_column_index].cpu().numpy() - low_value, high_value = np.percentile(outcome_data, [5, 95]) - conditioning_values = np.linspace(low_value, high_value, num_curves) - colors = colormaps["coolwarm"](np.linspace(0, 1, num_curves)) - - for conditioning_value, color in zip(conditioning_values, colors): - grid, density = analyzer.continuous_marginal_density(factor_name, float(conditioning_value), num_grid_points) - ax.plot(grid, density, color=color, linewidth=2, label=f"{analyzer.outcome_name}={conditioning_value:g}") - - # Multi-decade factor: show the conditioning shift on a log axis instead of cramping it. - if ( - factor_spec.range is not None - and factor_spec.range[0][0] > 0 - and np.log10(factor_spec.range[0][1] / factor_spec.range[0][0]) > 2 - ): - ax.set_xscale("log") - ax.set_xlabel(factor_name) - ax.set_ylabel("posterior density") - ax.legend(loc="best", fontsize=8, title=f"conditioned on {analyzer.outcome_name}") - ax.grid(alpha=0.3) - - -def draw_posterior_overlay( - ax, - dataset: SensitivityDataset, - factor_name: str, - outcome_name: str, - group_by: str | None = None, - num_grid_points: int = 200, -) -> None: - """Overlay ``P(factor | outcome=1)`` as filled KDE curves, one per group (robolab style). - - For each group, fits a Gaussian KDE to the factor values of the *successful* episodes - (``outcome >= SUCCESS_THRESHOLD``) — the posterior over the factor given success under a - uniform prior. Curves are filled and overlaid for comparison. A wide-range factor is fit - in log space so the density is sharp instead of crammed at the low end. - """ - from scipy.stats import gaussian_kde - - factor_column_slice = dataset.factor_columns[factor_name] - factor_values = dataset.theta[:, factor_column_slice].squeeze(-1).cpu().numpy() - outcome_column_index = dataset.outcome_columns[outcome_name] - success_mask = dataset.x[:, outcome_column_index].cpu().numpy() >= SUCCESS_THRESHOLD - - range_low, range_high = float(factor_values.min()), float(factor_values.max()) - use_log = range_low > 0 and np.log10(range_high / range_low) > 2 - to_fit_space = (lambda v: np.log10(v)) if use_log else (lambda v: v) - grid_fit = np.linspace(to_fit_space(range_low), to_fit_space(range_high), num_grid_points) - grid_display = 10**grid_fit if use_log else grid_fit - - if group_by is None: - groups = [(f"{outcome_name}=1", np.ones(len(factor_values), dtype=bool))] - else: - group_spec = next(factor for factor in dataset.schema.factors if factor.name == group_by) - assert group_spec.choices is not None, f"group_by factor {group_by!r} must be categorical" - group_codes = dataset.theta[:, dataset.factor_columns[group_by]].squeeze(-1).long().cpu().numpy() - groups = [(choice, group_codes == code) for code, choice in enumerate(group_spec.choices)] - - # Draw the curves, collecting each group's successful samples for a rug below the axis. - max_density = 0.0 - rug_series = [] # (sample display-values, color) per group - for group_label, group_mask in groups: - successful_factor = factor_values[group_mask & success_mask] - fit_values = to_fit_space(successful_factor) - if len(successful_factor) < 2 or float(np.std(fit_values)) < 1e-9: - continue # KDE undefined for fewer than 2 points or zero spread - density = gaussian_kde(fit_values)(grid_fit) - max_density = max(max_density, float(density.max())) - (line,) = ax.plot(grid_display, density, linewidth=2, label=f"{group_label} (n={len(successful_factor)})") - ax.fill_between(grid_display, 0, density, color=line.get_color(), alpha=0.3) - rug_series.append((successful_factor, line.get_color())) - - # Rug of the actual successful samples per group, stacked just below the axis: it shows - # where the data really is, so the KDE's taper reads as thinning data, not as signal. - for rug_index, (sample_values, color) in enumerate(rug_series): - rug_y = -(0.03 + 0.025 * rug_index) * max_density - ax.scatter( - sample_values, np.full(len(sample_values), rug_y), marker="|", color=color, s=_RUG_MARKER_SIZE, alpha=0.5 - ) - - if use_log: - ax.set_xscale("log") - if max_density > 0: - ax.set_ylim(-(0.03 + 0.025 * len(rug_series)) * max_density, max_density * 1.1) - ax.set_xlabel(factor_name) - ax.set_ylabel("posterior density") - ax.legend(loc="best", fontsize=8) - ax.grid(alpha=0.3) - - -def _draw_continuous_marginal( - ax, - analyzer: BaseAnalyzer, - factor_spec: FactorSpec, - outcome_value: float, - num_grid_points: int, -) -> None: - """Draw a continuous factor's marginal posterior onto ``ax`` as a density curve. - - The blue curve shows ``P(factor_value | outcome=outcome_value)`` from the analyzer. - Below the x-axis is an empirical "rug" — small vertical ticks at the actual recorded - theta values, coloured green for episodes where the outcome was achieved (``≥ 0.5``) - and red for episodes where it was not. The rug lets a human eyeball whether the - smooth posterior actually agrees with where the successful episodes lived. - """ - grid, density = analyzer.continuous_marginal_density(factor_spec.name, outcome_value, num_grid_points) - factor_column_slice = analyzer.dataset.factor_columns[factor_spec.name] - outcome_column_index = analyzer.dataset.outcome_columns[analyzer.outcome_name] - empirical_theta_values = analyzer.dataset.theta[:, factor_column_slice].squeeze(-1).cpu().numpy() - empirical_outcomes = analyzer.dataset.x[:, outcome_column_index].cpu().numpy() - - ax.plot( - grid, - density, - color=_POSTERIOR_COLOR, - linewidth=2, - label=f"P({factor_spec.name} | {analyzer.outcome_name}={outcome_value:g})", - ) - ax.fill_between(grid, 0, density, color=_POSTERIOR_COLOR, alpha=0.2) - - # Binary outcomes: split the rug green/red at the success threshold (successes vs failures). - # Continuous outcomes: the threshold is meaningless, so show one neutral rug. - is_binary_outcome = set(empirical_outcomes.flatten().tolist()).issubset({0.0, 1.0}) - if is_binary_outcome: - success_mask = empirical_outcomes >= SUCCESS_THRESHOLD - ax.scatter( - empirical_theta_values[success_mask], - np.full(success_mask.sum(), _RUG_SUCCESS_OFFSET * density.max()), - marker="|", - color=_SUCCESS_COLOR, - s=_RUG_MARKER_SIZE, - label=f"{analyzer.outcome_name} = 1 (n={success_mask.sum()})", - ) - ax.scatter( - empirical_theta_values[~success_mask], - np.full((~success_mask).sum(), _RUG_FAILURE_OFFSET * density.max()), - marker="|", - color=_FAILURE_COLOR, - s=_RUG_MARKER_SIZE, - label=f"{analyzer.outcome_name} = 0 (n={(~success_mask).sum()})", - ) - else: - ax.scatter( - empirical_theta_values, - np.full(len(empirical_theta_values), _RUG_SUCCESS_OFFSET * density.max()), - marker="|", - color=_NEUTRAL_COLOR, - s=_RUG_MARKER_SIZE, - label=f"observed samples (n={len(empirical_theta_values)})", - ) - ax.set_xlabel(factor_spec.name) +def _draw_continuous_marginal(ax, factor: FactorSpec, factor_samples: np.ndarray) -> None: + """Histogram of a continuous factor's posterior samples, with a mean line.""" + range_low, range_high = factor.range[0] + ax.hist(factor_samples, bins=50, range=(range_low, range_high), color=_CONTINUOUS_COLOR, alpha=0.7, density=True) + sample_mean = float(np.mean(factor_samples)) + ax.axvline(sample_mean, color=_MEAN_COLOR, linestyle="--", linewidth=2, label=f"mean = {sample_mean:.3g}") + ax.set_xlabel(factor.name) ax.set_ylabel("posterior density") ax.legend(loc="best", fontsize=9) ax.grid(alpha=0.3) -def _draw_categorical_marginal( - ax, - analyzer: BaseAnalyzer, - factor_spec: FactorSpec, - outcome_value: float, - num_samples: int, -) -> None: - """Draw a categorical factor's marginal onto ``ax`` as side-by-side bars per category. +def _draw_categorical_marginal(ax, factor: FactorSpec, factor_samples: np.ndarray) -> None: + """Bar chart of a categorical factor's posterior probability per choice. - Blue bar: the analyzer's ``P(category | outcome)``. Green bar: the empirical - per-category outcome rate from the raw data, annotated with its sample count ``n``. + sbi returns categorical columns as floats over the integer-code support, so samples are + rounded to the nearest code in ``[0, num_choices - 1]`` and tallied into frequencies. """ - assert factor_spec.choices is not None - choices = factor_spec.choices - num_choices = len(choices) - factor_column_slice = analyzer.dataset.factor_columns[factor_spec.name] - outcome_column_index = analyzer.dataset.outcome_columns[analyzer.outcome_name] - - # Posterior probs come from the analyzer; empirical rate and counts are raw data, - # rendered alongside as a sanity reference. - posterior_probabilities = analyzer.categorical_marginal_probs(factor_spec.name, outcome_value, num_samples) - - empirical_theta_codes = analyzer.dataset.theta[:, factor_column_slice].squeeze(-1).long().cpu().numpy() - empirical_outcomes = analyzer.dataset.x[:, outcome_column_index].cpu().numpy() - empirical_rates = np.zeros(num_choices) - empirical_counts = np.zeros(num_choices, dtype=int) - for code in range(num_choices): - category_mask = empirical_theta_codes == code - empirical_counts[code] = int(category_mask.sum()) - if category_mask.any(): - empirical_rates[code] = float((empirical_outcomes[category_mask] >= SUCCESS_THRESHOLD).mean()) - - bar_x_positions = np.arange(num_choices) - bar_width = 0.4 - ax.bar( - bar_x_positions - bar_width / 2, - posterior_probabilities, - bar_width, - color=_POSTERIOR_COLOR, - alpha=0.8, - label=f"P(category | {analyzer.outcome_name}={outcome_value:g})", - ) - ax.bar( - bar_x_positions + bar_width / 2, - empirical_rates, - bar_width, - color=_SUCCESS_COLOR, - alpha=0.7, - label=f"empirical {analyzer.outcome_name} rate per category", - ) - for category_index, count in enumerate(empirical_counts): - ax.text( - category_index + bar_width / 2, - empirical_rates[category_index] + 0.02, - f"n={count}", - ha="center", - fontsize=8, - ) - - ax.set_xticks(bar_x_positions) - ax.set_xticklabels(choices, rotation=30, ha="right") - ax.set_ylabel("probability") + assert factor.choices is not None + num_choices = len(factor.choices) + codes = np.clip(np.round(factor_samples), 0, num_choices - 1).astype(int) + probabilities = np.bincount(codes, minlength=num_choices) / len(codes) + + ax.bar(range(num_choices), probabilities, color=_CATEGORICAL_COLOR, alpha=0.8) + ax.set_xticks(range(num_choices)) + ax.set_xticklabels(factor.choices, rotation=30, ha="right") + ax.set_xlabel(factor.name) + ax.set_ylabel("posterior probability") ax.set_ylim(0, 1.05) - ax.legend(loc="best", fontsize=9) ax.grid(alpha=0.3, axis="y") diff --git a/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py b/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py deleted file mode 100644 index 0409bc3322..0000000000 --- a/isaaclab_arena/analysis/sensitivity/posterior_analyzer.py +++ /dev/null @@ -1,127 +0,0 @@ -# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -from __future__ import annotations - -import numpy as np -import torch - -from isaaclab_arena.analysis.sensitivity.analyzer_base import BaseAnalyzer -from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset - - -class PosteriorAnalyzer(BaseAnalyzer): - """Base for sbi-driven analyzers that fit a neural posterior estimator. - - Subclasses choose the sbi inference class via ``_inference_cls``; the training loop - and the density/sample queries are shared. After ``fit()``, ``self.posterior`` is an - sbi posterior supporting ``sample(shape, x=...)`` and ``log_prob(theta, x=...)``. - """ - - def __init__(self, dataset: SensitivityDataset, outcome_name: str): - super().__init__(dataset, outcome_name) - self.posterior = None - - def _inference_cls(self): - """Return the sbi inference *class* to train with (e.g. ``sbi.inference.MNPE``).""" - raise NotImplementedError("PosteriorAnalyzer subclasses must implement _inference_cls") - - def _make_inference(self): - """Instantiate the chosen sbi inference class on the dataset's uniform prior.""" - return self._inference_cls()(prior=self.dataset.prior) - - def fit(self, training_batch_size: int = 50) -> None: - """Train the sbi estimator and store the posterior on ``self``. - - Conditions on the single outcome column named by ``outcome_name``. - """ - outcome_column_index = self.dataset.outcome_columns[self.outcome_name] - selected_outcome_column = self.dataset.x[:, outcome_column_index : outcome_column_index + 1] - - print( - f"[INFO] {type(self).__name__}: fitting on {self.dataset.theta.shape[0]} samples" - f" (theta dim={self.dataset.theta.shape[1]}," - f" x dim={selected_outcome_column.shape[1]})." - ) - inference = self._make_inference() - inference.append_simulations(self.dataset.theta, selected_outcome_column) - density_estimator = inference.train(training_batch_size=training_batch_size) - self.posterior = inference.build_posterior(density_estimator) - - def continuous_marginal_density( - self, factor_name: str, outcome_value: float, num_grid_points: int - ) -> tuple[np.ndarray, np.ndarray]: - """Evaluate ``P(factor_value | outcome=outcome_value)`` over the factor's range. - - Returns ``(grid, density)`` as numpy arrays of length ``num_grid_points``. - """ - assert self.posterior is not None, "Call fit() before querying the posterior" - factor_spec = self._factor_spec(factor_name) - assert ( - factor_spec.type == "continuous" - ), f"continuous_marginal_density expects a continuous factor; {factor_name!r} is {factor_spec.type!r}" - assert ( - factor_spec.range is not None and len(factor_spec.range) == 1 - ), "Continuous-factor marginal expects a populated 1D range" - - factor_column_slice = self.dataset.factor_columns[factor_name] - observed_outcome = torch.tensor([outcome_value], dtype=torch.float32) - range_low, range_high = factor_spec.range[0] - - if self.dataset.theta.shape[1] == 1: - # This is the only factor: evaluate log_prob directly on a grid (exact, no sampling). - grid_tensor = torch.linspace(range_low, range_high, num_grid_points, dtype=torch.float32).unsqueeze(1) - with torch.no_grad(): - log_probabilities = self.posterior.log_prob(grid_tensor, x=observed_outcome) - density_numpy = torch.exp(log_probabilities).cpu().numpy() - grid_numpy = grid_tensor.squeeze(-1).cpu().numpy() - else: - # Other factors present: sample, take this factor's column (marginalizing the - # rest), then histogram-and-interpolate onto the grid. - with torch.no_grad(): - posterior_samples = self.posterior.sample((10_000,), x=observed_outcome) - factor_column_samples = posterior_samples[:, factor_column_slice].squeeze(-1).cpu().numpy() - grid_numpy = np.linspace(range_low, range_high, num_grid_points) - histogram_density, bin_edges = np.histogram( - factor_column_samples, bins=40, range=(range_low, range_high), density=True - ) - density_numpy = np.interp(grid_numpy, 0.5 * (bin_edges[:-1] + bin_edges[1:]), histogram_density) - - return grid_numpy, density_numpy - - def categorical_marginal_probs(self, factor_name: str, outcome_value: float, num_samples: int) -> np.ndarray: - """Estimate ``P(category | outcome=outcome_value)`` by sampling the trained posterior. - - Returns a length-``num_choices`` numpy array that sums to 1. - """ - assert self.posterior is not None, "Call fit() before querying the posterior" - factor_spec = self._factor_spec(factor_name) - assert factor_spec.type == "categorical" - assert factor_spec.choices is not None - factor_column_slice = self.dataset.factor_columns[factor_name] - num_choices = len(factor_spec.choices) - - observed_outcome = torch.tensor([outcome_value], dtype=torch.float32) - with torch.no_grad(): - posterior_samples = self.posterior.sample((num_samples,), x=observed_outcome) - # sbi returns the categorical column as floats over the BoxUniform support; round - # to the nearest code in [0, num_choices - 1] and tally into normalized frequencies. - factor_column_samples = posterior_samples[:, factor_column_slice].squeeze(-1).cpu().numpy() - clipped_codes = np.clip(np.round(factor_column_samples), 0, num_choices - 1).astype(int) - return np.bincount(clipped_codes, minlength=num_choices) / num_samples - - -class MNPEAnalyzer(PosteriorAnalyzer): - """Analyzer for schemas that mix continuous and categorical factors. - - Trains ``sbi.inference.MNPE``, whose mixed density estimator handles continuous and - categorical theta columns together. Requires at least one continuous factor. - """ - - def _inference_cls(self): - # Import sbi lazily — it is heavy and only needed once an analysis actually fits. - from sbi.inference import MNPE - - return MNPE diff --git a/isaaclab_arena/analysis/sensitivity/report.py b/isaaclab_arena/analysis/sensitivity/report.py new file mode 100644 index 0000000000..5d53ead24c --- /dev/null +++ b/isaaclab_arena/analysis/sensitivity/report.py @@ -0,0 +1,51 @@ +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +import torch +from pathlib import Path + +from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer +from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset +from isaaclab_arena.analysis.sensitivity.plotting import plot_marginals + + +def generate_report( + factors_yaml_path: str | Path, + jsonl_path: str | Path, + output_path: str | Path, + observation: list[float] | None = None, +) -> Path: + """Build a sensitivity report from a ``factors.yaml`` / ``episode_summary.jsonl`` pair. + + Loads the data, fits a :class:`SensitivityAnalyzer`, and saves a single posterior-marginals + figure. The output format follows the ``output_path`` extension (``.png``, ``.pdf``, …). + + Args: + factors_yaml_path: Schema file declaring factors and outcomes. + jsonl_path: ``episode_summary.jsonl`` produced by eval_runner. + output_path: Destination figure file (parent dirs created if absent). + observation: Outcome values to condition on, one per declared outcome. Defaults to + the analyzer's default (1.0 for binary outcomes, the mean otherwise). + + Returns: + The resolved output path. + """ + import matplotlib + + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + dataset = SensitivityDataset.from_files(Path(factors_yaml_path), Path(jsonl_path)) + analyzer = SensitivityAnalyzer(dataset) + analyzer.fit() + + observation_tensor = None if observation is None else torch.tensor(observation, dtype=torch.float32) + output_path = Path(output_path) + plot_marginals(analyzer, observation=observation_tensor, output_path=str(output_path)) + plt.close("all") + print(f"[INFO] Wrote report → {output_path}") + return output_path diff --git a/isaaclab_arena/tests/test_sensitivity_analysis.py b/isaaclab_arena/tests/test_sensitivity_analysis.py index a98497298b..cda7e876e2 100644 --- a/isaaclab_arena/tests/test_sensitivity_analysis.py +++ b/isaaclab_arena/tests/test_sensitivity_analysis.py @@ -5,11 +5,11 @@ """End-to-end sensitivity-analysis tests on synthetic data with a known ground truth. -Each test fits a *real* analyzer (via ``make_analyzer``) on a dataset whose factor→outcome -relationship is planted by ``synthetic_sensitivity`` (brighter light and oak raise success), -then asserts the recovered posterior reflects that relationship. The data is built in memory, -so these run on CPU without Isaac Sim. They cover the inference layer the way robolab's -``simple_simulator`` does — not the YAML/JSONL parsing, which has no bearing on inference. +Each test fits a ``SensitivityAnalyzer`` on a dataset whose factor→outcome relationship is +planted by ``synthetic_sensitivity`` (brighter light, smaller grasp offset, and oak raise +success), then asserts the posterior conditioned on success recovers that relationship. The +data is built in memory, so these run on CPU without Isaac Sim. They mirror robolab's +generated-data demo: MNPE for mixed schemas, NPE for continuous-only (with 2-D theta). """ from __future__ import annotations @@ -17,68 +17,58 @@ import numpy as np import torch -from isaaclab_arena.analysis.sensitivity.empirical_analyzer import KDEAnalyzer -from isaaclab_arena.analysis.sensitivity.factory import make_analyzer -from isaaclab_arena.analysis.sensitivity.posterior_analyzer import MNPEAnalyzer +from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer from isaaclab_arena.tests.utils.synthetic_sensitivity import ( + GRASP_OFFSET_RANGE, LIGHT_RANGE, MATERIAL_BASE_LOGIT, make_continuous_dataset, make_mixed_dataset, ) -_OUTCOME = "success" -_SUCCESS = 1.0 -_FAILURE = 0.0 -_NUM_GRID_POINTS = 200 _NUM_SAMPLES = 5000 -_LIGHT_LOW, _LIGHT_HIGH = LIGHT_RANGE -_LIGHT_MIDPOINT = 0.5 * (_LIGHT_LOW + _LIGHT_HIGH) +_LIGHT_MIDPOINT = 0.5 * (LIGHT_RANGE[0] + LIGHT_RANGE[1]) +_OFFSET_MIDPOINT = 0.5 * (GRASP_OFFSET_RANGE[0] + GRASP_OFFSET_RANGE[1]) -def _density_weighted_mean(grid: np.ndarray, density: np.ndarray) -> float: - """Mean light value under a (grid, density) marginal — i.e. where the posterior mass sits.""" - return float(np.sum(grid * density) / np.sum(density)) - - -def test_kde_recovers_brighter_light_drives_success(): - """A single continuous factor: successful episodes should concentrate at high light.""" - dataset = make_continuous_dataset(seed=0) - analyzer = make_analyzer(dataset, _OUTCOME) - assert isinstance(analyzer, KDEAnalyzer) - - analyzer.fit() - grid, success_density = analyzer.continuous_marginal_density("light_intensity", _SUCCESS, _NUM_GRID_POINTS) - - # Ground truth plants brighter ⇒ more successful, so the success posterior must skew well - # above the midpoint of the light range (a uniform posterior would sit exactly at it). - assert _density_weighted_mean(grid, success_density) > _LIGHT_MIDPOINT + 0.1 * (_LIGHT_HIGH - _LIGHT_LOW) +def _factor_samples(analyzer: SensitivityAnalyzer, samples: torch.Tensor, factor_name: str) -> np.ndarray: + """Pull one factor's column out of a posterior-sample tensor as a 1-D numpy array.""" + return samples[:, analyzer.dataset.factor_columns[factor_name]].squeeze(-1).cpu().numpy() def test_mnpe_recovers_light_and_material_effects(): - """Mixed continuous + categorical: recover both the light trend and the material ranking.""" + """Mixed continuous + categorical (MNPE): recover the light trend and the material ranking.""" dataset = make_mixed_dataset(seed=0) - analyzer = make_analyzer(dataset, _OUTCOME) - assert isinstance(analyzer, MNPEAnalyzer) + analyzer = SensitivityAnalyzer(dataset) + assert analyzer._inference_cls().__name__ == "MNPE", "mixed schema should select MNPE" - torch.manual_seed(0) # make the posterior sampling below reproducible + torch.manual_seed(0) analyzer.fit() + samples = analyzer.sample_posterior(num_samples=_NUM_SAMPLES) # conditions on success=1 by default - # Continuous effect: light conditioned on success should sit higher than on failure. - grid, success_density = analyzer.continuous_marginal_density("light_intensity", _SUCCESS, _NUM_GRID_POINTS) - _, failure_density = analyzer.continuous_marginal_density("light_intensity", _FAILURE, _NUM_GRID_POINTS) - assert _density_weighted_mean(grid, success_density) > _density_weighted_mean(grid, failure_density) + # Continuous effect: brighter light is planted to raise success. + assert _factor_samples(analyzer, samples, "light_intensity").mean() > _LIGHT_MIDPOINT # Categorical effect: oak is the planted best material, bamboo the worst. materials = list(MATERIAL_BASE_LOGIT) - oak_index, bamboo_index = materials.index("oak"), materials.index("bamboo") - success_probs = analyzer.categorical_marginal_probs("table_material", _SUCCESS, _NUM_SAMPLES) - failure_probs = analyzer.categorical_marginal_probs("table_material", _FAILURE, _NUM_SAMPLES) - - assert success_probs.argmax() == oak_index, f"expected oak most likely given success, got {success_probs}" - assert success_probs[oak_index] > success_probs[bamboo_index] - # Conditioning works in both directions: oak is overrepresented among successes, - # bamboo among failures, relative to the other outcome. - assert success_probs[oak_index] > failure_probs[oak_index] - assert failure_probs[bamboo_index] > success_probs[bamboo_index] + material_codes = np.clip(np.round(_factor_samples(analyzer, samples, "table_material")), 0, len(materials) - 1) + probabilities = np.bincount(material_codes.astype(int), minlength=len(materials)) / len(material_codes) + assert materials[int(probabilities.argmax())] == "oak", f"expected oak most likely, got {probabilities}" + assert probabilities[materials.index("oak")] > probabilities[materials.index("bamboo")] + + +def test_npe_recovers_two_continuous_effects(): + """Two continuous factors (NPE): recover that bright light and a small grasp offset drive success.""" + dataset = make_continuous_dataset(seed=0) + analyzer = SensitivityAnalyzer(dataset) + assert analyzer._inference_cls().__name__.startswith("NPE"), "continuous-only schema should select NPE" + + torch.manual_seed(0) + analyzer.fit() + samples = analyzer.sample_posterior(num_samples=_NUM_SAMPLES) # conditions on success=1 by default + + # Brighter light raises success → light posterior skews high. + assert _factor_samples(analyzer, samples, "light_intensity").mean() > _LIGHT_MIDPOINT + # A smaller grasp offset raises success → offset posterior skews low. + assert _factor_samples(analyzer, samples, "grasp_offset").mean() < _OFFSET_MIDPOINT diff --git a/isaaclab_arena/tests/utils/synthetic_sensitivity.py b/isaaclab_arena/tests/utils/synthetic_sensitivity.py index 8d483c845c..5bd135a2d4 100644 --- a/isaaclab_arena/tests/utils/synthetic_sensitivity.py +++ b/isaaclab_arena/tests/utils/synthetic_sensitivity.py @@ -9,14 +9,17 @@ factors from a uniform prior, runs them through a fixed generative model, and returns a :class:`SensitivityDataset` of in-memory ``theta`` / ``x`` tensors — no ``factors.yaml`` or ``episode_summary.jsonl`` round-trip. Because the planted relationship is known, a test can -fit an analyzer on the data and assert the recovered posterior reflects it. +fit a :class:`SensitivityAnalyzer` on the data and assert the recovered posterior reflects it. Ground truth (single-sourced in the constants below): - - ``light_intensity`` is continuous over ``LIGHT_RANGE``; higher light raises success - (``LIGHT_WEIGHT > 0``). + - ``light_intensity`` is continuous; higher light raises success (``LIGHT_WEIGHT > 0``). + - ``grasp_offset`` is continuous; a *smaller* offset raises success (``OFFSET_WEIGHT > 0``). - ``table_material`` is categorical; ``MATERIAL_BASE_LOGIT`` makes oak the most successful material and bamboo the least. - ``success`` is a binary outcome drawn from ``Bernoulli(sigmoid(logit))``. + +``make_mixed_dataset`` exercises the MNPE path (continuous + categorical); ``make_continuous_dataset`` +exercises the NPE path with two continuous factors (NPE restricts to a Gaussian on 1-D theta). """ from __future__ import annotations @@ -37,6 +40,12 @@ LIGHT_WEIGHT: float = 2.5 """Success-logit gain per unit of normalized light. Positive ⇒ brighter is more successful.""" +GRASP_OFFSET_RANGE: tuple[float, float] = (0.0, 0.2) +"""Range (metres) of the continuous ``grasp_offset`` factor.""" + +OFFSET_WEIGHT: float = 2.5 +"""Success-logit gain per unit of normalized offset. Subtracted ⇒ a smaller offset is more successful.""" + MATERIAL_BASE_LOGIT: dict[str, float] = {"oak": 1.5, "walnut": 0.0, "bamboo": -1.5} """Per-material base success logit. Ordered best→worst, so oak should dominate the posterior.""" @@ -44,12 +53,18 @@ _SLICE = SliceSpec(policy="synthetic", task="SyntheticTask", embodiment="synthetic") -def _normalized_light(light_intensity: torch.Tensor) -> torch.Tensor: - """Map ``light_intensity`` from ``LIGHT_RANGE`` onto roughly ``[-1, 1]`` for the logit.""" - low, high = LIGHT_RANGE +def _normalized(values: torch.Tensor, value_range: tuple[float, float]) -> torch.Tensor: + """Map ``values`` from ``value_range`` onto roughly ``[-1, 1]`` for the success logit.""" + low, high = value_range midpoint = 0.5 * (low + high) half_range = 0.5 * (high - low) - return (light_intensity - midpoint) / half_range + return (values - midpoint) / half_range + + +def _sample_uniform(value_range: tuple[float, float], num_episodes: int) -> torch.Tensor: + """Draw ``num_episodes`` values uniformly over ``value_range``.""" + low, high = value_range + return torch.rand(num_episodes) * (high - low) + low def _sample_success(success_logit: torch.Tensor) -> torch.Tensor: @@ -58,23 +73,30 @@ def _sample_success(success_logit: torch.Tensor) -> torch.Tensor: def make_continuous_dataset(seed: int, num_episodes: int = 2000) -> SensitivityDataset: - """Single continuous factor (``light_intensity``) driving a binary ``success`` outcome. + """Two continuous factors (``light_intensity``, ``grasp_offset``) driving ``success``. - Dispatches to ``KDEAnalyzer``. Success probability rises monotonically with light, so the - posterior over successful-episode light values should concentrate at the bright end. + Uses the NPE path. Both effects are planted — brighter light and a smaller grasp offset + raise success — so conditioning the posterior on success should favor high light values + and low offset values. Two factors keep theta 2-D, away from NPE's 1-D Gaussian fallback. """ torch.manual_seed(seed) - low, high = LIGHT_RANGE - light_intensity = torch.rand(num_episodes) * (high - low) + low - success = _sample_success(LIGHT_WEIGHT * _normalized_light(light_intensity)) + light_intensity = _sample_uniform(LIGHT_RANGE, num_episodes) + grasp_offset = _sample_uniform(GRASP_OFFSET_RANGE, num_episodes) + success_logit = LIGHT_WEIGHT * _normalized(light_intensity, LIGHT_RANGE) - OFFSET_WEIGHT * _normalized( + grasp_offset, GRASP_OFFSET_RANGE + ) + success = _sample_success(success_logit) schema = FactorSchema( slice=_SLICE, - factors=[FactorSpec(name="light_intensity", type="continuous", range=[list(LIGHT_RANGE)])], + factors=[ + FactorSpec(name="light_intensity", type="continuous", range=[list(LIGHT_RANGE)]), + FactorSpec(name="grasp_offset", type="continuous", range=[list(GRASP_OFFSET_RANGE)]), + ], outcomes=[OutcomeSpec(name=_OUTCOME_NAME, type="bool")], ) - theta = light_intensity.unsqueeze(1) + theta = torch.stack([light_intensity, grasp_offset], dim=1) x = success.unsqueeze(1) return SensitivityDataset(schema, theta, x) @@ -82,19 +104,18 @@ def make_continuous_dataset(seed: int, num_episodes: int = 2000) -> SensitivityD def make_mixed_dataset(seed: int, num_episodes: int = 2000) -> SensitivityDataset: """Continuous ``light_intensity`` + categorical ``table_material`` driving ``success``. - Dispatches to ``MNPEAnalyzer``. Both effects are planted: brighter light and "better" - materials (oak > walnut > bamboo) raise success, so conditioning the posterior on success - should favor high light values and oak. + Uses the MNPE path. Both effects are planted: brighter light and "better" materials + (oak > walnut > bamboo) raise success, so conditioning the posterior on success should + favor high light values and oak. """ torch.manual_seed(seed) materials = list(MATERIAL_BASE_LOGIT) material_base_logits = torch.tensor([MATERIAL_BASE_LOGIT[m] for m in materials]) - low, high = LIGHT_RANGE - light_intensity = torch.rand(num_episodes) * (high - low) + low + light_intensity = _sample_uniform(LIGHT_RANGE, num_episodes) material_code = torch.randint(0, len(materials), (num_episodes,)) - success_logit = material_base_logits[material_code] + LIGHT_WEIGHT * _normalized_light(light_intensity) + success_logit = material_base_logits[material_code] + LIGHT_WEIGHT * _normalized(light_intensity, LIGHT_RANGE) success = _sample_success(success_logit) schema = FactorSchema( @@ -106,7 +127,47 @@ def make_mixed_dataset(seed: int, num_episodes: int = 2000) -> SensitivityDatase outcomes=[OutcomeSpec(name=_OUTCOME_NAME, type="bool")], ) # Continuous column first, then the integer-coded categorical column — the layout - # SensitivityDataset.factor_columns describes and the analyzers expect. + # SensitivityDataset.factor_columns describes and the estimators expect. theta = torch.stack([light_intensity, material_code.float()], dim=1) x = success.unsqueeze(1) return SensitivityDataset(schema, theta, x) + + +def _demo(): + """Run the full pipeline on a synthetic dataset and save the marginals plot. + + The Arena counterpart of running robolab's ``posterior_inference.py`` in generated-data + mode: simulate → fit → plot, with no eval data needed. Run as:: + + python -m isaaclab_arena.tests.utils.synthetic_sensitivity --kind mixed --output /tmp/demo.png + """ + import argparse + + parser = argparse.ArgumentParser(description="Run the sensitivity pipeline on a synthetic dataset and plot it.") + parser.add_argument( + "--kind", choices=["mixed", "continuous"], default="mixed", help="'mixed' (MNPE) or 'continuous' (NPE)." + ) + parser.add_argument( + "--output", default="./sensitivity_synthetic.png", help="Output figure path; format follows the extension." + ) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--num-episodes", type=int, default=2000) + args = parser.parse_args() + + import matplotlib + + matplotlib.use("Agg") + + from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer + from isaaclab_arena.analysis.sensitivity.plotting import plot_marginals + + builder = make_mixed_dataset if args.kind == "mixed" else make_continuous_dataset + dataset = builder(seed=args.seed, num_episodes=args.num_episodes) + analyzer = SensitivityAnalyzer(dataset) + analyzer.fit() + plot_marginals(analyzer, output_path=args.output) + print(f"[INFO] Wrote synthetic sensitivity report → {args.output}") + + +if __name__ == "__main__": + _demo() From 7d02195976ec0eb41e57a16985368ddb23709c2f Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 12:00:02 +0200 Subject: [PATCH 39/74] Render continuous marginals as density curves; add rich multi-factor demo - plotting.py: continuous panels draw a filled KDE density curve (robolab's published style) instead of a histogram, and panels wrap into a grid so many factors stay readable - synthetic_sensitivity.py: add make_rich_dataset (3 continuous + 2 categorical) and a --kind rich demo mode for the one-command synthetic run Signed-off-by: Clemens Volk --- .../analysis/sensitivity/plotting.py | 36 +++++++--- .../tests/utils/synthetic_sensitivity.py | 71 ++++++++++++++++++- 2 files changed, 97 insertions(+), 10 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index 58e854833b..00a93bcea7 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -26,9 +26,9 @@ def plot_marginals( """Plot the posterior marginal of every factor in a single figure (robolab-style). Samples the joint posterior at ``observation`` (default: ``analyzer.default_observation()``), - then draws one panel per factor — a histogram for continuous factors, a probability bar - chart for categorical ones. This is the whole deliverable: one figure summarising which - factor values are consistent with the observed outcomes. + then draws one panel per factor — a density curve for continuous factors, a probability + bar chart for categorical ones, wrapped into a grid. This is the whole deliverable: one + figure summarising which factor values are consistent with the observed outcomes. Args: analyzer: A fitted :class:`SensitivityAnalyzer`. @@ -40,6 +40,7 @@ def plot_marginals( Returns: The matplotlib ``Figure``. """ + import math import matplotlib.pyplot as plt if observation is None: @@ -48,15 +49,21 @@ def plot_marginals( dataset = analyzer.dataset factors = dataset.schema.factors - figure, axes = plt.subplots(1, len(factors), figsize=(6.0 * len(factors), 4.5), squeeze=False) - for column_index, factor in enumerate(factors): - ax = axes[0][column_index] + # Wrap panels into a grid (at most 3 columns) so many factors stay readable. + num_columns = min(3, len(factors)) + num_rows = math.ceil(len(factors) / num_columns) + figure, axes = plt.subplots(num_rows, num_columns, figsize=(6.0 * num_columns, 4.5 * num_rows), squeeze=False) + flat_axes = axes.flatten() + for axis_index, factor in enumerate(factors): + ax = flat_axes[axis_index] factor_samples = samples[:, dataset.factor_columns[factor.name]].squeeze(-1) if factor.type == "continuous": _draw_continuous_marginal(ax, factor, factor_samples) else: _draw_categorical_marginal(ax, factor, factor_samples) ax.set_title(factor.name, fontsize=11) + for unused_index in range(len(factors), len(flat_axes)): + flat_axes[unused_index].axis("off") slice_info = dataset.schema.slice observation_label = ", ".join( @@ -79,11 +86,24 @@ def plot_marginals( def _draw_continuous_marginal(ax, factor: FactorSpec, factor_samples: np.ndarray) -> None: - """Histogram of a continuous factor's posterior samples, with a mean line.""" + """Smooth posterior density (filled KDE curve) of a continuous factor, with a mean line. + + A KDE line over the posterior samples — like robolab's published plots — reads the shape + of a continuous posterior better than a binned histogram. Falls back to a single line at + the mean when the samples have no spread (KDE bandwidth is then undefined). + """ + from scipy.stats import gaussian_kde + range_low, range_high = factor.range[0] - ax.hist(factor_samples, bins=50, range=(range_low, range_high), color=_CONTINUOUS_COLOR, alpha=0.7, density=True) sample_mean = float(np.mean(factor_samples)) + if float(np.std(factor_samples)) >= 1e-9: + grid = np.linspace(range_low, range_high, 200) + density = gaussian_kde(factor_samples)(grid) + ax.plot(grid, density, color=_CONTINUOUS_COLOR, linewidth=2) + ax.fill_between(grid, 0, density, color=_CONTINUOUS_COLOR, alpha=0.2) + ax.set_ylim(bottom=0) ax.axvline(sample_mean, color=_MEAN_COLOR, linestyle="--", linewidth=2, label=f"mean = {sample_mean:.3g}") + ax.set_xlim(range_low, range_high) ax.set_xlabel(factor.name) ax.set_ylabel("posterior density") ax.legend(loc="best", fontsize=9) diff --git a/isaaclab_arena/tests/utils/synthetic_sensitivity.py b/isaaclab_arena/tests/utils/synthetic_sensitivity.py index 5bd135a2d4..1ecb2bc1ec 100644 --- a/isaaclab_arena/tests/utils/synthetic_sensitivity.py +++ b/isaaclab_arena/tests/utils/synthetic_sensitivity.py @@ -49,6 +49,21 @@ MATERIAL_BASE_LOGIT: dict[str, float] = {"oak": 1.5, "walnut": 0.0, "bamboo": -1.5} """Per-material base success logit. Ordered best→worst, so oak should dominate the posterior.""" +OBJECT_MASS_RANGE: tuple[float, float] = (0.05, 2.0) +"""Range (kg) of the continuous ``object_mass`` factor.""" + +MASS_WEIGHT: float = 1.5 +"""Success-logit gain per unit of normalized mass. Subtracted ⇒ a lighter object is more successful.""" + +CAMERA_DISTANCE_RANGE: tuple[float, float] = (0.3, 1.5) +"""Range (m) of the continuous ``camera_distance`` factor.""" + +DISTANCE_WEIGHT: float = 1.5 +"""Success-logit gain per unit of normalized distance. Subtracted ⇒ a closer camera is more successful.""" + +OBJECT_TYPE_BASE_LOGIT: dict[str, float] = {"cube": 1.2, "can": 0.0, "mug": -1.2} +"""Per-object-type base success logit. Ordered best→worst, so cube should dominate the posterior.""" + _OUTCOME_NAME = "success" _SLICE = SliceSpec(policy="synthetic", task="SyntheticTask", embodiment="synthetic") @@ -133,6 +148,55 @@ def make_mixed_dataset(seed: int, num_episodes: int = 2000) -> SensitivityDatase return SensitivityDataset(schema, theta, x) +def make_rich_dataset(seed: int, num_episodes: int = 3000) -> SensitivityDataset: + """A realistic mix — three continuous + two categorical factors — driving ``success`` (MNPE). + + Mirrors the kind of data a real sweep produces: several continuous factors on different + scales (light, mass, camera distance) and several categoricals (object type, table material). + Every effect is planted (brighter / lighter / closer / cube / oak raise success), so the + posterior conditioned on success should recover all of them at once. + """ + torch.manual_seed(seed) + + object_types = list(OBJECT_TYPE_BASE_LOGIT) + materials = list(MATERIAL_BASE_LOGIT) + + light_intensity = _sample_uniform(LIGHT_RANGE, num_episodes) + object_mass = _sample_uniform(OBJECT_MASS_RANGE, num_episodes) + camera_distance = _sample_uniform(CAMERA_DISTANCE_RANGE, num_episodes) + object_type_code = torch.randint(0, len(object_types), (num_episodes,)) + material_code = torch.randint(0, len(materials), (num_episodes,)) + + object_type_base = torch.tensor([OBJECT_TYPE_BASE_LOGIT[t] for t in object_types]) + material_base = torch.tensor([MATERIAL_BASE_LOGIT[m] for m in materials]) + success_logit = ( + LIGHT_WEIGHT * _normalized(light_intensity, LIGHT_RANGE) + - MASS_WEIGHT * _normalized(object_mass, OBJECT_MASS_RANGE) + - DISTANCE_WEIGHT * _normalized(camera_distance, CAMERA_DISTANCE_RANGE) + + object_type_base[object_type_code] + + material_base[material_code] + ) + success = _sample_success(success_logit) + + schema = FactorSchema( + slice=_SLICE, + factors=[ + FactorSpec(name="light_intensity", type="continuous", range=[list(LIGHT_RANGE)]), + FactorSpec(name="object_mass", type="continuous", range=[list(OBJECT_MASS_RANGE)]), + FactorSpec(name="camera_distance", type="continuous", range=[list(CAMERA_DISTANCE_RANGE)]), + FactorSpec(name="object_type", type="categorical", choices=object_types), + FactorSpec(name="table_material", type="categorical", choices=materials), + ], + outcomes=[OutcomeSpec(name=_OUTCOME_NAME, type="bool")], + ) + # Continuous columns first (in declared order), then the integer-coded categorical columns. + theta = torch.stack( + [light_intensity, object_mass, camera_distance, object_type_code.float(), material_code.float()], dim=1 + ) + x = success.unsqueeze(1) + return SensitivityDataset(schema, theta, x) + + def _demo(): """Run the full pipeline on a synthetic dataset and save the marginals plot. @@ -145,7 +209,10 @@ def _demo(): parser = argparse.ArgumentParser(description="Run the sensitivity pipeline on a synthetic dataset and plot it.") parser.add_argument( - "--kind", choices=["mixed", "continuous"], default="mixed", help="'mixed' (MNPE) or 'continuous' (NPE)." + "--kind", + choices=["mixed", "continuous", "rich"], + default="mixed", + help="'mixed' (1 cont + 1 cat, MNPE), 'continuous' (2 cont, NPE), or 'rich' (3 cont + 2 cat, MNPE).", ) parser.add_argument( "--output", default="./sensitivity_synthetic.png", help="Output figure path; format follows the extension." @@ -161,7 +228,7 @@ def _demo(): from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer from isaaclab_arena.analysis.sensitivity.plotting import plot_marginals - builder = make_mixed_dataset if args.kind == "mixed" else make_continuous_dataset + builder = {"mixed": make_mixed_dataset, "continuous": make_continuous_dataset, "rich": make_rich_dataset}[args.kind] dataset = builder(seed=args.seed, num_episodes=args.num_episodes) analyzer = SensitivityAnalyzer(dataset) analyzer.fit() From 432a8ea5f0ff04e9313137f107d43bbfcf579c4f Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 13:20:54 +0200 Subject: [PATCH 40/74] Clean up sensitivity docstrings and hoist stdlib imports - drop RST backtick markup (double backticks, :class:/:meth: roles) from docstrings - analyzer: MNPE/NPE as two bullets, drop the NPE 1-D note - plotting: move math/pathlib imports to module top (heavy sbi/matplotlib/scipy stay lazy) Signed-off-by: Clemens Volk --- .../analysis/sensitivity/analyzer.py | 31 ++++---- .../analysis/sensitivity/dataset.py | 70 +++++++++---------- .../analysis/sensitivity/plotting.py | 15 ++-- isaaclab_arena/analysis/sensitivity/report.py | 8 +-- .../tests/utils/synthetic_sensitivity.py | 40 +++++------ 5 files changed, 81 insertions(+), 83 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/analyzer.py b/isaaclab_arena/analysis/sensitivity/analyzer.py index 1a3cad5a86..e6f1b14afa 100644 --- a/isaaclab_arena/analysis/sensitivity/analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/analyzer.py @@ -13,19 +13,18 @@ class SensitivityAnalyzer: """Fits a neural posterior over all factors, conditioned on all outcomes (robolab-style). - Picks the sbi estimator from the schema — ``MNPE`` when any factor is categorical (it - handles mixed continuous + categorical theta), ``NPE`` when every factor is continuous — - then trains on the full ``(theta, x)`` and samples the joint posterior at a chosen + Picks the sbi estimator from the schema: + + - MNPE when any factor is categorical (it handles mixed continuous + categorical theta). + - NPE when every factor is continuous. + + It then trains on the full (theta, x) and samples the joint posterior at a chosen observation. The single observation conditions on *all* outcome columns at once, so a query like "which factors produced success?" is answered for every factor jointly. - Continuous factors are normalized to ``[0, 1]`` before fitting and denormalized when + Continuous factors are normalized to [0, 1] before fitting and denormalized when sampling, so factors on very different scales (e.g. light in thousands, an offset in hundredths) train on equal footing. Categorical columns keep their integer codes. - - Note: NPE restricts itself to a Gaussian when theta is one-dimensional, so a meaningful - continuous-only analysis needs at least two continuous factors. Mixed schemas use MNPE - and are unaffected. """ def __init__(self, dataset: SensitivityDataset): @@ -39,14 +38,14 @@ def __init__(self, dataset: SensitivityDataset): self._continuous_high = torch.tensor([factor.range[0][1] for factor in continuous_factors]) def _inference_cls(self): - """Return the sbi inference class for this schema (``MNPE`` if categoricals, else ``NPE``).""" + """Return the sbi inference class for this schema (MNPE if categoricals, else NPE).""" # Import sbi lazily — it is heavy and only needed once an analysis actually fits. from sbi.inference import MNPE, NPE return MNPE if self.dataset.has_categorical_factors else NPE def _normalized_prior(self): - """Uniform prior matching the normalized theta: continuous dims ``[0, 1]``, categoricals ``[0, k-1]``.""" + """Uniform prior matching the normalized theta: continuous dims [0, 1], categoricals [0, k-1].""" from sbi.utils import BoxUniform low_bounds = [0.0] * self._num_continuous @@ -58,21 +57,21 @@ def _normalized_prior(self): return BoxUniform(low=torch.tensor(low_bounds), high=torch.tensor(high_bounds)) def _normalize(self, theta: torch.Tensor) -> torch.Tensor: - """Scale the continuous (leading) theta columns to ``[0, 1]``; leave categoricals untouched.""" + """Scale the continuous (leading) theta columns to [0, 1]; leave categoricals untouched.""" normalized = theta.clone() span = (self._continuous_high - self._continuous_low).clamp_min(1e-12) normalized[:, : self._num_continuous] = (theta[:, : self._num_continuous] - self._continuous_low) / span return normalized def _denormalize(self, theta: torch.Tensor) -> torch.Tensor: - """Inverse of :meth:`_normalize`: map the continuous columns back to their original ranges.""" + """Inverse of _normalize: map the continuous columns back to their original ranges.""" denormalized = theta.clone() span = self._continuous_high - self._continuous_low denormalized[:, : self._num_continuous] = theta[:, : self._num_continuous] * span + self._continuous_low return denormalized def fit(self, training_batch_size: int = 50) -> None: - """Train the estimator on the full ``(theta, x)`` and store the posterior on ``self``.""" + """Train the estimator on the full (theta, x) and store the posterior on self.""" print( f"[INFO] SensitivityAnalyzer: fitting {self._inference_cls().__name__} on" f" {self.dataset.num_episodes} episodes" @@ -84,7 +83,7 @@ def fit(self, training_batch_size: int = 50) -> None: self.posterior = inference.build_posterior(density_estimator) def default_observation(self) -> torch.Tensor: - """Default observation to condition on: ``1.0`` for binary outcomes, the mean otherwise. + """Default observation to condition on: 1.0 for binary outcomes, the mean otherwise. A binary outcome's interesting query is "what produced success?" (condition on 1.0); a continuous outcome has no such value, so its mean is the natural "typical case". @@ -97,9 +96,9 @@ def default_observation(self) -> torch.Tensor: return torch.tensor(outcome_values, dtype=torch.float32) def sample_posterior(self, observation: torch.Tensor | None = None, num_samples: int = 5000) -> torch.Tensor: - """Sample the joint posterior over all factors at ``observation`` (default: see above). + """Sample the joint posterior over all factors at observation (default: see above). - Returns a ``(num_samples, total_factor_dim)`` tensor laid out like ``theta`` — continuous + Returns a (num_samples, total_factor_dim) tensor laid out like theta — continuous columns first (in original, denormalized units), then integer-coded categorical columns. """ assert self.posterior is not None, "Call fit() before sampling the posterior" diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py index fc728890b7..315c54535c 100644 --- a/isaaclab_arena/analysis/sensitivity/dataset.py +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -15,10 +15,10 @@ @dataclass class FactorSpec: - """One factor's schema as declared in ``factors.yaml``. + """One factor's schema as declared in factors.yaml. - Continuous factors carry a ``range`` (one ``[low, high]`` pair per dim); categorical - factors carry ``choices`` (a list of string labels, integer-encoded by index in theta). + Continuous factors carry a range (one [low, high] pair per dim); categorical + factors carry choices (a list of string labels, integer-encoded by index in theta). """ name: str @@ -40,7 +40,7 @@ class OutcomeSpec: class SliceSpec: """Where a dataset came from: which policy ran which task on which embodiment. - Read from the ``slice:`` block of ``factors.yaml`` and shown in the report title. + Read from the slice: block of factors.yaml and shown in the report title. It just labels the data — nothing checks it against the rows. """ @@ -51,7 +51,7 @@ class SliceSpec: @dataclass class FactorSchema: - """Parsed ``factors.yaml`` — slice + factor list + outcome list.""" + """Parsed factors.yaml — slice + factor list + outcome list.""" slice: SliceSpec factors: list[FactorSpec] @@ -59,11 +59,11 @@ class FactorSchema: @classmethod def from_yaml(cls, path: str | Path) -> FactorSchema: - """Load a ``factors.yaml`` from disk into a typed ``FactorSchema``. + """Load a factors.yaml from disk into a typed FactorSchema. - The YAML must have three top-level blocks: ``slice`` (policy/task/embodiment), - ``factors`` (one entry per varied input), and ``outcomes`` (one entry per - measured output). Each factor's ``type`` must be ``continuous`` or ``categorical``. + The YAML must have three top-level blocks: slice (policy/task/embodiment), + factors (one entry per varied input), and outcomes (one entry per + measured output). Each factor's type must be continuous or categorical. """ with open(path, encoding="utf-8") as yaml_file: yaml_data = yaml.safe_load(yaml_file) @@ -112,14 +112,14 @@ def from_yaml(cls, path: str | Path) -> FactorSchema: @property def total_factor_dim(self) -> int: - """Total width of theta — sum of ``dim`` over continuous factors plus 1 per categorical.""" + """Total width of theta — sum of dim over continuous factors plus 1 per categorical.""" return sum(factor.dim if factor.type == "continuous" else 1 for factor in self.factors) @property def factor_columns(self) -> dict[str, slice]: """Map factor name → its column slice in theta. - Continuous factors occupy the leading columns (``dim`` each), then each categorical + Continuous factors occupy the leading columns (dim each), then each categorical factor occupies one trailing column. This continuous-first layout is what sbi's mixed density estimator expects. """ @@ -135,26 +135,26 @@ def factor_columns(self) -> dict[str, slice]: class SensitivityDataset: - """A ``FactorSchema`` paired with its per-episode ``theta`` (factors) and ``x`` (outcomes). + """A FactorSchema paired with its per-episode theta (factors) and x (outcomes). The object is a pure container: it holds the schema and the two tensors, and exposes - the ``prior`` and column layouts an analyzer consumes. It can be built two ways: + the prior and column layouts an analyzer consumes. It can be built two ways: - - :meth:`from_files` — parse a ``factors.yaml`` / ``episode_summary.jsonl`` pair + - from_files — parse a factors.yaml / episode_summary.jsonl pair (the path eval runs take). - the constructor — wrap in-memory tensors directly (what a synthetic simulator or - a unit test takes). The tensors must already be in the layout :meth:`factor_columns` + a unit test takes). The tensors must already be in the layout factor_columns describes: continuous columns first, then one integer-coded column per categorical. """ def __init__(self, schema: FactorSchema, theta: torch.Tensor, x: torch.Tensor): - """Wrap an in-memory ``schema`` plus its ``theta`` / ``x`` tensors, validating shapes. + """Wrap an in-memory schema plus its theta / x tensors, validating shapes. Args: schema: The parsed factor/outcome schema. Continuous factors must carry a - ``range``; categorical factors must carry ``choices``. - theta: ``(num_episodes, total_factor_dim)`` factor matrix, continuous-first. - x: ``(num_episodes, num_outcomes)`` outcome matrix. + range; categorical factors must carry choices. + theta: (num_episodes, total_factor_dim) factor matrix, continuous-first. + x: (num_episodes, num_outcomes) outcome matrix. """ assert theta.ndim == 2 and x.ndim == 2, f"theta and x must be 2D; got {theta.shape} and {x.shape}" assert theta.shape[0] == x.shape[0], f"theta/x row counts disagree: {theta.shape[0]} vs {x.shape[0]}" @@ -171,10 +171,10 @@ def __init__(self, schema: FactorSchema, theta: torch.Tensor, x: torch.Tensor): @classmethod def from_files(cls, factors_yaml: str | Path, jsonl_path: str | Path) -> SensitivityDataset: - """Build a dataset from a ``factors.yaml`` schema and an ``episode_summary.jsonl``. + """Build a dataset from a factors.yaml schema and an episode_summary.jsonl. Parses and validates both, infers any missing continuous range from the data, and - assembles the ``theta`` / ``x`` tensors in the layout the analyzers expect. + assembles the theta / x tensors in the layout the analyzers expect. """ schema = FactorSchema.from_yaml(factors_yaml) @@ -191,19 +191,19 @@ def from_files(cls, factors_yaml: str | Path, jsonl_path: str | Path) -> Sensiti @property def theta(self) -> torch.Tensor: - """``(num_episodes, total_factor_dim)`` matrix of factor values, one row per episode. + """(num_episodes, total_factor_dim) matrix of factor values, one row per episode. This is the "input" sbi infers a posterior over. Column layout is given by - ``factor_columns`` — continuous factors first, then categoricals (integer-coded). + factor_columns — continuous factors first, then categoricals (integer-coded). """ return self._theta @property def x(self) -> torch.Tensor: - """``(num_episodes, num_outcomes)`` matrix of outcome values, one row per episode. + """(num_episodes, num_outcomes) matrix of outcome values, one row per episode. This is what the analyzer conditions queries on. The analyzer typically selects a - single outcome column at fit time (e.g. ``success_rate``) and asks + single outcome column at fit time (e.g. success_rate) and asks "what theta values were consistent with observing this outcome?" """ return self._x @@ -215,7 +215,7 @@ def num_episodes(self) -> int: @property def factor_columns(self) -> dict[str, slice]: - """Map factor name → its column slice in theta. Same as ``schema.factor_columns``.""" + """Map factor name → its column slice in theta. Same as schema.factor_columns.""" return self.schema.factor_columns @property @@ -230,10 +230,10 @@ def has_categorical_factors(self) -> bool: @property def prior(self): - """Uniform prior (``sbi.utils.BoxUniform``) over all factor dims. + """Uniform prior (sbi.utils.BoxUniform) over all factor dims. - Continuous factors use their ``[low, high]`` range; categorical factors use - ``[0, num_choices - 1]`` over their integer codes, in continuous-first order. + Continuous factors use their [low, high] range; categorical factors use + [0, num_choices - 1] over their integer codes, in continuous-first order. """ # Import sbi lazily so loading the dataset does not pay the sbi import cost # unless an analyzer actually runs. @@ -270,7 +270,7 @@ def prior(self): def _validate_rows(schema: FactorSchema, rows: list[dict], jsonl_path: str | Path) -> None: """Assert every JSONL row carries the schema's declared factor and outcome keys. - The declared names need only be a subset of each row's ``arena_env_args`` / ``outcomes``; + The declared names need only be a subset of each row's arena_env_args / outcomes; extra keys are ignored. Raises pointing at the first offending row. """ expected_factor_names = {factor.name for factor in schema.factors} @@ -292,9 +292,9 @@ def _validate_rows(schema: FactorSchema, rows: list[dict], jsonl_path: str | Pat def _infer_missing_factor_ranges(schema: FactorSchema, rows: list[dict]) -> None: - """Fill any continuous factor's missing ``range`` from the observed min/max. + """Fill any continuous factor's missing range from the observed min/max. - A ``range`` declared in factors.yaml takes precedence and is left untouched. + A range declared in factors.yaml takes precedence and is left untouched. """ for factor in schema.factors: if factor.type != "continuous" or factor.range is not None: @@ -309,10 +309,10 @@ def _infer_missing_factor_ranges(schema: FactorSchema, rows: list[dict]) -> None def _build_factor_tensor(schema: FactorSchema, rows: list[dict]) -> torch.Tensor: - """Assemble the per-episode factor matrix ``theta``. + """Assemble the per-episode factor matrix theta. Continuous columns first (one per dim), then one column per categorical factor with its - value integer-coded as a ``float32`` index into ``FactorSpec.choices``. + value integer-coded as a float32 index into FactorSpec.choices. """ continuous_factors = [factor for factor in schema.factors if factor.type == "continuous"] categorical_factors = [factor for factor in schema.factors if factor.type == "categorical"] @@ -352,7 +352,7 @@ def _build_factor_tensor(schema: FactorSchema, rows: list[dict]) -> torch.Tensor def _build_outcome_tensor(schema: FactorSchema, rows: list[dict]) -> torch.Tensor: - """Assemble the per-episode outcome matrix ``x`` (one column per declared outcome). + """Assemble the per-episode outcome matrix x (one column per declared outcome). Each outcome value is cast to float; bool outcomes become 0.0/1.0. The analyzer usually selects a single outcome column at fit time and conditions queries on it. diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index 00a93bcea7..21e27fb956 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -5,7 +5,9 @@ from __future__ import annotations +import math import numpy as np +from pathlib import Path from typing import TYPE_CHECKING if TYPE_CHECKING: @@ -25,22 +27,21 @@ def plot_marginals( ): """Plot the posterior marginal of every factor in a single figure (robolab-style). - Samples the joint posterior at ``observation`` (default: ``analyzer.default_observation()``), + Samples the joint posterior at observation (default: analyzer.default_observation()), then draws one panel per factor — a density curve for continuous factors, a probability bar chart for categorical ones, wrapped into a grid. This is the whole deliverable: one figure summarising which factor values are consistent with the observed outcomes. Args: - analyzer: A fitted :class:`SensitivityAnalyzer`. + analyzer: A fitted SensitivityAnalyzer. observation: Outcome vector to condition on. Defaults to the analyzer's default. num_samples: Number of posterior samples to draw. output_path: If given, save the figure here. The format follows the path's - extension (``.png``, ``.pdf``, …); parent directories are created. + extension (.png, .pdf, …); parent directories are created. Returns: - The matplotlib ``Figure``. + The matplotlib Figure. """ - import math import matplotlib.pyplot as plt if observation is None: @@ -78,8 +79,6 @@ def plot_marginals( figure.tight_layout(rect=[0, 0, 1, 0.92]) if output_path is not None: - from pathlib import Path - Path(output_path).parent.mkdir(parents=True, exist_ok=True) figure.savefig(output_path, dpi=150, bbox_inches="tight") return figure @@ -114,7 +113,7 @@ def _draw_categorical_marginal(ax, factor: FactorSpec, factor_samples: np.ndarra """Bar chart of a categorical factor's posterior probability per choice. sbi returns categorical columns as floats over the integer-code support, so samples are - rounded to the nearest code in ``[0, num_choices - 1]`` and tallied into frequencies. + rounded to the nearest code in [0, num_choices - 1] and tallied into frequencies. """ assert factor.choices is not None num_choices = len(factor.choices) diff --git a/isaaclab_arena/analysis/sensitivity/report.py b/isaaclab_arena/analysis/sensitivity/report.py index 5d53ead24c..4bf485800f 100644 --- a/isaaclab_arena/analysis/sensitivity/report.py +++ b/isaaclab_arena/analysis/sensitivity/report.py @@ -19,14 +19,14 @@ def generate_report( output_path: str | Path, observation: list[float] | None = None, ) -> Path: - """Build a sensitivity report from a ``factors.yaml`` / ``episode_summary.jsonl`` pair. + """Build a sensitivity report from a factors.yaml / episode_summary.jsonl pair. - Loads the data, fits a :class:`SensitivityAnalyzer`, and saves a single posterior-marginals - figure. The output format follows the ``output_path`` extension (``.png``, ``.pdf``, …). + Loads the data, fits a SensitivityAnalyzer, and saves a single posterior-marginals + figure. The output format follows the output_path extension (.png, .pdf, …). Args: factors_yaml_path: Schema file declaring factors and outcomes. - jsonl_path: ``episode_summary.jsonl`` produced by eval_runner. + jsonl_path: episode_summary.jsonl produced by eval_runner. output_path: Destination figure file (parent dirs created if absent). observation: Outcome values to condition on, one per declared outcome. Defaults to the analyzer's default (1.0 for binary outcomes, the mean otherwise). diff --git a/isaaclab_arena/tests/utils/synthetic_sensitivity.py b/isaaclab_arena/tests/utils/synthetic_sensitivity.py index 1ecb2bc1ec..867d82a766 100644 --- a/isaaclab_arena/tests/utils/synthetic_sensitivity.py +++ b/isaaclab_arena/tests/utils/synthetic_sensitivity.py @@ -5,20 +5,20 @@ """Synthetic sensitivity datasets with a *known* ground-truth relationship. -This is the Arena analogue of the ``simple_simulator`` in robolab's MNPE demo: it samples +This is the Arena analogue of the simple_simulator in robolab's MNPE demo: it samples factors from a uniform prior, runs them through a fixed generative model, and returns a -:class:`SensitivityDataset` of in-memory ``theta`` / ``x`` tensors — no ``factors.yaml`` or -``episode_summary.jsonl`` round-trip. Because the planted relationship is known, a test can -fit a :class:`SensitivityAnalyzer` on the data and assert the recovered posterior reflects it. +SensitivityDataset of in-memory theta / x tensors — no factors.yaml or +episode_summary.jsonl round-trip. Because the planted relationship is known, a test can +fit a SensitivityAnalyzer on the data and assert the recovered posterior reflects it. Ground truth (single-sourced in the constants below): - - ``light_intensity`` is continuous; higher light raises success (``LIGHT_WEIGHT > 0``). - - ``grasp_offset`` is continuous; a *smaller* offset raises success (``OFFSET_WEIGHT > 0``). - - ``table_material`` is categorical; ``MATERIAL_BASE_LOGIT`` makes oak the most successful + - light_intensity is continuous; higher light raises success (LIGHT_WEIGHT > 0). + - grasp_offset is continuous; a *smaller* offset raises success (OFFSET_WEIGHT > 0). + - table_material is categorical; MATERIAL_BASE_LOGIT makes oak the most successful material and bamboo the least. - - ``success`` is a binary outcome drawn from ``Bernoulli(sigmoid(logit))``. + - success is a binary outcome drawn from Bernoulli(sigmoid(logit)). -``make_mixed_dataset`` exercises the MNPE path (continuous + categorical); ``make_continuous_dataset`` +make_mixed_dataset exercises the MNPE path (continuous + categorical); make_continuous_dataset exercises the NPE path with two continuous factors (NPE restricts to a Gaussian on 1-D theta). """ @@ -35,13 +35,13 @@ ) LIGHT_RANGE: tuple[float, float] = (0.0, 5000.0) -"""Range of the continuous ``light_intensity`` factor.""" +"""Range of the continuous light_intensity factor.""" LIGHT_WEIGHT: float = 2.5 """Success-logit gain per unit of normalized light. Positive ⇒ brighter is more successful.""" GRASP_OFFSET_RANGE: tuple[float, float] = (0.0, 0.2) -"""Range (metres) of the continuous ``grasp_offset`` factor.""" +"""Range (metres) of the continuous grasp_offset factor.""" OFFSET_WEIGHT: float = 2.5 """Success-logit gain per unit of normalized offset. Subtracted ⇒ a smaller offset is more successful.""" @@ -50,13 +50,13 @@ """Per-material base success logit. Ordered best→worst, so oak should dominate the posterior.""" OBJECT_MASS_RANGE: tuple[float, float] = (0.05, 2.0) -"""Range (kg) of the continuous ``object_mass`` factor.""" +"""Range (kg) of the continuous object_mass factor.""" MASS_WEIGHT: float = 1.5 """Success-logit gain per unit of normalized mass. Subtracted ⇒ a lighter object is more successful.""" CAMERA_DISTANCE_RANGE: tuple[float, float] = (0.3, 1.5) -"""Range (m) of the continuous ``camera_distance`` factor.""" +"""Range (m) of the continuous camera_distance factor.""" DISTANCE_WEIGHT: float = 1.5 """Success-logit gain per unit of normalized distance. Subtracted ⇒ a closer camera is more successful.""" @@ -69,7 +69,7 @@ def _normalized(values: torch.Tensor, value_range: tuple[float, float]) -> torch.Tensor: - """Map ``values`` from ``value_range`` onto roughly ``[-1, 1]`` for the success logit.""" + """Map values from value_range onto roughly [-1, 1] for the success logit.""" low, high = value_range midpoint = 0.5 * (low + high) half_range = 0.5 * (high - low) @@ -77,18 +77,18 @@ def _normalized(values: torch.Tensor, value_range: tuple[float, float]) -> torch def _sample_uniform(value_range: tuple[float, float], num_episodes: int) -> torch.Tensor: - """Draw ``num_episodes`` values uniformly over ``value_range``.""" + """Draw num_episodes values uniformly over value_range.""" low, high = value_range return torch.rand(num_episodes) * (high - low) + low def _sample_success(success_logit: torch.Tensor) -> torch.Tensor: - """Draw a binary success outcome per episode from ``Bernoulli(sigmoid(logit))``.""" + """Draw a binary success outcome per episode from Bernoulli(sigmoid(logit)).""" return torch.bernoulli(torch.sigmoid(success_logit)) def make_continuous_dataset(seed: int, num_episodes: int = 2000) -> SensitivityDataset: - """Two continuous factors (``light_intensity``, ``grasp_offset``) driving ``success``. + """Two continuous factors (light_intensity, grasp_offset) driving success. Uses the NPE path. Both effects are planted — brighter light and a smaller grasp offset raise success — so conditioning the posterior on success should favor high light values @@ -117,7 +117,7 @@ def make_continuous_dataset(seed: int, num_episodes: int = 2000) -> SensitivityD def make_mixed_dataset(seed: int, num_episodes: int = 2000) -> SensitivityDataset: - """Continuous ``light_intensity`` + categorical ``table_material`` driving ``success``. + """Continuous light_intensity + categorical table_material driving success. Uses the MNPE path. Both effects are planted: brighter light and "better" materials (oak > walnut > bamboo) raise success, so conditioning the posterior on success should @@ -149,7 +149,7 @@ def make_mixed_dataset(seed: int, num_episodes: int = 2000) -> SensitivityDatase def make_rich_dataset(seed: int, num_episodes: int = 3000) -> SensitivityDataset: - """A realistic mix — three continuous + two categorical factors — driving ``success`` (MNPE). + """A realistic mix — three continuous + two categorical factors — driving success (MNPE). Mirrors the kind of data a real sweep produces: several continuous factors on different scales (light, mass, camera distance) and several categoricals (object type, table material). @@ -200,7 +200,7 @@ def make_rich_dataset(seed: int, num_episodes: int = 3000) -> SensitivityDataset def _demo(): """Run the full pipeline on a synthetic dataset and save the marginals plot. - The Arena counterpart of running robolab's ``posterior_inference.py`` in generated-data + The Arena counterpart of running robolab's posterior_inference.py in generated-data mode: simulate → fit → plot, with no eval data needed. Run as:: python -m isaaclab_arena.tests.utils.synthetic_sensitivity --kind mixed --output /tmp/demo.png From 5aeba86f29f1bb8f21fa82219f05c15a75242590 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 13:28:34 +0200 Subject: [PATCH 41/74] Address PR review comments MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - copyright headers state only 2026 (analysis/__init__, synthetic + test files) - analyzer: drop the column-bounds comment in __init__ - analyzer: rename _inference_cls → _select_inference_class with a clearer docstring - analyzer: fit() returns the fitted posterior instead of None Signed-off-by: Clemens Volk --- isaaclab_arena/analysis/__init__.py | 2 +- .../analysis/sensitivity/analyzer.py | 19 +++++++++++-------- .../tests/test_sensitivity_analysis.py | 6 +++--- .../tests/utils/synthetic_sensitivity.py | 2 +- sensitivity_mixed.png | 3 +++ sensitivity_rich.png | 3 +++ sensitivity_synthetic.png | 3 +++ 7 files changed, 25 insertions(+), 13 deletions(-) create mode 100644 sensitivity_mixed.png create mode 100644 sensitivity_rich.png create mode 100644 sensitivity_synthetic.png diff --git a/isaaclab_arena/analysis/__init__.py b/isaaclab_arena/analysis/__init__.py index fee3a6a9f6..16ea4c2183 100644 --- a/isaaclab_arena/analysis/__init__.py +++ b/isaaclab_arena/analysis/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: Apache-2.0 diff --git a/isaaclab_arena/analysis/sensitivity/analyzer.py b/isaaclab_arena/analysis/sensitivity/analyzer.py index e6f1b14afa..a634756aca 100644 --- a/isaaclab_arena/analysis/sensitivity/analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/analyzer.py @@ -30,15 +30,17 @@ class SensitivityAnalyzer: def __init__(self, dataset: SensitivityDataset): self.dataset = dataset self.posterior = None - # Continuous factors occupy the leading theta columns (one each); cache their - # [low, high] bounds for the normalize/denormalize round-trip around sbi. continuous_factors = [factor for factor in dataset.schema.factors if factor.type == "continuous"] self._num_continuous = len(continuous_factors) self._continuous_low = torch.tensor([factor.range[0][0] for factor in continuous_factors]) self._continuous_high = torch.tensor([factor.range[0][1] for factor in continuous_factors]) - def _inference_cls(self): - """Return the sbi inference class for this schema (MNPE if categoricals, else NPE).""" + def _select_inference_class(self): + """Choose the sbi inference class for this schema. + + Returns MNPE when any factor is categorical (its mixed density estimator handles + continuous + categorical theta together), and NPE when every factor is continuous. + """ # Import sbi lazily — it is heavy and only needed once an analysis actually fits. from sbi.inference import MNPE, NPE @@ -70,17 +72,18 @@ def _denormalize(self, theta: torch.Tensor) -> torch.Tensor: denormalized[:, : self._num_continuous] = theta[:, : self._num_continuous] * span + self._continuous_low return denormalized - def fit(self, training_batch_size: int = 50) -> None: - """Train the estimator on the full (theta, x) and store the posterior on self.""" + def fit(self, training_batch_size: int = 50): + """Train the estimator on the full (theta, x); store and return the fitted posterior.""" print( - f"[INFO] SensitivityAnalyzer: fitting {self._inference_cls().__name__} on" + f"[INFO] SensitivityAnalyzer: fitting {self._select_inference_class().__name__} on" f" {self.dataset.num_episodes} episodes" f" (theta dim={self.dataset.theta.shape[1]}, x dim={self.dataset.x.shape[1]})." ) - inference = self._inference_cls()(prior=self._normalized_prior()) + inference = self._select_inference_class()(prior=self._normalized_prior()) inference.append_simulations(self._normalize(self.dataset.theta), self.dataset.x) density_estimator = inference.train(training_batch_size=training_batch_size) self.posterior = inference.build_posterior(density_estimator) + return self.posterior def default_observation(self) -> torch.Tensor: """Default observation to condition on: 1.0 for binary outcomes, the mean otherwise. diff --git a/isaaclab_arena/tests/test_sensitivity_analysis.py b/isaaclab_arena/tests/test_sensitivity_analysis.py index cda7e876e2..98af425a7d 100644 --- a/isaaclab_arena/tests/test_sensitivity_analysis.py +++ b/isaaclab_arena/tests/test_sensitivity_analysis.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: Apache-2.0 @@ -41,7 +41,7 @@ def test_mnpe_recovers_light_and_material_effects(): """Mixed continuous + categorical (MNPE): recover the light trend and the material ranking.""" dataset = make_mixed_dataset(seed=0) analyzer = SensitivityAnalyzer(dataset) - assert analyzer._inference_cls().__name__ == "MNPE", "mixed schema should select MNPE" + assert analyzer._select_inference_class().__name__ == "MNPE", "mixed schema should select MNPE" torch.manual_seed(0) analyzer.fit() @@ -62,7 +62,7 @@ def test_npe_recovers_two_continuous_effects(): """Two continuous factors (NPE): recover that bright light and a small grasp offset drive success.""" dataset = make_continuous_dataset(seed=0) analyzer = SensitivityAnalyzer(dataset) - assert analyzer._inference_cls().__name__.startswith("NPE"), "continuous-only schema should select NPE" + assert analyzer._select_inference_class().__name__.startswith("NPE"), "continuous-only schema should select NPE" torch.manual_seed(0) analyzer.fit() diff --git a/isaaclab_arena/tests/utils/synthetic_sensitivity.py b/isaaclab_arena/tests/utils/synthetic_sensitivity.py index 867d82a766..fe2c3f4083 100644 --- a/isaaclab_arena/tests/utils/synthetic_sensitivity.py +++ b/isaaclab_arena/tests/utils/synthetic_sensitivity.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025-2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). +# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: Apache-2.0 diff --git a/sensitivity_mixed.png b/sensitivity_mixed.png new file mode 100644 index 0000000000..0c74df597a --- /dev/null +++ b/sensitivity_mixed.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:52c02b579b63f11ca5fa1bcd009840eef8022faec7e0a87575cb63b1ad6d2a8d +size 96934 diff --git a/sensitivity_rich.png b/sensitivity_rich.png new file mode 100644 index 0000000000..483b8cf96b --- /dev/null +++ b/sensitivity_rich.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:314e08938b0043b939df50c94a45ceac71c8f182ee4de120fa13da9194719fb7 +size 189814 diff --git a/sensitivity_synthetic.png b/sensitivity_synthetic.png new file mode 100644 index 0000000000..c511034b18 --- /dev/null +++ b/sensitivity_synthetic.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ea649f10a53d11a4069154824190bbabc57e1697e1755c5fe87dba6393620c6 +size 77273 From ea2355fa02747bf313ba0154bc3cc6c5edae174a Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 13:44:16 +0200 Subject: [PATCH 42/74] Sensitivity cleanup: top-level imports, merge report CLI, eval/ outputs - move all in-function imports (sbi, matplotlib, scipy) to module top; these analysis modules already import torch at top and are only loaded when running an analysis, so lazy imports added no value - merge report.py into generate_report.py and delete report.py - write generated reports to eval/ (gitignored) instead of the repo root; stop tracking the sensitivity_*.png demo artifacts committed by mistake - drop 'robolab' mentions and remaining backticks from sensitivity docstrings - plot_marginals docstring: drop the 'whole deliverable' sentence Signed-off-by: Clemens Volk --- .gitignore | 2 + .../analysis/sensitivity/analyzer.py | 10 ++-- .../analysis/sensitivity/dataset.py | 6 +-- .../analysis/sensitivity/generate_report.py | 44 ++++++++++++++-- .../analysis/sensitivity/plotting.py | 17 +++---- isaaclab_arena/analysis/sensitivity/report.py | 51 ------------------- .../tests/test_sensitivity_analysis.py | 8 +-- .../tests/utils/synthetic_sensitivity.py | 30 +++++------ sensitivity_mixed.png | 3 -- sensitivity_rich.png | 3 -- sensitivity_synthetic.png | 3 -- 11 files changed, 73 insertions(+), 104 deletions(-) delete mode 100644 isaaclab_arena/analysis/sensitivity/report.py delete mode 100644 sensitivity_mixed.png delete mode 100644 sensitivity_rich.png delete mode 100644 sensitivity_synthetic.png diff --git a/.gitignore b/.gitignore index b3a54b0034..7dba264430 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,7 @@ **/build +# Eval outputs (generated sensitivity reports, etc.) +/eval/ # Prerequisites *.d diff --git a/isaaclab_arena/analysis/sensitivity/analyzer.py b/isaaclab_arena/analysis/sensitivity/analyzer.py index a634756aca..2e87a64812 100644 --- a/isaaclab_arena/analysis/sensitivity/analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/analyzer.py @@ -7,11 +7,14 @@ import torch +from sbi.inference import MNPE, NPE +from sbi.utils import BoxUniform + from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset class SensitivityAnalyzer: - """Fits a neural posterior over all factors, conditioned on all outcomes (robolab-style). + """Fits a neural posterior over all factors, conditioned on all outcomes. Picks the sbi estimator from the schema: @@ -41,15 +44,10 @@ def _select_inference_class(self): Returns MNPE when any factor is categorical (its mixed density estimator handles continuous + categorical theta together), and NPE when every factor is continuous. """ - # Import sbi lazily — it is heavy and only needed once an analysis actually fits. - from sbi.inference import MNPE, NPE - return MNPE if self.dataset.has_categorical_factors else NPE def _normalized_prior(self): """Uniform prior matching the normalized theta: continuous dims [0, 1], categoricals [0, k-1].""" - from sbi.utils import BoxUniform - low_bounds = [0.0] * self._num_continuous high_bounds = [1.0] * self._num_continuous for factor in self.dataset.schema.factors: diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py index 315c54535c..224297b1b9 100644 --- a/isaaclab_arena/analysis/sensitivity/dataset.py +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -12,6 +12,8 @@ from pathlib import Path from typing import Literal +from sbi.utils import BoxUniform + @dataclass class FactorSpec: @@ -235,10 +237,6 @@ def prior(self): Continuous factors use their [low, high] range; categorical factors use [0, num_choices - 1] over their integer codes, in continuous-first order. """ - # Import sbi lazily so loading the dataset does not pay the sbi import cost - # unless an analyzer actually runs. - from sbi.utils import BoxUniform - low_bounds: list[float] = [] high_bounds: list[float] = [] diff --git a/isaaclab_arena/analysis/sensitivity/generate_report.py b/isaaclab_arena/analysis/sensitivity/generate_report.py index 5254b00e90..af7ad67a8b 100644 --- a/isaaclab_arena/analysis/sensitivity/generate_report.py +++ b/isaaclab_arena/analysis/sensitivity/generate_report.py @@ -6,8 +6,46 @@ from __future__ import annotations import argparse +import matplotlib.pyplot as plt +import torch +from pathlib import Path -from isaaclab_arena.analysis.sensitivity.report import generate_report +from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer +from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset +from isaaclab_arena.analysis.sensitivity.plotting import plot_marginals + + +def generate_report( + factors_yaml_path: str | Path, + jsonl_path: str | Path, + output_path: str | Path, + observation: list[float] | None = None, +) -> Path: + """Build a sensitivity report from a factors.yaml / episode_summary.jsonl pair. + + Loads the data, fits a SensitivityAnalyzer, and saves a single posterior-marginals + figure. The output format follows the output_path extension (.png, .pdf, …). + + Args: + factors_yaml_path: Schema file declaring factors and outcomes. + jsonl_path: episode_summary.jsonl produced by eval_runner. + output_path: Destination figure file (parent dirs created if absent). + observation: Outcome values to condition on, one per declared outcome. Defaults to + the analyzer's default (1.0 for binary outcomes, the mean otherwise). + + Returns: + The resolved output path. + """ + dataset = SensitivityDataset.from_files(Path(factors_yaml_path), Path(jsonl_path)) + analyzer = SensitivityAnalyzer(dataset) + analyzer.fit() + + observation_tensor = None if observation is None else torch.tensor(observation, dtype=torch.float32) + output_path = Path(output_path) + plot_marginals(analyzer, observation=observation_tensor, output_path=str(output_path)) + plt.close("all") + print(f"[INFO] Wrote report → {output_path}") + return output_path def main(): @@ -24,8 +62,8 @@ def main(): parser.add_argument( "--output", type=str, - default="./sensitivity_report.png", - help="Output figure file; format follows the extension (.png, .pdf, …). Default: ./sensitivity_report.png.", + default="eval/sensitivity_report.png", + help="Output figure file; format follows the extension (.png, .pdf, …). Default: eval/sensitivity_report.png.", ) parser.add_argument( "--observation", diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index 21e27fb956..d31e7af792 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -6,8 +6,10 @@ from __future__ import annotations import math +import matplotlib.pyplot as plt import numpy as np from pathlib import Path +from scipy.stats import gaussian_kde from typing import TYPE_CHECKING if TYPE_CHECKING: @@ -25,12 +27,11 @@ def plot_marginals( num_samples: int = 5000, output_path: str | None = None, ): - """Plot the posterior marginal of every factor in a single figure (robolab-style). + """Plot the posterior marginal of every factor in a single figure. Samples the joint posterior at observation (default: analyzer.default_observation()), then draws one panel per factor — a density curve for continuous factors, a probability - bar chart for categorical ones, wrapped into a grid. This is the whole deliverable: one - figure summarising which factor values are consistent with the observed outcomes. + bar chart for categorical ones, wrapped into a grid. Args: analyzer: A fitted SensitivityAnalyzer. @@ -42,8 +43,6 @@ def plot_marginals( Returns: The matplotlib Figure. """ - import matplotlib.pyplot as plt - if observation is None: observation = analyzer.default_observation() samples = analyzer.sample_posterior(observation, num_samples).cpu().numpy() @@ -87,12 +86,10 @@ def plot_marginals( def _draw_continuous_marginal(ax, factor: FactorSpec, factor_samples: np.ndarray) -> None: """Smooth posterior density (filled KDE curve) of a continuous factor, with a mean line. - A KDE line over the posterior samples — like robolab's published plots — reads the shape - of a continuous posterior better than a binned histogram. Falls back to a single line at - the mean when the samples have no spread (KDE bandwidth is then undefined). + A KDE line over the posterior samples reads the shape of a continuous posterior better + than a binned histogram. Falls back to a single line at the mean when the samples have + no spread (KDE bandwidth is then undefined). """ - from scipy.stats import gaussian_kde - range_low, range_high = factor.range[0] sample_mean = float(np.mean(factor_samples)) if float(np.std(factor_samples)) >= 1e-9: diff --git a/isaaclab_arena/analysis/sensitivity/report.py b/isaaclab_arena/analysis/sensitivity/report.py deleted file mode 100644 index 4bf485800f..0000000000 --- a/isaaclab_arena/analysis/sensitivity/report.py +++ /dev/null @@ -1,51 +0,0 @@ -# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -from __future__ import annotations - -import torch -from pathlib import Path - -from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer -from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset -from isaaclab_arena.analysis.sensitivity.plotting import plot_marginals - - -def generate_report( - factors_yaml_path: str | Path, - jsonl_path: str | Path, - output_path: str | Path, - observation: list[float] | None = None, -) -> Path: - """Build a sensitivity report from a factors.yaml / episode_summary.jsonl pair. - - Loads the data, fits a SensitivityAnalyzer, and saves a single posterior-marginals - figure. The output format follows the output_path extension (.png, .pdf, …). - - Args: - factors_yaml_path: Schema file declaring factors and outcomes. - jsonl_path: episode_summary.jsonl produced by eval_runner. - output_path: Destination figure file (parent dirs created if absent). - observation: Outcome values to condition on, one per declared outcome. Defaults to - the analyzer's default (1.0 for binary outcomes, the mean otherwise). - - Returns: - The resolved output path. - """ - import matplotlib - - matplotlib.use("Agg") - import matplotlib.pyplot as plt - - dataset = SensitivityDataset.from_files(Path(factors_yaml_path), Path(jsonl_path)) - analyzer = SensitivityAnalyzer(dataset) - analyzer.fit() - - observation_tensor = None if observation is None else torch.tensor(observation, dtype=torch.float32) - output_path = Path(output_path) - plot_marginals(analyzer, observation=observation_tensor, output_path=str(output_path)) - plt.close("all") - print(f"[INFO] Wrote report → {output_path}") - return output_path diff --git a/isaaclab_arena/tests/test_sensitivity_analysis.py b/isaaclab_arena/tests/test_sensitivity_analysis.py index 98af425a7d..10174b0381 100644 --- a/isaaclab_arena/tests/test_sensitivity_analysis.py +++ b/isaaclab_arena/tests/test_sensitivity_analysis.py @@ -5,11 +5,11 @@ """End-to-end sensitivity-analysis tests on synthetic data with a known ground truth. -Each test fits a ``SensitivityAnalyzer`` on a dataset whose factor→outcome relationship is -planted by ``synthetic_sensitivity`` (brighter light, smaller grasp offset, and oak raise +Each test fits a SensitivityAnalyzer on a dataset whose factor→outcome relationship is +planted by synthetic_sensitivity (brighter light, smaller grasp offset, and oak raise success), then asserts the posterior conditioned on success recovers that relationship. The -data is built in memory, so these run on CPU without Isaac Sim. They mirror robolab's -generated-data demo: MNPE for mixed schemas, NPE for continuous-only (with 2-D theta). +data is built in memory, so these run on CPU without Isaac Sim. They cover both estimator +paths: MNPE for mixed schemas, NPE for continuous-only (with 2-D theta). """ from __future__ import annotations diff --git a/isaaclab_arena/tests/utils/synthetic_sensitivity.py b/isaaclab_arena/tests/utils/synthetic_sensitivity.py index fe2c3f4083..e6d56c0e97 100644 --- a/isaaclab_arena/tests/utils/synthetic_sensitivity.py +++ b/isaaclab_arena/tests/utils/synthetic_sensitivity.py @@ -5,11 +5,11 @@ """Synthetic sensitivity datasets with a *known* ground-truth relationship. -This is the Arena analogue of the simple_simulator in robolab's MNPE demo: it samples -factors from a uniform prior, runs them through a fixed generative model, and returns a -SensitivityDataset of in-memory theta / x tensors — no factors.yaml or -episode_summary.jsonl round-trip. Because the planted relationship is known, a test can -fit a SensitivityAnalyzer on the data and assert the recovered posterior reflects it. +A simple forward simulator: it samples factors from a uniform prior, runs them through a +fixed generative model, and returns a SensitivityDataset of in-memory theta / x tensors — +no factors.yaml or episode_summary.jsonl round-trip. Because the planted relationship is +known, a test can fit a SensitivityAnalyzer on the data and assert the recovered posterior +reflects it. Ground truth (single-sourced in the constants below): - light_intensity is continuous; higher light raises success (LIGHT_WEIGHT > 0). @@ -24,8 +24,10 @@ from __future__ import annotations +import argparse import torch +from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer from isaaclab_arena.analysis.sensitivity.dataset import ( FactorSchema, FactorSpec, @@ -33,6 +35,7 @@ SensitivityDataset, SliceSpec, ) +from isaaclab_arena.analysis.sensitivity.plotting import plot_marginals LIGHT_RANGE: tuple[float, float] = (0.0, 5000.0) """Range of the continuous light_intensity factor.""" @@ -200,13 +203,11 @@ def make_rich_dataset(seed: int, num_episodes: int = 3000) -> SensitivityDataset def _demo(): """Run the full pipeline on a synthetic dataset and save the marginals plot. - The Arena counterpart of running robolab's posterior_inference.py in generated-data - mode: simulate → fit → plot, with no eval data needed. Run as:: + Runs the pipeline end to end on generated data: simulate → fit → plot, with no eval + data needed. Run as:: python -m isaaclab_arena.tests.utils.synthetic_sensitivity --kind mixed --output /tmp/demo.png """ - import argparse - parser = argparse.ArgumentParser(description="Run the sensitivity pipeline on a synthetic dataset and plot it.") parser.add_argument( "--kind", @@ -215,19 +216,14 @@ def _demo(): help="'mixed' (1 cont + 1 cat, MNPE), 'continuous' (2 cont, NPE), or 'rich' (3 cont + 2 cat, MNPE).", ) parser.add_argument( - "--output", default="./sensitivity_synthetic.png", help="Output figure path; format follows the extension." + "--output", + default="eval/sensitivity_synthetic.png", + help="Output figure path; format follows the extension.", ) parser.add_argument("--seed", type=int, default=0) parser.add_argument("--num-episodes", type=int, default=2000) args = parser.parse_args() - import matplotlib - - matplotlib.use("Agg") - - from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer - from isaaclab_arena.analysis.sensitivity.plotting import plot_marginals - builder = {"mixed": make_mixed_dataset, "continuous": make_continuous_dataset, "rich": make_rich_dataset}[args.kind] dataset = builder(seed=args.seed, num_episodes=args.num_episodes) analyzer = SensitivityAnalyzer(dataset) diff --git a/sensitivity_mixed.png b/sensitivity_mixed.png deleted file mode 100644 index 0c74df597a..0000000000 --- a/sensitivity_mixed.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:52c02b579b63f11ca5fa1bcd009840eef8022faec7e0a87575cb63b1ad6d2a8d -size 96934 diff --git a/sensitivity_rich.png b/sensitivity_rich.png deleted file mode 100644 index 483b8cf96b..0000000000 --- a/sensitivity_rich.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:314e08938b0043b939df50c94a45ceac71c8f182ee4de120fa13da9194719fb7 -size 189814 diff --git a/sensitivity_synthetic.png b/sensitivity_synthetic.png deleted file mode 100644 index c511034b18..0000000000 --- a/sensitivity_synthetic.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:9ea649f10a53d11a4069154824190bbabc57e1697e1755c5fe87dba6393620c6 -size 77273 From e695b378579acaf06640dfbe4df14f3ac09ad065 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 13:52:35 +0200 Subject: [PATCH 43/74] Commit to binary outcomes in the conditioning default Outcomes are binary (0/1) in the current scope, so default_observation now conditions on success (1) for every outcome and asserts binary instead of carrying a speculative mean-for-continuous branch. CLI help updated to match (use 1 for success, 0 for failure). Signed-off-by: Clemens Volk --- isaaclab_arena/analysis/sensitivity/analyzer.py | 16 +++++++--------- .../analysis/sensitivity/generate_report.py | 4 ++-- 2 files changed, 9 insertions(+), 11 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/analyzer.py b/isaaclab_arena/analysis/sensitivity/analyzer.py index 2e87a64812..51fc6e6174 100644 --- a/isaaclab_arena/analysis/sensitivity/analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/analyzer.py @@ -84,17 +84,15 @@ def fit(self, training_batch_size: int = 50): return self.posterior def default_observation(self) -> torch.Tensor: - """Default observation to condition on: 1.0 for binary outcomes, the mean otherwise. + """Condition on success (outcome = 1) for every outcome. - A binary outcome's interesting query is "what produced success?" (condition on 1.0); - a continuous outcome has no such value, so its mean is the natural "typical case". + Outcomes are binary (0/1) in the current scope, so the natural default query is + "what produced success?". Asserts the outcomes are binary, so adding a continuous + outcome later fails loudly here instead of silently conditioning on a meaningless value. """ - outcome_values = [] - for column_index in range(self.dataset.x.shape[1]): - column = self.dataset.x[:, column_index] - is_binary = set(column.tolist()).issubset({0.0, 1.0}) - outcome_values.append(1.0 if is_binary else float(column.mean())) - return torch.tensor(outcome_values, dtype=torch.float32) + is_binary = set(self.dataset.x.flatten().tolist()).issubset({0.0, 1.0}) + assert is_binary, "default_observation assumes binary (0/1) outcomes; pass an explicit observation otherwise." + return torch.ones(self.dataset.x.shape[1], dtype=torch.float32) def sample_posterior(self, observation: torch.Tensor | None = None, num_samples: int = 5000) -> torch.Tensor: """Sample the joint posterior over all factors at observation (default: see above). diff --git a/isaaclab_arena/analysis/sensitivity/generate_report.py b/isaaclab_arena/analysis/sensitivity/generate_report.py index af7ad67a8b..65ffac09d2 100644 --- a/isaaclab_arena/analysis/sensitivity/generate_report.py +++ b/isaaclab_arena/analysis/sensitivity/generate_report.py @@ -31,7 +31,7 @@ def generate_report( jsonl_path: episode_summary.jsonl produced by eval_runner. output_path: Destination figure file (parent dirs created if absent). observation: Outcome values to condition on, one per declared outcome. Defaults to - the analyzer's default (1.0 for binary outcomes, the mean otherwise). + conditioning on success (1) for every binary outcome. Returns: The resolved output path. @@ -72,7 +72,7 @@ def main(): default=None, help=( "Outcome values to condition on, one per declared outcome (in schema order). " - "Defaults to 1.0 for binary outcomes and the mean otherwise." + "Outcomes are binary, so use 1 for success or 0 for failure. Defaults to 1 (success)." ), ) args = parser.parse_args() From a12aec8a78d11fb41d77570f97e0038073c7981e Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 14:01:25 +0200 Subject: [PATCH 44/74] Add sensitivity analysis documentation page MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit New concept page (docs/pages/concepts/policy/concept_sensitivity_analysis.rst) covering the toolbox: the recording → schema → inference → report pipeline, factors.yaml / episode_summary.jsonl inputs, MNPE vs NPE selection, the generate_report CLI and the synthetic demo, how to read the marginals, and why a joint posterior beats per-factor success rates. Linked from the Policy concepts toctree. Signed-off-by: Clemens Volk --- .../policy/concept_sensitivity_analysis.rst | 165 ++++++++++++++++++ docs/pages/concepts/policy/index.rst | 1 + 2 files changed, 166 insertions(+) create mode 100644 docs/pages/concepts/policy/concept_sensitivity_analysis.rst diff --git a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst new file mode 100644 index 0000000000..9db7a5f96d --- /dev/null +++ b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst @@ -0,0 +1,165 @@ +Sensitivity Analysis +==================== + +The sensitivity-analysis toolbox answers a single question about a policy: +*which environment conditions drive success?* Given the per-episode results of an +evaluation sweep — where factors such as lighting, object mass, or table material were +varied — it fits a posterior over those factors conditioned on the outcome and renders +one figure summarising which factor values are associated with success. + +It builds directly on the evaluation pipeline described in +:doc:`concept_evaluation_types` and the metrics described in +:doc:`../task/concept_metrics_design`. + +How it works +------------ + +The toolbox is a thin analysis layer over `sbi `_'s +neural posterior estimators. The flow is: + +1. **Per-episode recording.** During evaluation, ``episode_writer`` appends one row per + episode to an ``episode_summary.jsonl`` file — the factor values that were used + (``arena_env_args``) and the measured per-episode ``outcomes``. +2. **Schema.** A hand-authored ``factors.yaml`` declares which of those columns are + *factors* to analyse (and whether each is continuous or categorical) and which are + *outcomes* to condition on. +3. **Inference.** ``SensitivityAnalyzer`` loads the pair, trains an estimator on the full + ``(theta, x)`` — every factor and every outcome jointly — and samples the joint + posterior conditioned on a chosen observation (by default, success). +4. **Report.** One figure is produced: a smooth density curve for each continuous factor + and a probability bar chart for each categorical factor. + +Inputs +------ + +**factors.yaml** declares the slice the data came from, the factors to study, and the +outcomes: + +.. code-block:: yaml + + slice: + policy: pi0 + task: PickUpObject + embodiment: droid + + factors: + light_intensity: + type: continuous + range: [[0.0, 5000.0]] # one [low, high] pair; inferred from data if omitted + table_material: + type: categorical + choices: [oak, walnut, bamboo] + + outcomes: + success: + type: bool + +**episode_summary.jsonl** is produced by the eval runner — one JSON object per episode: + +.. code-block:: json + + {"job_name": "pi0_sweep", "episode_idx": 0, + "arena_env_args": {"light_intensity": 3200.0, "table_material": "oak"}, + "outcomes": {"success": 1}} + +The schema's factor and outcome names need only be a subset of the keys present in each +row; extra ``arena_env_args`` keys are ignored, so the same JSONL can back several +different analyses. + +Choice of estimator +------------------- + +``SensitivityAnalyzer`` picks the estimator from the schema automatically: + +.. list-table:: + :header-rows: 1 + :widths: 25 25 50 + + * - Schema + - Estimator + - Notes + * - Any categorical factor + - MNPE + - Mixed density estimator; handles continuous + categorical factors together. + * - All continuous factors + - NPE + - Restricts to a Gaussian on a single factor, so a meaningful continuous-only + analysis needs at least two continuous factors. + +Continuous factors are normalised to ``[0, 1]`` before fitting and de-normalised when +sampling, so factors on very different scales (e.g. light in the thousands, an offset in +the hundredths) train on equal footing. Outcomes are binary (0/1); the default query +conditions on success (1). + +Running a report +---------------- + +Point the report generator at a ``(factors.yaml, episode_summary.jsonl)`` pair. The output +format follows the file extension (``.png``, ``.pdf``, …); reports are written under +``eval/`` by default. + +.. code-block:: bash + + python -m isaaclab_arena.analysis.sensitivity.generate_report \ + --factors_yaml factors.yaml \ + --episode_summary episode_summary.jsonl \ + --output eval/sensitivity_report.png + +Pass ``--observation`` to condition on specific outcome values (one per declared outcome, +in schema order); since outcomes are binary, use ``1`` for success or ``0`` for failure. +It defaults to ``1`` (success). + +Trying it on synthetic data +--------------------------- + +A synthetic simulator with a *known* ground truth lets you run the whole pipeline on CPU, +without Isaac Sim — useful for seeing the output shape and for validating the toolbox +(the recovered posterior should reflect the planted relationship): + +.. code-block:: bash + + # mixed (MNPE): one continuous + one categorical factor + python -m isaaclab_arena.tests.utils.synthetic_sensitivity --kind mixed --output eval/demo.png + + # continuous (NPE): two continuous factors + python -m isaaclab_arena.tests.utils.synthetic_sensitivity --kind continuous --output eval/demo.png + + # rich: three continuous + two categorical factors + python -m isaaclab_arena.tests.utils.synthetic_sensitivity --kind rich --output eval/demo.png + +Reading the output +------------------ + +Each panel is the posterior over one factor *conditioned on success* — "given the policy +succeeded, which values of this factor were responsible?" For a continuous factor, mass +concentrated at one end of its range means success favoured that end (e.g. a curve rising +toward bright light → the policy is light-gated). For a categorical factor, the tallest +bar is the value most associated with success. + +Why a joint posterior, not a success rate per factor? +----------------------------------------------------- + +The simplest analysis would chart a success rate for each factor independently. That hides +the two things that matter most in a multi-factor sweep: + +- **Factors interact.** How much light a policy needs can depend on the object — a matte + object may succeed at low light while a shiny one needs far more. A per-factor + "success vs light" curve averages over objects and reports one blurry gate that is wrong + for both. The joint posterior keeps the interaction, so you can condition on a specific + object and see its gate. +- **Factors confound each other.** If bright-light episodes also happened to use an easy + object, a per-factor light chart cannot tell which one drove success. Modelling all + factors together attributes the effect to the factor that actually carries it. + +The per-factor rate is a projection of the joint posterior — derivable from it, but not the +other way around. The toolbox therefore always fits the joint and reads the per-factor +marginals from it. + +Current scope +------------- + +- Outcomes are treated as **binary** (0/1). Conditioning defaults to success; a continuous + outcome is rejected with a clear error rather than silently averaged. +- Continuous **vector** factors (``dim > 1``) are reserved for a future extension. +- The estimators run on CPU and do not require Isaac Sim, so a report can be generated + anywhere the evaluation JSONL is available. diff --git a/docs/pages/concepts/policy/index.rst b/docs/pages/concepts/policy/index.rst index 8fb97a554e..870e226b9c 100644 --- a/docs/pages/concepts/policy/index.rst +++ b/docs/pages/concepts/policy/index.rst @@ -91,3 +91,4 @@ More details :maxdepth: 1 concept_evaluation_types + concept_sensitivity_analysis From 4a0a47b80a06a7c2ef4ade403e8f4990eab55ca2 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 14:19:02 +0200 Subject: [PATCH 45/74] Address docs review comments on the sensitivity page - move the 'why a joint posterior' section above 'How it works' so the motivation for simulation-based inference (MNPE/NPE) comes first - drop 'hand-authored' from the factors.yaml description (it will be auto-generated) - remove the eval-pipeline cross-reference sentence and the 'subset of keys' paragraph - tighten the recording / inference / report wording - add a TODO to include a sample report figure in 'Reading the output' Signed-off-by: Clemens Volk --- .../policy/concept_sensitivity_analysis.rst | 64 +++++++++---------- 1 file changed, 29 insertions(+), 35 deletions(-) diff --git a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst index 9db7a5f96d..7349dff5ec 100644 --- a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst +++ b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst @@ -7,9 +7,24 @@ evaluation sweep — where factors such as lighting, object mass, or table mater varied — it fits a posterior over those factors conditioned on the outcome and renders one figure summarising which factor values are associated with success. -It builds directly on the evaluation pipeline described in -:doc:`concept_evaluation_types` and the metrics described in -:doc:`../task/concept_metrics_design`. +Why a joint posterior, not a success rate per factor? +----------------------------------------------------- + +The simplest analysis would chart a success rate for each factor independently. That hides +the two things that matter most in a multi-factor sweep: + +- **Factors interact.** How much light a policy needs can depend on the object — a matte + object may succeed at low light while a shiny one needs far more. A per-factor + "success vs light" curve averages over objects and reports one blurry gate that is wrong + for both. The joint posterior keeps the interaction, so you can condition on a specific + object and see its gate. +- **Factors confound each other.** If bright-light episodes also happened to use an easy + object, a per-factor light chart cannot tell which one drove success. Modelling all + factors together attributes the effect to the factor that actually carries it. + +The per-factor rate is a projection of the joint posterior — derivable from it, but not the +other way around. The toolbox therefore always fits the joint — via simulation-based +inference (MNPE or NPE) — and reads the per-factor marginals from it. How it works ------------ @@ -18,16 +33,14 @@ The toolbox is a thin analysis layer over `sbi `_'s neural posterior estimators. The flow is: 1. **Per-episode recording.** During evaluation, ``episode_writer`` appends one row per - episode to an ``episode_summary.jsonl`` file — the factor values that were used - (``arena_env_args``) and the measured per-episode ``outcomes``. -2. **Schema.** A hand-authored ``factors.yaml`` declares which of those columns are - *factors* to analyse (and whether each is continuous or categorical) and which are - *outcomes* to condition on. + episode to an ``episode_summary.jsonl`` file. +2. **Schema.** A ``factors.yaml`` declares which of those columns are *factors* to analyse + (and whether each is continuous or categorical) and which are *outcomes* to condition on. 3. **Inference.** ``SensitivityAnalyzer`` loads the pair, trains an estimator on the full - ``(theta, x)`` — every factor and every outcome jointly — and samples the joint - posterior conditioned on a chosen observation (by default, success). -4. **Report.** One figure is produced: a smooth density curve for each continuous factor - and a probability bar chart for each categorical factor. + ``(theta, x)`` jointly, and samples the joint posterior conditioned on a chosen + observation (by default, success). +4. **Report.** A smooth density curve for each continuous factor and a probability bar chart + for each categorical factor. Inputs ------ @@ -62,10 +75,6 @@ outcomes: "arena_env_args": {"light_intensity": 3200.0, "table_material": "oak"}, "outcomes": {"success": 1}} -The schema's factor and outcome names need only be a subset of the keys present in each -row; extra ``arena_env_args`` keys are ignored, so the same JSONL can back several -different analyses. - Choice of estimator ------------------- @@ -130,31 +139,16 @@ without Isaac Sim — useful for seeing the output shape and for validating the Reading the output ------------------ +.. todo:: + + Add a sample report figure here and walk through reading it. + Each panel is the posterior over one factor *conditioned on success* — "given the policy succeeded, which values of this factor were responsible?" For a continuous factor, mass concentrated at one end of its range means success favoured that end (e.g. a curve rising toward bright light → the policy is light-gated). For a categorical factor, the tallest bar is the value most associated with success. -Why a joint posterior, not a success rate per factor? ------------------------------------------------------ - -The simplest analysis would chart a success rate for each factor independently. That hides -the two things that matter most in a multi-factor sweep: - -- **Factors interact.** How much light a policy needs can depend on the object — a matte - object may succeed at low light while a shiny one needs far more. A per-factor - "success vs light" curve averages over objects and reports one blurry gate that is wrong - for both. The joint posterior keeps the interaction, so you can condition on a specific - object and see its gate. -- **Factors confound each other.** If bright-light episodes also happened to use an easy - object, a per-factor light chart cannot tell which one drove success. Modelling all - factors together attributes the effect to the factor that actually carries it. - -The per-factor rate is a projection of the joint posterior — derivable from it, but not the -other way around. The toolbox therefore always fits the joint and reads the per-factor -marginals from it. - Current scope ------------- From 7871aa14e223bfb54848849817d21011135a959e Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 14:33:34 +0200 Subject: [PATCH 46/74] Relocate synthetic generator into the package and dedupe its builders - move synthetic_sensitivity.py (tests/utils) -> analysis/sensitivity/synthetic.py so the synthetic example ships with the toolbox and the demo lives in the core module (run: python -m isaaclab_arena.analysis.sensitivity.synthetic --kind {mixed,continuous,rich}); the recovery test now imports it from the package (tests -> package direction) - dedupe the three make_* builders via _sample_categorical and _build_dataset helpers, keeping each fixture's ground-truth logit explicit and the RNG draw order unchanged - update the docs demo commands to the new module path Signed-off-by: Clemens Volk --- .../policy/concept_sensitivity_analysis.rst | 6 +- .../sensitivity/synthetic.py} | 127 +++++++++--------- .../tests/test_sensitivity_analysis.py | 4 +- 3 files changed, 68 insertions(+), 69 deletions(-) rename isaaclab_arena/{tests/utils/synthetic_sensitivity.py => analysis/sensitivity/synthetic.py} (70%) diff --git a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst index 7349dff5ec..9100b9e700 100644 --- a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst +++ b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst @@ -128,13 +128,13 @@ without Isaac Sim — useful for seeing the output shape and for validating the .. code-block:: bash # mixed (MNPE): one continuous + one categorical factor - python -m isaaclab_arena.tests.utils.synthetic_sensitivity --kind mixed --output eval/demo.png + python -m isaaclab_arena.analysis.sensitivity.synthetic --kind mixed --output eval/demo.png # continuous (NPE): two continuous factors - python -m isaaclab_arena.tests.utils.synthetic_sensitivity --kind continuous --output eval/demo.png + python -m isaaclab_arena.analysis.sensitivity.synthetic --kind continuous --output eval/demo.png # rich: three continuous + two categorical factors - python -m isaaclab_arena.tests.utils.synthetic_sensitivity --kind rich --output eval/demo.png + python -m isaaclab_arena.analysis.sensitivity.synthetic --kind rich --output eval/demo.png Reading the output ------------------ diff --git a/isaaclab_arena/tests/utils/synthetic_sensitivity.py b/isaaclab_arena/analysis/sensitivity/synthetic.py similarity index 70% rename from isaaclab_arena/tests/utils/synthetic_sensitivity.py rename to isaaclab_arena/analysis/sensitivity/synthetic.py index e6d56c0e97..bd7e645f62 100644 --- a/isaaclab_arena/tests/utils/synthetic_sensitivity.py +++ b/isaaclab_arena/analysis/sensitivity/synthetic.py @@ -90,6 +90,38 @@ def _sample_success(success_logit: torch.Tensor) -> torch.Tensor: return torch.bernoulli(torch.sigmoid(success_logit)) +def _sample_categorical( + base_logit: dict[str, float], num_episodes: int +) -> tuple[list[str], torch.Tensor, torch.Tensor]: + """Uniformly sample a categorical factor from its base-logit table. + + Returns (choices, per-episode integer codes, per-episode base success logit). + """ + choices = list(base_logit) + codes = torch.randint(0, len(choices), (num_episodes,)) + base_logit_per_choice = torch.tensor([base_logit[choice] for choice in choices]) + return choices, codes, base_logit_per_choice[codes] + + +def _build_dataset( + continuous: list[tuple[str, tuple[float, float], torch.Tensor]], + categorical: list[tuple[str, list[str], torch.Tensor]], + success: torch.Tensor, +) -> SensitivityDataset: + """Assemble a SensitivityDataset from sampled factors and the binary success outcome. + + continuous: (name, value_range, values) per continuous factor. + categorical: (name, choices, integer codes) per categorical factor. + """ + factors = [FactorSpec(name=n, type="continuous", range=[list(r)]) for n, r, _ in continuous] + factors += [FactorSpec(name=n, type="categorical", choices=c) for n, c, _ in categorical] + schema = FactorSchema(slice=_SLICE, factors=factors, outcomes=[OutcomeSpec(name=_OUTCOME_NAME, type="bool")]) + # Continuous columns first, then integer-coded categorical columns — the layout + # SensitivityDataset.factor_columns describes and the estimators expect. + columns = [values for _, _, values in continuous] + [codes.float() for _, _, codes in categorical] + return SensitivityDataset(schema, torch.stack(columns, dim=1), success.unsqueeze(1)) + + def make_continuous_dataset(seed: int, num_episodes: int = 2000) -> SensitivityDataset: """Two continuous factors (light_intensity, grasp_offset) driving success. @@ -98,25 +130,20 @@ def make_continuous_dataset(seed: int, num_episodes: int = 2000) -> SensitivityD and low offset values. Two factors keep theta 2-D, away from NPE's 1-D Gaussian fallback. """ torch.manual_seed(seed) - light_intensity = _sample_uniform(LIGHT_RANGE, num_episodes) grasp_offset = _sample_uniform(GRASP_OFFSET_RANGE, num_episodes) - success_logit = LIGHT_WEIGHT * _normalized(light_intensity, LIGHT_RANGE) - OFFSET_WEIGHT * _normalized( - grasp_offset, GRASP_OFFSET_RANGE + success = _sample_success( + LIGHT_WEIGHT * _normalized(light_intensity, LIGHT_RANGE) + - OFFSET_WEIGHT * _normalized(grasp_offset, GRASP_OFFSET_RANGE) ) - success = _sample_success(success_logit) - - schema = FactorSchema( - slice=_SLICE, - factors=[ - FactorSpec(name="light_intensity", type="continuous", range=[list(LIGHT_RANGE)]), - FactorSpec(name="grasp_offset", type="continuous", range=[list(GRASP_OFFSET_RANGE)]), + return _build_dataset( + continuous=[ + ("light_intensity", LIGHT_RANGE, light_intensity), + ("grasp_offset", GRASP_OFFSET_RANGE, grasp_offset), ], - outcomes=[OutcomeSpec(name=_OUTCOME_NAME, type="bool")], + categorical=[], + success=success, ) - theta = torch.stack([light_intensity, grasp_offset], dim=1) - x = success.unsqueeze(1) - return SensitivityDataset(schema, theta, x) def make_mixed_dataset(seed: int, num_episodes: int = 2000) -> SensitivityDataset: @@ -127,28 +154,14 @@ def make_mixed_dataset(seed: int, num_episodes: int = 2000) -> SensitivityDatase favor high light values and oak. """ torch.manual_seed(seed) - - materials = list(MATERIAL_BASE_LOGIT) - material_base_logits = torch.tensor([MATERIAL_BASE_LOGIT[m] for m in materials]) - light_intensity = _sample_uniform(LIGHT_RANGE, num_episodes) - material_code = torch.randint(0, len(materials), (num_episodes,)) - success_logit = material_base_logits[material_code] + LIGHT_WEIGHT * _normalized(light_intensity, LIGHT_RANGE) - success = _sample_success(success_logit) - - schema = FactorSchema( - slice=_SLICE, - factors=[ - FactorSpec(name="light_intensity", type="continuous", range=[list(LIGHT_RANGE)]), - FactorSpec(name="table_material", type="categorical", choices=materials), - ], - outcomes=[OutcomeSpec(name=_OUTCOME_NAME, type="bool")], + materials, material_code, material_logit = _sample_categorical(MATERIAL_BASE_LOGIT, num_episodes) + success = _sample_success(material_logit + LIGHT_WEIGHT * _normalized(light_intensity, LIGHT_RANGE)) + return _build_dataset( + continuous=[("light_intensity", LIGHT_RANGE, light_intensity)], + categorical=[("table_material", materials, material_code)], + success=success, ) - # Continuous column first, then the integer-coded categorical column — the layout - # SensitivityDataset.factor_columns describes and the estimators expect. - theta = torch.stack([light_intensity, material_code.float()], dim=1) - x = success.unsqueeze(1) - return SensitivityDataset(schema, theta, x) def make_rich_dataset(seed: int, num_episodes: int = 3000) -> SensitivityDataset: @@ -160,44 +173,30 @@ def make_rich_dataset(seed: int, num_episodes: int = 3000) -> SensitivityDataset posterior conditioned on success should recover all of them at once. """ torch.manual_seed(seed) - - object_types = list(OBJECT_TYPE_BASE_LOGIT) - materials = list(MATERIAL_BASE_LOGIT) - light_intensity = _sample_uniform(LIGHT_RANGE, num_episodes) object_mass = _sample_uniform(OBJECT_MASS_RANGE, num_episodes) camera_distance = _sample_uniform(CAMERA_DISTANCE_RANGE, num_episodes) - object_type_code = torch.randint(0, len(object_types), (num_episodes,)) - material_code = torch.randint(0, len(materials), (num_episodes,)) - - object_type_base = torch.tensor([OBJECT_TYPE_BASE_LOGIT[t] for t in object_types]) - material_base = torch.tensor([MATERIAL_BASE_LOGIT[m] for m in materials]) - success_logit = ( + object_types, object_type_code, object_type_logit = _sample_categorical(OBJECT_TYPE_BASE_LOGIT, num_episodes) + materials, material_code, material_logit = _sample_categorical(MATERIAL_BASE_LOGIT, num_episodes) + success = _sample_success( LIGHT_WEIGHT * _normalized(light_intensity, LIGHT_RANGE) - MASS_WEIGHT * _normalized(object_mass, OBJECT_MASS_RANGE) - DISTANCE_WEIGHT * _normalized(camera_distance, CAMERA_DISTANCE_RANGE) - + object_type_base[object_type_code] - + material_base[material_code] + + object_type_logit + + material_logit ) - success = _sample_success(success_logit) - - schema = FactorSchema( - slice=_SLICE, - factors=[ - FactorSpec(name="light_intensity", type="continuous", range=[list(LIGHT_RANGE)]), - FactorSpec(name="object_mass", type="continuous", range=[list(OBJECT_MASS_RANGE)]), - FactorSpec(name="camera_distance", type="continuous", range=[list(CAMERA_DISTANCE_RANGE)]), - FactorSpec(name="object_type", type="categorical", choices=object_types), - FactorSpec(name="table_material", type="categorical", choices=materials), + return _build_dataset( + continuous=[ + ("light_intensity", LIGHT_RANGE, light_intensity), + ("object_mass", OBJECT_MASS_RANGE, object_mass), + ("camera_distance", CAMERA_DISTANCE_RANGE, camera_distance), ], - outcomes=[OutcomeSpec(name=_OUTCOME_NAME, type="bool")], - ) - # Continuous columns first (in declared order), then the integer-coded categorical columns. - theta = torch.stack( - [light_intensity, object_mass, camera_distance, object_type_code.float(), material_code.float()], dim=1 + categorical=[ + ("object_type", object_types, object_type_code), + ("table_material", materials, material_code), + ], + success=success, ) - x = success.unsqueeze(1) - return SensitivityDataset(schema, theta, x) def _demo(): @@ -206,7 +205,7 @@ def _demo(): Runs the pipeline end to end on generated data: simulate → fit → plot, with no eval data needed. Run as:: - python -m isaaclab_arena.tests.utils.synthetic_sensitivity --kind mixed --output /tmp/demo.png + python -m isaaclab_arena.analysis.sensitivity.synthetic --kind mixed --output eval/demo.png """ parser = argparse.ArgumentParser(description="Run the sensitivity pipeline on a synthetic dataset and plot it.") parser.add_argument( diff --git a/isaaclab_arena/tests/test_sensitivity_analysis.py b/isaaclab_arena/tests/test_sensitivity_analysis.py index 10174b0381..088a158c0c 100644 --- a/isaaclab_arena/tests/test_sensitivity_analysis.py +++ b/isaaclab_arena/tests/test_sensitivity_analysis.py @@ -6,7 +6,7 @@ """End-to-end sensitivity-analysis tests on synthetic data with a known ground truth. Each test fits a SensitivityAnalyzer on a dataset whose factor→outcome relationship is -planted by synthetic_sensitivity (brighter light, smaller grasp offset, and oak raise +planted by the synthetic module (brighter light, smaller grasp offset, and oak raise success), then asserts the posterior conditioned on success recovers that relationship. The data is built in memory, so these run on CPU without Isaac Sim. They cover both estimator paths: MNPE for mixed schemas, NPE for continuous-only (with 2-D theta). @@ -18,7 +18,7 @@ import torch from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer -from isaaclab_arena.tests.utils.synthetic_sensitivity import ( +from isaaclab_arena.analysis.sensitivity.synthetic import ( GRASP_OFFSET_RANGE, LIGHT_RANGE, MATERIAL_BASE_LOGIT, From 096ba3e8bd630a1ad36bfd7cd24b58896aeca291 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 14:42:45 +0200 Subject: [PATCH 47/74] Decouple plot_marginals from the analyzer MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit plot_marginals now takes (samples, dataset, observation) and is a pure renderer — it no longer runs inference. Callers sample via analyzer.sample_posterior and pass the draws in. Plotting depends on the dataset (schema/layout), not on the inference object. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/generate_report.py | 7 +++-- .../analysis/sensitivity/plotting.py | 30 +++++++++---------- .../analysis/sensitivity/synthetic.py | 4 ++- 3 files changed, 22 insertions(+), 19 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/generate_report.py b/isaaclab_arena/analysis/sensitivity/generate_report.py index 65ffac09d2..135019ddcf 100644 --- a/isaaclab_arena/analysis/sensitivity/generate_report.py +++ b/isaaclab_arena/analysis/sensitivity/generate_report.py @@ -40,9 +40,12 @@ def generate_report( analyzer = SensitivityAnalyzer(dataset) analyzer.fit() - observation_tensor = None if observation is None else torch.tensor(observation, dtype=torch.float32) + observation_tensor = ( + analyzer.default_observation() if observation is None else torch.tensor(observation, dtype=torch.float32) + ) + samples = analyzer.sample_posterior(observation_tensor) output_path = Path(output_path) - plot_marginals(analyzer, observation=observation_tensor, output_path=str(output_path)) + plot_marginals(samples, dataset, observation_tensor, output_path=str(output_path)) plt.close("all") print(f"[INFO] Wrote report → {output_path}") return output_path diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index d31e7af792..80b8a08798 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -13,8 +13,9 @@ from typing import TYPE_CHECKING if TYPE_CHECKING: - from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer - from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec + import torch + + from isaaclab_arena.analysis.sensitivity.dataset import FactorSpec, SensitivityDataset _CONTINUOUS_COLOR = "steelblue" _CATEGORICAL_COLOR = "steelblue" @@ -22,32 +23,29 @@ def plot_marginals( - analyzer: SensitivityAnalyzer, - observation=None, - num_samples: int = 5000, + samples: torch.Tensor, + dataset: SensitivityDataset, + observation: torch.Tensor, output_path: str | None = None, ): """Plot the posterior marginal of every factor in a single figure. - Samples the joint posterior at observation (default: analyzer.default_observation()), - then draws one panel per factor — a density curve for continuous factors, a probability - bar chart for categorical ones, wrapped into a grid. + A pure renderer: it draws already-sampled posterior draws and does not run inference. + One panel per factor — a density curve for continuous factors, a probability bar chart + for categorical ones, wrapped into a grid. Args: - analyzer: A fitted SensitivityAnalyzer. - observation: Outcome vector to condition on. Defaults to the analyzer's default. - num_samples: Number of posterior samples to draw. + samples: ``(num_samples, total_factor_dim)`` posterior draws in the dataset's factor + layout (continuous-first, original units), e.g. from ``SensitivityAnalyzer.sample_posterior``. + dataset: The dataset, for the factor schema and column layout. + observation: The outcome vector the samples were conditioned on (shown in the title). output_path: If given, save the figure here. The format follows the path's extension (.png, .pdf, …); parent directories are created. Returns: The matplotlib Figure. """ - if observation is None: - observation = analyzer.default_observation() - samples = analyzer.sample_posterior(observation, num_samples).cpu().numpy() - - dataset = analyzer.dataset + samples = samples.cpu().numpy() factors = dataset.schema.factors # Wrap panels into a grid (at most 3 columns) so many factors stay readable. num_columns = min(3, len(factors)) diff --git a/isaaclab_arena/analysis/sensitivity/synthetic.py b/isaaclab_arena/analysis/sensitivity/synthetic.py index bd7e645f62..bf6e628f9f 100644 --- a/isaaclab_arena/analysis/sensitivity/synthetic.py +++ b/isaaclab_arena/analysis/sensitivity/synthetic.py @@ -227,7 +227,9 @@ def _demo(): dataset = builder(seed=args.seed, num_episodes=args.num_episodes) analyzer = SensitivityAnalyzer(dataset) analyzer.fit() - plot_marginals(analyzer, output_path=args.output) + observation = analyzer.default_observation() + samples = analyzer.sample_posterior(observation) + plot_marginals(samples, dataset, observation, output_path=args.output) print(f"[INFO] Wrote synthetic sensitivity report → {args.output}") From b9a9cf5ca918331d7f332c42bf371ee14090ace0 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 14:50:15 +0200 Subject: [PATCH 48/74] Model synthetic factors as frozen dataclasses Replace the parallel RANGE/WEIGHT constants and the _normalized/_sample_uniform/ _sample_categorical helpers with _ContinuousFactor / _CategoricalFactor dataclasses that each know how to sample, contribute their success-logit term, and emit their FactorSpec. The 10 flat constants become 6 self-describing factor instances with signed weights, and the make_* builders shrink to: sample factors, sum their logits, build. RNG draw order is preserved, so seeded datasets are unchanged. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/synthetic.py | 191 ++++++++---------- .../tests/test_sensitivity_analysis.py | 12 +- 2 files changed, 94 insertions(+), 109 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/synthetic.py b/isaaclab_arena/analysis/sensitivity/synthetic.py index bf6e628f9f..09ba2c6e75 100644 --- a/isaaclab_arena/analysis/sensitivity/synthetic.py +++ b/isaaclab_arena/analysis/sensitivity/synthetic.py @@ -11,11 +11,10 @@ known, a test can fit a SensitivityAnalyzer on the data and assert the recovered posterior reflects it. -Ground truth (single-sourced in the constants below): - - light_intensity is continuous; higher light raises success (LIGHT_WEIGHT > 0). - - grasp_offset is continuous; a *smaller* offset raises success (OFFSET_WEIGHT > 0). - - table_material is categorical; MATERIAL_BASE_LOGIT makes oak the most successful - material and bamboo the least. +Ground truth (single-sourced in the factor definitions below): + - light_intensity is continuous; brighter raises success (LIGHT.weight > 0). + - grasp_offset is continuous; a *smaller* offset raises success (GRASP_OFFSET.weight < 0). + - table_material is categorical; MATERIAL makes oak the most successful, bamboo the least. - success is a binary outcome drawn from Bernoulli(sigmoid(logit)). make_mixed_dataset exercises the MNPE path (continuous + categorical); make_continuous_dataset @@ -26,6 +25,7 @@ import argparse import torch +from dataclasses import dataclass from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer from isaaclab_arena.analysis.sensitivity.dataset import ( @@ -37,89 +37,90 @@ ) from isaaclab_arena.analysis.sensitivity.plotting import plot_marginals -LIGHT_RANGE: tuple[float, float] = (0.0, 5000.0) -"""Range of the continuous light_intensity factor.""" -LIGHT_WEIGHT: float = 2.5 -"""Success-logit gain per unit of normalized light. Positive ⇒ brighter is more successful.""" +@dataclass(frozen=True) +class _ContinuousFactor: + """A continuous factor with a planted, signed effect on the success logit.""" -GRASP_OFFSET_RANGE: tuple[float, float] = (0.0, 0.2) -"""Range (metres) of the continuous grasp_offset factor.""" + name: str + value_range: tuple[float, float] + weight: float # success-logit gain per normalized unit; the sign sets the direction of the effect -OFFSET_WEIGHT: float = 2.5 -"""Success-logit gain per unit of normalized offset. Subtracted ⇒ a smaller offset is more successful.""" + def sample(self, num_episodes: int) -> torch.Tensor: + low, high = self.value_range + return torch.rand(num_episodes) * (high - low) + low -MATERIAL_BASE_LOGIT: dict[str, float] = {"oak": 1.5, "walnut": 0.0, "bamboo": -1.5} -"""Per-material base success logit. Ordered best→worst, so oak should dominate the posterior.""" + def logit(self, values: torch.Tensor) -> torch.Tensor: + low, high = self.value_range + normalized = (values - 0.5 * (low + high)) / (0.5 * (high - low)) # map value_range onto [-1, 1] + return self.weight * normalized -OBJECT_MASS_RANGE: tuple[float, float] = (0.05, 2.0) -"""Range (kg) of the continuous object_mass factor.""" + def spec(self) -> FactorSpec: + return FactorSpec(name=self.name, type="continuous", range=[list(self.value_range)]) -MASS_WEIGHT: float = 1.5 -"""Success-logit gain per unit of normalized mass. Subtracted ⇒ a lighter object is more successful.""" + def column(self, values: torch.Tensor) -> torch.Tensor: + return values -CAMERA_DISTANCE_RANGE: tuple[float, float] = (0.3, 1.5) -"""Range (m) of the continuous camera_distance factor.""" -DISTANCE_WEIGHT: float = 1.5 -"""Success-logit gain per unit of normalized distance. Subtracted ⇒ a closer camera is more successful.""" +@dataclass(frozen=True) +class _CategoricalFactor: + """A categorical factor with a per-choice base success logit (ordered best→worst).""" -OBJECT_TYPE_BASE_LOGIT: dict[str, float] = {"cube": 1.2, "can": 0.0, "mug": -1.2} -"""Per-object-type base success logit. Ordered best→worst, so cube should dominate the posterior.""" + name: str + base_logit: dict[str, float] -_OUTCOME_NAME = "success" -_SLICE = SliceSpec(policy="synthetic", task="SyntheticTask", embodiment="synthetic") + @property + def choices(self) -> list[str]: + return list(self.base_logit) + def sample(self, num_episodes: int) -> torch.Tensor: + return torch.randint(0, len(self.base_logit), (num_episodes,)) -def _normalized(values: torch.Tensor, value_range: tuple[float, float]) -> torch.Tensor: - """Map values from value_range onto roughly [-1, 1] for the success logit.""" - low, high = value_range - midpoint = 0.5 * (low + high) - half_range = 0.5 * (high - low) - return (values - midpoint) / half_range + def logit(self, codes: torch.Tensor) -> torch.Tensor: + return torch.tensor([self.base_logit[choice] for choice in self.choices])[codes] + def spec(self) -> FactorSpec: + return FactorSpec(name=self.name, type="categorical", choices=self.choices) -def _sample_uniform(value_range: tuple[float, float], num_episodes: int) -> torch.Tensor: - """Draw num_episodes values uniformly over value_range.""" - low, high = value_range - return torch.rand(num_episodes) * (high - low) + low + def column(self, codes: torch.Tensor) -> torch.Tensor: + return codes.float() -def _sample_success(success_logit: torch.Tensor) -> torch.Tensor: - """Draw a binary success outcome per episode from Bernoulli(sigmoid(logit)).""" - return torch.bernoulli(torch.sigmoid(success_logit)) +# Planted ground truth: brighter light, a smaller grasp offset, a lighter object, a closer +# camera, and the leading category (oak / cube) all raise success. +LIGHT = _ContinuousFactor("light_intensity", (0.0, 5000.0), weight=2.5) +GRASP_OFFSET = _ContinuousFactor("grasp_offset", (0.0, 0.2), weight=-2.5) +OBJECT_MASS = _ContinuousFactor("object_mass", (0.05, 2.0), weight=-1.5) +CAMERA_DISTANCE = _ContinuousFactor("camera_distance", (0.3, 1.5), weight=-1.5) +MATERIAL = _CategoricalFactor("table_material", {"oak": 1.5, "walnut": 0.0, "bamboo": -1.5}) +OBJECT_TYPE = _CategoricalFactor("object_type", {"cube": 1.2, "can": 0.0, "mug": -1.2}) +_OUTCOME_NAME = "success" +_SLICE = SliceSpec(policy="synthetic", task="SyntheticTask", embodiment="synthetic") -def _sample_categorical( - base_logit: dict[str, float], num_episodes: int -) -> tuple[list[str], torch.Tensor, torch.Tensor]: - """Uniformly sample a categorical factor from its base-logit table. - Returns (choices, per-episode integer codes, per-episode base success logit). - """ - choices = list(base_logit) - codes = torch.randint(0, len(choices), (num_episodes,)) - base_logit_per_choice = torch.tensor([base_logit[choice] for choice in choices]) - return choices, codes, base_logit_per_choice[codes] +def _sample_success(success_logit: torch.Tensor) -> torch.Tensor: + """Draw a binary success outcome per episode from Bernoulli(sigmoid(logit)).""" + return torch.bernoulli(torch.sigmoid(success_logit)) def _build_dataset( - continuous: list[tuple[str, tuple[float, float], torch.Tensor]], - categorical: list[tuple[str, list[str], torch.Tensor]], + factors_and_columns: list[tuple[_ContinuousFactor | _CategoricalFactor, torch.Tensor]], success: torch.Tensor, ) -> SensitivityDataset: - """Assemble a SensitivityDataset from sampled factors and the binary success outcome. + """Assemble a SensitivityDataset from (factor, sampled column) pairs and the success outcome. - continuous: (name, value_range, values) per continuous factor. - categorical: (name, choices, integer codes) per categorical factor. + Continuous factors are placed before the categorical ones, matching the layout + SensitivityDataset.factor_columns expects. """ - factors = [FactorSpec(name=n, type="continuous", range=[list(r)]) for n, r, _ in continuous] - factors += [FactorSpec(name=n, type="categorical", choices=c) for n, c, _ in categorical] - schema = FactorSchema(slice=_SLICE, factors=factors, outcomes=[OutcomeSpec(name=_OUTCOME_NAME, type="bool")]) - # Continuous columns first, then integer-coded categorical columns — the layout - # SensitivityDataset.factor_columns describes and the estimators expect. - columns = [values for _, _, values in continuous] + [codes.float() for _, _, codes in categorical] - return SensitivityDataset(schema, torch.stack(columns, dim=1), success.unsqueeze(1)) + ordered = sorted(factors_and_columns, key=lambda pair: isinstance(pair[0], _CategoricalFactor)) + schema = FactorSchema( + slice=_SLICE, + factors=[factor.spec() for factor, _ in ordered], + outcomes=[OutcomeSpec(name=_OUTCOME_NAME, type="bool")], + ) + theta = torch.stack([factor.column(values) for factor, values in ordered], dim=1) + return SensitivityDataset(schema, theta, success.unsqueeze(1)) def make_continuous_dataset(seed: int, num_episodes: int = 2000) -> SensitivityDataset: @@ -130,20 +131,10 @@ def make_continuous_dataset(seed: int, num_episodes: int = 2000) -> SensitivityD and low offset values. Two factors keep theta 2-D, away from NPE's 1-D Gaussian fallback. """ torch.manual_seed(seed) - light_intensity = _sample_uniform(LIGHT_RANGE, num_episodes) - grasp_offset = _sample_uniform(GRASP_OFFSET_RANGE, num_episodes) - success = _sample_success( - LIGHT_WEIGHT * _normalized(light_intensity, LIGHT_RANGE) - - OFFSET_WEIGHT * _normalized(grasp_offset, GRASP_OFFSET_RANGE) - ) - return _build_dataset( - continuous=[ - ("light_intensity", LIGHT_RANGE, light_intensity), - ("grasp_offset", GRASP_OFFSET_RANGE, grasp_offset), - ], - categorical=[], - success=success, - ) + light = LIGHT.sample(num_episodes) + grasp_offset = GRASP_OFFSET.sample(num_episodes) + success = _sample_success(LIGHT.logit(light) + GRASP_OFFSET.logit(grasp_offset)) + return _build_dataset([(LIGHT, light), (GRASP_OFFSET, grasp_offset)], success) def make_mixed_dataset(seed: int, num_episodes: int = 2000) -> SensitivityDataset: @@ -154,14 +145,10 @@ def make_mixed_dataset(seed: int, num_episodes: int = 2000) -> SensitivityDatase favor high light values and oak. """ torch.manual_seed(seed) - light_intensity = _sample_uniform(LIGHT_RANGE, num_episodes) - materials, material_code, material_logit = _sample_categorical(MATERIAL_BASE_LOGIT, num_episodes) - success = _sample_success(material_logit + LIGHT_WEIGHT * _normalized(light_intensity, LIGHT_RANGE)) - return _build_dataset( - continuous=[("light_intensity", LIGHT_RANGE, light_intensity)], - categorical=[("table_material", materials, material_code)], - success=success, - ) + light = LIGHT.sample(num_episodes) + material = MATERIAL.sample(num_episodes) + success = _sample_success(LIGHT.logit(light) + MATERIAL.logit(material)) + return _build_dataset([(LIGHT, light), (MATERIAL, material)], success) def make_rich_dataset(seed: int, num_episodes: int = 3000) -> SensitivityDataset: @@ -173,29 +160,27 @@ def make_rich_dataset(seed: int, num_episodes: int = 3000) -> SensitivityDataset posterior conditioned on success should recover all of them at once. """ torch.manual_seed(seed) - light_intensity = _sample_uniform(LIGHT_RANGE, num_episodes) - object_mass = _sample_uniform(OBJECT_MASS_RANGE, num_episodes) - camera_distance = _sample_uniform(CAMERA_DISTANCE_RANGE, num_episodes) - object_types, object_type_code, object_type_logit = _sample_categorical(OBJECT_TYPE_BASE_LOGIT, num_episodes) - materials, material_code, material_logit = _sample_categorical(MATERIAL_BASE_LOGIT, num_episodes) + light = LIGHT.sample(num_episodes) + object_mass = OBJECT_MASS.sample(num_episodes) + camera_distance = CAMERA_DISTANCE.sample(num_episodes) + object_type = OBJECT_TYPE.sample(num_episodes) + material = MATERIAL.sample(num_episodes) success = _sample_success( - LIGHT_WEIGHT * _normalized(light_intensity, LIGHT_RANGE) - - MASS_WEIGHT * _normalized(object_mass, OBJECT_MASS_RANGE) - - DISTANCE_WEIGHT * _normalized(camera_distance, CAMERA_DISTANCE_RANGE) - + object_type_logit - + material_logit + LIGHT.logit(light) + + OBJECT_MASS.logit(object_mass) + + CAMERA_DISTANCE.logit(camera_distance) + + OBJECT_TYPE.logit(object_type) + + MATERIAL.logit(material) ) return _build_dataset( - continuous=[ - ("light_intensity", LIGHT_RANGE, light_intensity), - ("object_mass", OBJECT_MASS_RANGE, object_mass), - ("camera_distance", CAMERA_DISTANCE_RANGE, camera_distance), - ], - categorical=[ - ("object_type", object_types, object_type_code), - ("table_material", materials, material_code), + [ + (LIGHT, light), + (OBJECT_MASS, object_mass), + (CAMERA_DISTANCE, camera_distance), + (OBJECT_TYPE, object_type), + (MATERIAL, material), ], - success=success, + success, ) diff --git a/isaaclab_arena/tests/test_sensitivity_analysis.py b/isaaclab_arena/tests/test_sensitivity_analysis.py index 088a158c0c..5c16829a34 100644 --- a/isaaclab_arena/tests/test_sensitivity_analysis.py +++ b/isaaclab_arena/tests/test_sensitivity_analysis.py @@ -19,17 +19,17 @@ from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer from isaaclab_arena.analysis.sensitivity.synthetic import ( - GRASP_OFFSET_RANGE, - LIGHT_RANGE, - MATERIAL_BASE_LOGIT, + GRASP_OFFSET, + LIGHT, + MATERIAL, make_continuous_dataset, make_mixed_dataset, ) _NUM_SAMPLES = 5000 -_LIGHT_MIDPOINT = 0.5 * (LIGHT_RANGE[0] + LIGHT_RANGE[1]) -_OFFSET_MIDPOINT = 0.5 * (GRASP_OFFSET_RANGE[0] + GRASP_OFFSET_RANGE[1]) +_LIGHT_MIDPOINT = 0.5 * (LIGHT.value_range[0] + LIGHT.value_range[1]) +_OFFSET_MIDPOINT = 0.5 * (GRASP_OFFSET.value_range[0] + GRASP_OFFSET.value_range[1]) def _factor_samples(analyzer: SensitivityAnalyzer, samples: torch.Tensor, factor_name: str) -> np.ndarray: @@ -51,7 +51,7 @@ def test_mnpe_recovers_light_and_material_effects(): assert _factor_samples(analyzer, samples, "light_intensity").mean() > _LIGHT_MIDPOINT # Categorical effect: oak is the planted best material, bamboo the worst. - materials = list(MATERIAL_BASE_LOGIT) + materials = MATERIAL.choices material_codes = np.clip(np.round(_factor_samples(analyzer, samples, "table_material")), 0, len(materials) - 1) probabilities = np.bincount(material_codes.astype(int), minlength=len(materials)) / len(material_codes) assert materials[int(probabilities.argmax())] == "oak", f"expected oak most likely, got {probabilities}" From 0e688ee886b7bd80cd16361c6d12322366a15a84 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 15:03:49 +0200 Subject: [PATCH 49/74] Move default_observation from the analyzer to the dataset The default observation (condition on success) is derived purely from the dataset's outcomes and never touches the posterior, so it belongs on SensitivityDataset, not the inference object. sample_posterior now delegates to dataset.default_observation() and callers read it off the dataset. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/analyzer.py | 20 +++++-------------- .../analysis/sensitivity/dataset.py | 11 ++++++++++ .../analysis/sensitivity/generate_report.py | 2 +- .../analysis/sensitivity/synthetic.py | 2 +- 4 files changed, 18 insertions(+), 17 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/analyzer.py b/isaaclab_arena/analysis/sensitivity/analyzer.py index 51fc6e6174..58b0f987cf 100644 --- a/isaaclab_arena/analysis/sensitivity/analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/analyzer.py @@ -83,26 +83,16 @@ def fit(self, training_batch_size: int = 50): self.posterior = inference.build_posterior(density_estimator) return self.posterior - def default_observation(self) -> torch.Tensor: - """Condition on success (outcome = 1) for every outcome. - - Outcomes are binary (0/1) in the current scope, so the natural default query is - "what produced success?". Asserts the outcomes are binary, so adding a continuous - outcome later fails loudly here instead of silently conditioning on a meaningless value. - """ - is_binary = set(self.dataset.x.flatten().tolist()).issubset({0.0, 1.0}) - assert is_binary, "default_observation assumes binary (0/1) outcomes; pass an explicit observation otherwise." - return torch.ones(self.dataset.x.shape[1], dtype=torch.float32) - def sample_posterior(self, observation: torch.Tensor | None = None, num_samples: int = 5000) -> torch.Tensor: - """Sample the joint posterior over all factors at observation (default: see above). + """Sample the joint posterior over all factors at observation. - Returns a (num_samples, total_factor_dim) tensor laid out like theta — continuous - columns first (in original, denormalized units), then integer-coded categorical columns. + Defaults to the dataset's default observation (condition on success). Returns a + (num_samples, total_factor_dim) tensor laid out like theta — continuous columns first + (in original, denormalized units), then integer-coded categorical columns. """ assert self.posterior is not None, "Call fit() before sampling the posterior" if observation is None: - observation = self.default_observation() + observation = self.dataset.default_observation() with torch.no_grad(): normalized_samples = self.posterior.sample((num_samples,), x=observation) return self._denormalize(normalized_samples) diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py index 224297b1b9..cbbaf73e74 100644 --- a/isaaclab_arena/analysis/sensitivity/dataset.py +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -225,6 +225,17 @@ def outcome_columns(self) -> dict[str, int]: """Map outcome name → its column index in x.""" return {outcome.name: index for index, outcome in enumerate(self.schema.outcomes)} + def default_observation(self) -> torch.Tensor: + """The default outcome vector to condition a query on: success (1) for every outcome. + + Outcomes are binary (0/1) in the current scope, so the natural default query is + "what produced success?". Asserts the outcomes are binary, so adding a continuous + outcome later fails loudly here instead of silently conditioning on a meaningless value. + """ + is_binary = set(self._x.flatten().tolist()).issubset({0.0, 1.0}) + assert is_binary, "default_observation assumes binary (0/1) outcomes; pass an explicit observation otherwise." + return torch.ones(self._x.shape[1], dtype=torch.float32) + @property def has_categorical_factors(self) -> bool: """True iff the schema declares at least one categorical factor.""" diff --git a/isaaclab_arena/analysis/sensitivity/generate_report.py b/isaaclab_arena/analysis/sensitivity/generate_report.py index 135019ddcf..ae35f13e87 100644 --- a/isaaclab_arena/analysis/sensitivity/generate_report.py +++ b/isaaclab_arena/analysis/sensitivity/generate_report.py @@ -41,7 +41,7 @@ def generate_report( analyzer.fit() observation_tensor = ( - analyzer.default_observation() if observation is None else torch.tensor(observation, dtype=torch.float32) + dataset.default_observation() if observation is None else torch.tensor(observation, dtype=torch.float32) ) samples = analyzer.sample_posterior(observation_tensor) output_path = Path(output_path) diff --git a/isaaclab_arena/analysis/sensitivity/synthetic.py b/isaaclab_arena/analysis/sensitivity/synthetic.py index 09ba2c6e75..149e59e87f 100644 --- a/isaaclab_arena/analysis/sensitivity/synthetic.py +++ b/isaaclab_arena/analysis/sensitivity/synthetic.py @@ -212,7 +212,7 @@ def _demo(): dataset = builder(seed=args.seed, num_episodes=args.num_episodes) analyzer = SensitivityAnalyzer(dataset) analyzer.fit() - observation = analyzer.default_observation() + observation = dataset.default_observation() samples = analyzer.sample_posterior(observation) plot_marginals(samples, dataset, observation, output_path=args.output) print(f"[INFO] Wrote synthetic sensitivity report → {args.output}") From 1dac0e3c46faba8650207c848bfdde90beababbf Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 15:05:31 +0200 Subject: [PATCH 50/74] Remove sensitivity sweep eval configs from the MVP PR Parked on cvolk/feature/sensitivity_eval_configs_parked; they are experiment-specific inputs, not part of the toolbox. Signed-off-by: Clemens Volk --- .../light_intensity_sweep_factors.yaml | 36 ---- .../light_intensity_sweep_jobs_config.json | 184 ------------------ ...t_intensity_sweep_minimal_jobs_config.json | 64 ------ 3 files changed, 284 deletions(-) delete mode 100644 isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_factors.yaml delete mode 100644 isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_jobs_config.json delete mode 100644 isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_minimal_jobs_config.json diff --git a/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_factors.yaml b/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_factors.yaml deleted file mode 100644 index 9f9fca1e15..0000000000 --- a/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_factors.yaml +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -# Sensitivity-analysis schema for the light_intensity sweep on droid + pi0. -# Paired with: light_intensity_sweep_jobs_config.json (and the minimal variant). -# Hand-authored — the factor names here must match keys in the eval's arena_env_args. -# -# - slice identifies the (policy, task, embodiment) the dataset comes from; MNPE/NPE -# assumes a single data-generating source per analysis. -# - factors declares what the eval varies; the eval writer logs the full arena_env_args -# per episode (--episode_summary) and the analyzer treats these keys as factors. -# - outcomes declares what the eval measures; the writer pulls these from the -# registered task metrics (compute_metric_from_recording on each demo). - -slice: - policy: pi0_remote - task: pick_and_place_maple_table - embodiment: droid_abs_joint_pos - -factors: - light_intensity: - type: continuous - dim: 1 - # Mirrors the robolab evaluated endpoints [10, 5000] for direct comparison; spans the - # dark / normal / bright regimes around the policy's trained operating point (~500). - range: [[10, 5000]] - -outcomes: - success_rate: - # Per-episode value of SuccessRateMetric. Returns 0.0 or 1.0 for a single demo. - type: float - object_moved_rate: - # Per-episode value of ObjectMovedRateMetric. Same shape as success_rate. - type: float diff --git a/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_jobs_config.json b/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_jobs_config.json deleted file mode 100644 index 6da57f7199..0000000000 --- a/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_jobs_config.json +++ /dev/null @@ -1,184 +0,0 @@ -{ - "jobs": [ - { - "name": "light_intensity_sweep_10", - "arena_env_args": { - "enable_cameras": true, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 10 - }, - "num_episodes": 20, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "light_intensity_sweep_25", - "arena_env_args": { - "enable_cameras": true, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 25 - }, - "num_episodes": 20, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "light_intensity_sweep_60", - "arena_env_args": { - "enable_cameras": true, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 60 - }, - "num_episodes": 20, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "light_intensity_sweep_150", - "arena_env_args": { - "enable_cameras": true, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 150 - }, - "num_episodes": 20, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "light_intensity_sweep_350", - "arena_env_args": { - "enable_cameras": true, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 350 - }, - "num_episodes": 20, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "light_intensity_sweep_800", - "arena_env_args": { - "enable_cameras": true, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 800 - }, - "num_episodes": 20, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "light_intensity_sweep_1800", - "arena_env_args": { - "enable_cameras": true, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 1800 - }, - "num_episodes": 20, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "light_intensity_sweep_4000", - "arena_env_args": { - "enable_cameras": true, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 4000 - }, - "num_episodes": 20, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "light_intensity_sweep_5000", - "arena_env_args": { - "enable_cameras": true, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 5000 - }, - "num_episodes": 20, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - } - ] -} diff --git a/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_minimal_jobs_config.json b/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_minimal_jobs_config.json deleted file mode 100644 index 0e6f1d3ce0..0000000000 --- a/isaaclab_arena_environments/eval_jobs_configs/light_intensity_sweep_minimal_jobs_config.json +++ /dev/null @@ -1,64 +0,0 @@ -{ - "jobs": [ - { - "name": "light_intensity_minimal_100", - "arena_env_args": { - "enable_cameras": true, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 100 - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "light_intensity_minimal_500", - "arena_env_args": { - "enable_cameras": true, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 500 - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - }, - { - "name": "light_intensity_minimal_5000", - "arena_env_args": { - "enable_cameras": true, - "environment": "pick_and_place_maple_table", - "embodiment": "droid_abs_joint_pos", - "hdr": "billiard_hall_robolab", - "light_intensity": 5000 - }, - "num_episodes": 2, - "language_instruction": "Pick up the Rubik's cube and place it in the bowl.", - "policy_type": "isaaclab_arena_openpi.policy.pi0_remote_policy.Pi0RemotePolicy", - "policy_config_dict": { - "policy_variant": "pi05", - "policy_device": "cuda:0", - "remote_host": "127.0.0.1", - "remote_port": 8000, - "openpi_embodiment_adapter": "droid" - } - } - ] -} From e41e3750d40e1a12b0e4922d36db6eb2c79d61ad Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 15:28:57 +0200 Subject: [PATCH 51/74] Drop the slice block from factors.yaml; TODO a data filter MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The slice (policy/task/embodiment) was a required, hand-authored block used only as a report-title caption — never validated against the rows or used in inference. Remove SliceSpec, the slice schema field/validation, and the title line. factors.yaml now declares just factors + outcomes. Left a TODO for a robolab-style filter (select one policy/task/embodiment slice from a larger episode_summary.jsonl) — the operation the slice block was gesturing at without performing. Signed-off-by: Clemens Volk --- .../policy/concept_sensitivity_analysis.rst | 12 +++--- .../analysis/sensitivity/dataset.py | 40 +++++-------------- .../analysis/sensitivity/plotting.py | 6 +-- .../analysis/sensitivity/synthetic.py | 10 +---- 4 files changed, 17 insertions(+), 51 deletions(-) diff --git a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst index 9100b9e700..4486cf3cf9 100644 --- a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst +++ b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst @@ -45,16 +45,10 @@ neural posterior estimators. The flow is: Inputs ------ -**factors.yaml** declares the slice the data came from, the factors to study, and the -outcomes: +**factors.yaml** declares the factors to study and the outcomes to condition on: .. code-block:: yaml - slice: - policy: pi0 - task: PickUpObject - embodiment: droid - factors: light_intensity: type: continuous @@ -157,3 +151,7 @@ Current scope - Continuous **vector** factors (``dim > 1``) are reserved for a future extension. - The estimators run on CPU and do not require Isaac Sim, so a report can be generated anywhere the evaluation JSONL is available. +- The analysis assumes the ``episode_summary.jsonl`` is a single coherent slice — one + policy, task, and embodiment. **TODO:** add a filter (in the spirit of robolab's + ``--filter-policy`` / ``--filter-task``) to select that slice from a larger JSONL, + rather than relying on the caller to pre-filter it. diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py index cbbaf73e74..73dc5a0d5f 100644 --- a/isaaclab_arena/analysis/sensitivity/dataset.py +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -38,24 +38,10 @@ class OutcomeSpec: type: str # "bool", "float", "int" — informational; loader treats all as float -@dataclass -class SliceSpec: - """Where a dataset came from: which policy ran which task on which embodiment. - - Read from the slice: block of factors.yaml and shown in the report title. - It just labels the data — nothing checks it against the rows. - """ - - policy: str - task: str - embodiment: str - - @dataclass class FactorSchema: - """Parsed factors.yaml — slice + factor list + outcome list.""" + """Parsed factors.yaml — factor list + outcome list.""" - slice: SliceSpec factors: list[FactorSpec] outcomes: list[OutcomeSpec] @@ -63,27 +49,19 @@ class FactorSchema: def from_yaml(cls, path: str | Path) -> FactorSchema: """Load a factors.yaml from disk into a typed FactorSchema. - The YAML must have three top-level blocks: slice (policy/task/embodiment), - factors (one entry per varied input), and outcomes (one entry per - measured output). Each factor's type must be continuous or categorical. + The YAML must have two top-level blocks: factors (one entry per varied input) and + outcomes (one entry per measured output). Each factor's type must be continuous or + categorical. """ + # TODO: add a robolab-style filter (e.g. select rows by policy/task/embodiment) so a + # single episode_summary.jsonl can be sliced to one coherent (policy, task, embodiment) + # before analysis, instead of assuming the caller pre-filtered it. with open(path, encoding="utf-8") as yaml_file: yaml_data = yaml.safe_load(yaml_file) assert isinstance(yaml_data, dict), f"factors.yaml at {path} must be a mapping at top level" - for required_key in ("slice", "factors", "outcomes"): + for required_key in ("factors", "outcomes"): assert required_key in yaml_data, f"factors.yaml at {path} is missing top-level `{required_key}:` block" - slice_block = yaml_data["slice"] - for required_key in ("policy", "task", "embodiment"): - assert ( - required_key in slice_block - ), f"factors.yaml at {path} `slice:` block is missing `{required_key}` (need policy/task/embodiment)" - slice_spec = SliceSpec( - policy=slice_block["policy"], - task=slice_block["task"], - embodiment=slice_block["embodiment"], - ) - factors: list[FactorSpec] = [] for factor_name, factor_block in yaml_data["factors"].items(): assert "type" in factor_block, ( @@ -110,7 +88,7 @@ def from_yaml(cls, path: str | Path) -> FactorSchema: for outcome_name, outcome_block in yaml_data["outcomes"].items() ] - return cls(slice=slice_spec, factors=factors, outcomes=outcomes) + return cls(factors=factors, outcomes=outcomes) @property def total_factor_dim(self) -> int: diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index 80b8a08798..767dec6df6 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -63,17 +63,15 @@ def plot_marginals( for unused_index in range(len(factors), len(flat_axes)): flat_axes[unused_index].axis("off") - slice_info = dataset.schema.slice observation_label = ", ".join( f"{outcome.name}={value:g}" for outcome, value in zip(dataset.schema.outcomes, observation.tolist()) ) figure.suptitle( - f"Posterior marginals — {dataset.num_episodes} episodes (observed: {observation_label})\n" - f"{slice_info.policy} / {slice_info.task} / {slice_info.embodiment}", + f"Posterior marginals — {dataset.num_episodes} episodes (observed: {observation_label})", fontsize=12, fontweight="bold", ) - figure.tight_layout(rect=[0, 0, 1, 0.92]) + figure.tight_layout(rect=[0, 0, 1, 0.95]) if output_path is not None: Path(output_path).parent.mkdir(parents=True, exist_ok=True) diff --git a/isaaclab_arena/analysis/sensitivity/synthetic.py b/isaaclab_arena/analysis/sensitivity/synthetic.py index 149e59e87f..d225c84120 100644 --- a/isaaclab_arena/analysis/sensitivity/synthetic.py +++ b/isaaclab_arena/analysis/sensitivity/synthetic.py @@ -28,13 +28,7 @@ from dataclasses import dataclass from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer -from isaaclab_arena.analysis.sensitivity.dataset import ( - FactorSchema, - FactorSpec, - OutcomeSpec, - SensitivityDataset, - SliceSpec, -) +from isaaclab_arena.analysis.sensitivity.dataset import FactorSchema, FactorSpec, OutcomeSpec, SensitivityDataset from isaaclab_arena.analysis.sensitivity.plotting import plot_marginals @@ -96,7 +90,6 @@ def column(self, codes: torch.Tensor) -> torch.Tensor: OBJECT_TYPE = _CategoricalFactor("object_type", {"cube": 1.2, "can": 0.0, "mug": -1.2}) _OUTCOME_NAME = "success" -_SLICE = SliceSpec(policy="synthetic", task="SyntheticTask", embodiment="synthetic") def _sample_success(success_logit: torch.Tensor) -> torch.Tensor: @@ -115,7 +108,6 @@ def _build_dataset( """ ordered = sorted(factors_and_columns, key=lambda pair: isinstance(pair[0], _CategoricalFactor)) schema = FactorSchema( - slice=_SLICE, factors=[factor.spec() for factor, _ in ordered], outcomes=[OutcomeSpec(name=_OUTCOME_NAME, type="bool")], ) From 57e7f2406057a6721762f5982febbb467447aef6 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 15:42:42 +0200 Subject: [PATCH 52/74] Outcomes are an analysis-time query, not part of factors.yaml MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Factors carry real schema (continuous/categorical, range/choices) needed to rebuild theta and the prior, so they stay in factors.yaml. Outcomes carry no structure — always read as float, just a name — and *which* outcome to condition on is a query, like --observation. So: - drop the outcomes block from factors.yaml and the OutcomeSpec class (the type field was dead — the loader treated every outcome as float) - the outcome name is now a generate_report --outcome flag (default: success); the dataset carries outcome_names (default ('success',)) for x-column labels - remove the now-dead outcome_columns property - docs corrected: factors.yaml declares only factors; outcome is chosen at analysis time Signed-off-by: Clemens Volk --- .../policy/concept_sensitivity_analysis.rst | 26 ++--- .../analysis/sensitivity/dataset.py | 94 +++++++++---------- .../analysis/sensitivity/generate_report.py | 23 +++-- .../analysis/sensitivity/plotting.py | 2 +- .../analysis/sensitivity/synthetic.py | 10 +- 5 files changed, 81 insertions(+), 74 deletions(-) diff --git a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst index 4486cf3cf9..c254b9d08c 100644 --- a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst +++ b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst @@ -34,8 +34,10 @@ neural posterior estimators. The flow is: 1. **Per-episode recording.** During evaluation, ``episode_writer`` appends one row per episode to an ``episode_summary.jsonl`` file. -2. **Schema.** A ``factors.yaml`` declares which of those columns are *factors* to analyse - (and whether each is continuous or categorical) and which are *outcomes* to condition on. +2. **Schema.** A ``factors.yaml`` declares the *factors* — which ``arena_env_args`` columns + were varied and whether each is continuous or categorical, plus the continuous ranges + that were swept (so the analyzer's prior matches the simulation). It does **not** list + outcomes — *which* outcome to condition on is chosen at analysis time, not saved here. 3. **Inference.** ``SensitivityAnalyzer`` loads the pair, trains an estimator on the full ``(theta, x)`` jointly, and samples the joint posterior conditioned on a chosen observation (by default, success). @@ -45,23 +47,21 @@ neural posterior estimators. The flow is: Inputs ------ -**factors.yaml** declares the factors to study and the outcomes to condition on: +**factors.yaml** declares only the factors that were varied (and the continuous ranges that +were swept). Outcomes are not declared here — they're selected at analysis time (see below): .. code-block:: yaml factors: light_intensity: type: continuous - range: [[0.0, 5000.0]] # one [low, high] pair; inferred from data if omitted + range: [[0.0, 5000.0]] # the swept range; inferred from the data's min/max if omitted table_material: type: categorical choices: [oak, walnut, bamboo] - outcomes: - success: - type: bool - -**episode_summary.jsonl** is produced by the eval runner — one JSON object per episode: +**episode_summary.jsonl** is produced by the eval runner — one JSON object per episode. It +carries every measured outcome; the analysis picks which one(s) to condition on: .. code-block:: json @@ -106,11 +106,13 @@ format follows the file extension (``.png``, ``.pdf``, …); reports are written python -m isaaclab_arena.analysis.sensitivity.generate_report \ --factors_yaml factors.yaml \ --episode_summary episode_summary.jsonl \ + --outcome success \ --output eval/sensitivity_report.png -Pass ``--observation`` to condition on specific outcome values (one per declared outcome, -in schema order); since outcomes are binary, use ``1`` for success or ``0`` for failure. -It defaults to ``1`` (success). +``--outcome`` selects which per-episode outcome(s) to condition on (keys in the rows' +``outcomes`` block); it defaults to ``success``. Pass ``--observation`` to set the value +per outcome — since outcomes are binary, use ``1`` for success or ``0`` for failure; it +defaults to ``1`` (success). Trying it on synthetic data --------------------------- diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py index 73dc5a0d5f..924117ba4f 100644 --- a/isaaclab_arena/analysis/sensitivity/dataset.py +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -30,28 +30,23 @@ class FactorSpec: choices: list[str] | None = None # categorical only -@dataclass -class OutcomeSpec: - """One outcome's schema (just a name and a type hint; the loader treats all as float).""" - - name: str - type: str # "bool", "float", "int" — informational; loader treats all as float - - @dataclass class FactorSchema: - """Parsed factors.yaml — factor list + outcome list.""" + """Parsed factors.yaml — the list of factors that were varied. + + Factors carry structure (continuous vs categorical, range/choices) needed to build theta. + Outcomes are not part of the schema: they are always read as floats and *which* outcome to + condition on is a query, chosen at analysis time (see SensitivityDataset.from_files). + """ factors: list[FactorSpec] - outcomes: list[OutcomeSpec] @classmethod def from_yaml(cls, path: str | Path) -> FactorSchema: """Load a factors.yaml from disk into a typed FactorSchema. - The YAML must have two top-level blocks: factors (one entry per varied input) and - outcomes (one entry per measured output). Each factor's type must be continuous or - categorical. + The YAML has one top-level block, factors (one entry per varied input). Each factor's + type must be continuous or categorical. """ # TODO: add a robolab-style filter (e.g. select rows by policy/task/embodiment) so a # single episode_summary.jsonl can be sliced to one coherent (policy, task, embodiment) @@ -59,8 +54,7 @@ def from_yaml(cls, path: str | Path) -> FactorSchema: with open(path, encoding="utf-8") as yaml_file: yaml_data = yaml.safe_load(yaml_file) assert isinstance(yaml_data, dict), f"factors.yaml at {path} must be a mapping at top level" - for required_key in ("factors", "outcomes"): - assert required_key in yaml_data, f"factors.yaml at {path} is missing top-level `{required_key}:` block" + assert "factors" in yaml_data, f"factors.yaml at {path} is missing top-level `factors:` block" factors: list[FactorSpec] = [] for factor_name, factor_block in yaml_data["factors"].items(): @@ -83,12 +77,7 @@ def from_yaml(cls, path: str | Path) -> FactorSchema: ) ) - outcomes = [ - OutcomeSpec(name=outcome_name, type=outcome_block.get("type", "float")) - for outcome_name, outcome_block in yaml_data["outcomes"].items() - ] - - return cls(factors=factors, outcomes=outcomes) + return cls(factors=factors) @property def total_factor_dim(self) -> int: @@ -127,14 +116,21 @@ class SensitivityDataset: describes: continuous columns first, then one integer-coded column per categorical. """ - def __init__(self, schema: FactorSchema, theta: torch.Tensor, x: torch.Tensor): + def __init__( + self, + schema: FactorSchema, + theta: torch.Tensor, + x: torch.Tensor, + outcome_names: list[str] | tuple[str, ...] = ("success",), + ): """Wrap an in-memory schema plus its theta / x tensors, validating shapes. Args: - schema: The parsed factor/outcome schema. Continuous factors must carry a - range; categorical factors must carry choices. + schema: The parsed factor schema. Continuous factors must carry a range; + categorical factors must carry choices. theta: (num_episodes, total_factor_dim) factor matrix, continuous-first. x: (num_episodes, num_outcomes) outcome matrix. + outcome_names: Name of each outcome column in x, in order (used for plot labels). """ assert theta.ndim == 2 and x.ndim == 2, f"theta and x must be 2D; got {theta.shape} and {x.shape}" assert theta.shape[0] == x.shape[0], f"theta/x row counts disagree: {theta.shape[0]} vs {x.shape[0]}" @@ -143,18 +139,25 @@ def __init__(self, schema: FactorSchema, theta: torch.Tensor, x: torch.Tensor): theta.shape[1] == schema.total_factor_dim ), f"theta has {theta.shape[1]} columns but schema declares {schema.total_factor_dim} factor dims" assert x.shape[1] == len( - schema.outcomes - ), f"x has {x.shape[1]} columns but schema declares {len(schema.outcomes)} outcomes" + outcome_names + ), f"x has {x.shape[1]} columns but {len(outcome_names)} outcome name(s) were given" self.schema = schema + self.outcome_names = list(outcome_names) self._theta = theta self._x = x @classmethod - def from_files(cls, factors_yaml: str | Path, jsonl_path: str | Path) -> SensitivityDataset: + def from_files( + cls, + factors_yaml: str | Path, + jsonl_path: str | Path, + outcome_names: list[str] | tuple[str, ...] = ("success",), + ) -> SensitivityDataset: """Build a dataset from a factors.yaml schema and an episode_summary.jsonl. Parses and validates both, infers any missing continuous range from the data, and - assembles the theta / x tensors in the layout the analyzers expect. + assembles the theta / x tensors in the layout the analyzers expect. ``outcome_names`` + selects which per-episode outcome columns to condition on (the analysis-time query). """ schema = FactorSchema.from_yaml(factors_yaml) @@ -162,12 +165,12 @@ def from_files(cls, factors_yaml: str | Path, jsonl_path: str | Path) -> Sensiti rows = [json.loads(line) for line in jsonl_text.splitlines() if line.strip()] assert len(rows) > 0, f"Empty episode_summary.jsonl at {jsonl_path}" - _validate_rows(schema, rows, jsonl_path) + _validate_rows(schema, rows, outcome_names, jsonl_path) _infer_missing_factor_ranges(schema, rows) theta = _build_factor_tensor(schema, rows) - x = _build_outcome_tensor(schema, rows) - return cls(schema, theta, x) + x = _build_outcome_tensor(rows, outcome_names) + return cls(schema, theta, x, outcome_names) @property def theta(self) -> torch.Tensor: @@ -182,9 +185,8 @@ def theta(self) -> torch.Tensor: def x(self) -> torch.Tensor: """(num_episodes, num_outcomes) matrix of outcome values, one row per episode. - This is what the analyzer conditions queries on. The analyzer typically selects a - single outcome column at fit time (e.g. success_rate) and asks - "what theta values were consistent with observing this outcome?" + This is what the analyzer conditions queries on — "what factor values were consistent + with observing these outcomes?". Columns are named by ``outcome_names``. """ return self._x @@ -198,11 +200,6 @@ def factor_columns(self) -> dict[str, slice]: """Map factor name → its column slice in theta. Same as schema.factor_columns.""" return self.schema.factor_columns - @property - def outcome_columns(self) -> dict[str, int]: - """Map outcome name → its column index in x.""" - return {outcome.name: index for index, outcome in enumerate(self.schema.outcomes)} - def default_observation(self) -> torch.Tensor: """The default outcome vector to condition a query on: success (1) for every outcome. @@ -254,14 +251,16 @@ def prior(self): ) -def _validate_rows(schema: FactorSchema, rows: list[dict], jsonl_path: str | Path) -> None: - """Assert every JSONL row carries the schema's declared factor and outcome keys. +def _validate_rows( + schema: FactorSchema, rows: list[dict], outcome_names: list[str] | tuple[str, ...], jsonl_path: str | Path +) -> None: + """Assert every JSONL row carries the declared factor keys and the requested outcome keys. The declared names need only be a subset of each row's arena_env_args / outcomes; extra keys are ignored. Raises pointing at the first offending row. """ expected_factor_names = {factor.name for factor in schema.factors} - expected_outcome_names = {outcome.name for outcome in schema.outcomes} + expected_outcome_names = set(outcome_names) for row_index, row in enumerate(rows): assert ( "arena_env_args" in row and "outcomes" in row @@ -338,14 +337,13 @@ def _build_factor_tensor(schema: FactorSchema, rows: list[dict]) -> torch.Tensor return torch.zeros((len(rows), 0), dtype=torch.float32) -def _build_outcome_tensor(schema: FactorSchema, rows: list[dict]) -> torch.Tensor: - """Assemble the per-episode outcome matrix x (one column per declared outcome). +def _build_outcome_tensor(rows: list[dict], outcome_names: list[str] | tuple[str, ...]) -> torch.Tensor: + """Assemble the per-episode outcome matrix x (one column per requested outcome). - Each outcome value is cast to float; bool outcomes become 0.0/1.0. The analyzer usually - selects a single outcome column at fit time and conditions queries on it. + Each outcome value is cast to float; bool outcomes become 0.0/1.0. """ outcome_column_tensors = [ - torch.tensor([float(row["outcomes"][outcome.name]) for row in rows], dtype=torch.float32).unsqueeze(1) - for outcome in schema.outcomes + torch.tensor([float(row["outcomes"][name]) for row in rows], dtype=torch.float32).unsqueeze(1) + for name in outcome_names ] return torch.cat(outcome_column_tensors, dim=1) diff --git a/isaaclab_arena/analysis/sensitivity/generate_report.py b/isaaclab_arena/analysis/sensitivity/generate_report.py index ae35f13e87..8063071ec2 100644 --- a/isaaclab_arena/analysis/sensitivity/generate_report.py +++ b/isaaclab_arena/analysis/sensitivity/generate_report.py @@ -19,6 +19,7 @@ def generate_report( factors_yaml_path: str | Path, jsonl_path: str | Path, output_path: str | Path, + outcome_names: list[str] | tuple[str, ...] = ("success",), observation: list[float] | None = None, ) -> Path: """Build a sensitivity report from a factors.yaml / episode_summary.jsonl pair. @@ -27,16 +28,17 @@ def generate_report( figure. The output format follows the output_path extension (.png, .pdf, …). Args: - factors_yaml_path: Schema file declaring factors and outcomes. + factors_yaml_path: Schema file declaring the factors. jsonl_path: episode_summary.jsonl produced by eval_runner. output_path: Destination figure file (parent dirs created if absent). - observation: Outcome values to condition on, one per declared outcome. Defaults to - conditioning on success (1) for every binary outcome. + outcome_names: Which per-episode outcome(s) to condition on. + observation: Outcome values to condition on, one per outcome name. Defaults to + conditioning on success (1) for every (binary) outcome. Returns: The resolved output path. """ - dataset = SensitivityDataset.from_files(Path(factors_yaml_path), Path(jsonl_path)) + dataset = SensitivityDataset.from_files(Path(factors_yaml_path), Path(jsonl_path), outcome_names) analyzer = SensitivityAnalyzer(dataset) analyzer.fit() @@ -68,19 +70,28 @@ def main(): default="eval/sensitivity_report.png", help="Output figure file; format follows the extension (.png, .pdf, …). Default: eval/sensitivity_report.png.", ) + parser.add_argument( + "--outcome", + type=str, + nargs="+", + default=["success"], + help="Which per-episode outcome(s) to condition on (keys in the rows' outcomes block). Default: success.", + ) parser.add_argument( "--observation", type=float, nargs="*", default=None, help=( - "Outcome values to condition on, one per declared outcome (in schema order). " + "Outcome values to condition on, one per --outcome (in order). " "Outcomes are binary, so use 1 for success or 0 for failure. Defaults to 1 (success)." ), ) args = parser.parse_args() - generate_report(args.factors_yaml, args.episode_summary, args.output, observation=args.observation) + generate_report( + args.factors_yaml, args.episode_summary, args.output, outcome_names=args.outcome, observation=args.observation + ) if __name__ == "__main__": diff --git a/isaaclab_arena/analysis/sensitivity/plotting.py b/isaaclab_arena/analysis/sensitivity/plotting.py index 767dec6df6..73a4961e7b 100644 --- a/isaaclab_arena/analysis/sensitivity/plotting.py +++ b/isaaclab_arena/analysis/sensitivity/plotting.py @@ -64,7 +64,7 @@ def plot_marginals( flat_axes[unused_index].axis("off") observation_label = ", ".join( - f"{outcome.name}={value:g}" for outcome, value in zip(dataset.schema.outcomes, observation.tolist()) + f"{name}={value:g}" for name, value in zip(dataset.outcome_names, observation.tolist()) ) figure.suptitle( f"Posterior marginals — {dataset.num_episodes} episodes (observed: {observation_label})", diff --git a/isaaclab_arena/analysis/sensitivity/synthetic.py b/isaaclab_arena/analysis/sensitivity/synthetic.py index d225c84120..b72fe9d271 100644 --- a/isaaclab_arena/analysis/sensitivity/synthetic.py +++ b/isaaclab_arena/analysis/sensitivity/synthetic.py @@ -28,7 +28,7 @@ from dataclasses import dataclass from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer -from isaaclab_arena.analysis.sensitivity.dataset import FactorSchema, FactorSpec, OutcomeSpec, SensitivityDataset +from isaaclab_arena.analysis.sensitivity.dataset import FactorSchema, FactorSpec, SensitivityDataset from isaaclab_arena.analysis.sensitivity.plotting import plot_marginals @@ -89,8 +89,6 @@ def column(self, codes: torch.Tensor) -> torch.Tensor: MATERIAL = _CategoricalFactor("table_material", {"oak": 1.5, "walnut": 0.0, "bamboo": -1.5}) OBJECT_TYPE = _CategoricalFactor("object_type", {"cube": 1.2, "can": 0.0, "mug": -1.2}) -_OUTCOME_NAME = "success" - def _sample_success(success_logit: torch.Tensor) -> torch.Tensor: """Draw a binary success outcome per episode from Bernoulli(sigmoid(logit)).""" @@ -107,11 +105,9 @@ def _build_dataset( SensitivityDataset.factor_columns expects. """ ordered = sorted(factors_and_columns, key=lambda pair: isinstance(pair[0], _CategoricalFactor)) - schema = FactorSchema( - factors=[factor.spec() for factor, _ in ordered], - outcomes=[OutcomeSpec(name=_OUTCOME_NAME, type="bool")], - ) + schema = FactorSchema(factors=[factor.spec() for factor, _ in ordered]) theta = torch.stack([factor.column(values) for factor, values in ordered], dim=1) + # outcome_names defaults to ("success",), matching the single binary outcome built here. return SensitivityDataset(schema, theta, success.unsqueeze(1)) From 2ee03e444cc077e6d1a9280ce48048c9fd5ecf76 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 15:44:45 +0200 Subject: [PATCH 53/74] Revert .gitignore change Drop the /eval/ ignore added earlier; leave .gitignore as on main. Signed-off-by: Clemens Volk --- .gitignore | 2 -- 1 file changed, 2 deletions(-) diff --git a/.gitignore b/.gitignore index 7dba264430..b3a54b0034 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,5 @@ **/build -# Eval outputs (generated sensitivity reports, etc.) -/eval/ # Prerequisites *.d From 6075a551275fb4260ca43e791de54dc274ae3a3a Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 15:57:23 +0200 Subject: [PATCH 54/74] Move per-episode recording to a follow-up PR MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Keep #729 focused on the analysis toolbox (analysis/sensitivity + tests + docs), which is CPU-only and validated on synthetic data. The eval-side recording that produces episode_summary.jsonl — episode_writer.py plus the eval_runner/CLI/job_manager/metrics hooks — moves to a stacked follow-up PR where it can land with proper simulation tests. Signed-off-by: Clemens Volk --- isaaclab_arena/evaluation/episode_writer.py | 62 -------------------- isaaclab_arena/evaluation/eval_runner.py | 14 ----- isaaclab_arena/evaluation/eval_runner_cli.py | 9 --- isaaclab_arena/evaluation/job_manager.py | 6 -- isaaclab_arena/metrics/metrics_manager.py | 30 ---------- 5 files changed, 121 deletions(-) delete mode 100644 isaaclab_arena/evaluation/episode_writer.py diff --git a/isaaclab_arena/evaluation/episode_writer.py b/isaaclab_arena/evaluation/episode_writer.py deleted file mode 100644 index 889f82b1b4..0000000000 --- a/isaaclab_arena/evaluation/episode_writer.py +++ /dev/null @@ -1,62 +0,0 @@ -# Copyright (c) 2026, The Isaac Lab Arena Project Developers (https://github.com/isaac-sim/IsaacLab-Arena/blob/main/CONTRIBUTORS.md). -# All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 - -from __future__ import annotations - -import json -from pathlib import Path -from typing import TYPE_CHECKING - -from isaaclab_arena.metrics.metrics_logger import metrics_to_plain_python_types - -if TYPE_CHECKING: - from isaaclab_arena.evaluation.job_manager import Job - - -def write_episode_summaries(env, job: Job, output_path: str | Path) -> int: - """Append one JSONL row per recorded episode for the just-completed job. - - Each row has shape:: - - { - "job_name": "", - "episode_idx": , - "arena_env_args": , - "outcomes": - } - - Per-episode metric values come from the env's ``MetricsManager`` (the same machinery - that backs ``compute_metrics``), so all HDF5/metric access stays in the metrics layer. - - Args: - env: The (possibly gym-wrapped) Arena env that just finished its rollout. Its - ``MetricsManager`` provides the per-episode metric values. - job: The Job that ran. Its ``arena_env_args_dict`` is logged verbatim under - ``arena_env_args``. - output_path: JSONL file to append to. Created (with parent dirs) if absent. - - Returns: - Number of rows written. - """ - unwrapped_env = env.unwrapped - if not hasattr(unwrapped_env.cfg, "metrics") or unwrapped_env.cfg.metrics is None: - return 0 - - per_episode_metrics = unwrapped_env.metrics_manager.compute_per_episode() - arena_env_args_snapshot = dict(job.arena_env_args_dict) - - output_path = Path(output_path) - output_path.parent.mkdir(parents=True, exist_ok=True) - with open(output_path, "a", encoding="utf-8") as jsonl_output: - for episode_idx, episode_metrics in enumerate(per_episode_metrics): - summary_row = { - "job_name": job.name, - "episode_idx": episode_idx, - "arena_env_args": arena_env_args_snapshot, - "outcomes": metrics_to_plain_python_types(episode_metrics), - } - jsonl_output.write(json.dumps(summary_row) + "\n") - - return len(per_episode_metrics) diff --git a/isaaclab_arena/evaluation/eval_runner.py b/isaaclab_arena/evaluation/eval_runner.py index ee440f3d91..2359a9ee8b 100644 --- a/isaaclab_arena/evaluation/eval_runner.py +++ b/isaaclab_arena/evaluation/eval_runner.py @@ -19,7 +19,6 @@ from typing import TYPE_CHECKING from isaaclab_arena.cli.isaaclab_arena_cli import get_isaaclab_arena_cli_parser -from isaaclab_arena.evaluation.episode_writer import write_episode_summaries from isaaclab_arena.evaluation.eval_runner_cli import add_eval_runner_arguments from isaaclab_arena.evaluation.job_manager import Job, JobManager, Status from isaaclab_arena.evaluation.policy_runner import get_policy_cls, rollout_policy @@ -201,15 +200,6 @@ def main(): # Check if any job requires cameras and enable them if needed before starting simulation enable_cameras_if_required(eval_jobs_config, args_cli) - # --episode_summary (opt-in): the writer logs the full arena_env_args per episode; - # the analyzer's factors.yaml decides which keys are factors (no eval-side knowledge). - episode_summary_enabled = args_cli.episode_summary is not None - if episode_summary_enabled: - print( - "[INFO] Episode summary recording enabled. Per-episode arena_env_args + outcomes" - f" → {args_cli.episode_summary}" - ) - with SimulationAppContext(args_cli): job_manager = JobManager(eval_jobs_config["jobs"]) metrics_logger = MetricsLogger() @@ -260,10 +250,6 @@ def main(): language_instruction=job.language_instruction, ) - if episode_summary_enabled: - rows = write_episode_summaries(env, job, args_cli.episode_summary) - print(f"[INFO] Wrote {rows} episode summaries for job '{job.name}'") - job_manager.complete_job(job, metrics=metrics, status=Status.COMPLETED) # users may not specify metrics for a task, although it's not recommended diff --git a/isaaclab_arena/evaluation/eval_runner_cli.py b/isaaclab_arena/evaluation/eval_runner_cli.py index 3f95c04fd9..15e6131b42 100644 --- a/isaaclab_arena/evaluation/eval_runner_cli.py +++ b/isaaclab_arena/evaluation/eval_runner_cli.py @@ -38,12 +38,3 @@ def add_eval_runner_arguments(parser: argparse.ArgumentParser) -> None: " set only if a long sweep grows in host memory or gets OOM-killed." ), ) - parser.add_argument( - "--episode_summary", - type=str, - default=None, - help=( - "Append one JSONL row per recorded episode (arena_env_args + outcomes) to" - " this file. Consumed by the sensitivity analyzer. Default unset — no recording." - ), - ) diff --git a/isaaclab_arena/evaluation/job_manager.py b/isaaclab_arena/evaluation/job_manager.py index 43bbe1ffb4..8c4d09c467 100644 --- a/isaaclab_arena/evaluation/job_manager.py +++ b/isaaclab_arena/evaluation/job_manager.py @@ -28,7 +28,6 @@ def __init__( policy_config_dict: dict = None, status: Status = None, language_instruction: str = None, - arena_env_args_dict: dict | None = None, ): """Initialize a Job instance. @@ -43,13 +42,9 @@ def __init__( status: Job status (defaults to PENDING) language_instruction: Optional language instruction override for the policy. When set, takes precedence over the task's own description. - arena_env_args_dict: The original dict form of arena_env_args before conversion to - CLI args list. Preserves typed values (e.g. floats stay floats) for downstream - consumers that need to index by key. """ self.name = name self.arena_env_args = arena_env_args - self.arena_env_args_dict = arena_env_args_dict if arena_env_args_dict is not None else {} assert num_envs > 0, "num_envs must be greater than 0" assert not ( num_steps is not None and num_episodes is not None @@ -107,7 +102,6 @@ def from_dict(cls, data: dict) -> "Job": return cls( name=data["name"], arena_env_args=cls.convert_args_dict_to_cli_args_list(data["arena_env_args"]), - arena_env_args_dict=data["arena_env_args"], policy_type=data["policy_type"], num_envs=num_envs, num_steps=num_steps, diff --git a/isaaclab_arena/metrics/metrics_manager.py b/isaaclab_arena/metrics/metrics_manager.py index 6a3f9b102b..333207f911 100644 --- a/isaaclab_arena/metrics/metrics_manager.py +++ b/isaaclab_arena/metrics/metrics_manager.py @@ -62,33 +62,3 @@ def compute(self) -> dict[str, Any]: metrics_data[term_name] = term_cfg.compute_metric_func(recorded_metric_data, **term_cfg.params) metrics_data["num_episodes"] = get_num_episodes(dataset_path) return metrics_data - - def compute_per_episode(self) -> list[dict[str, Any]]: - """Compute every registered metric separately for each recorded episode. - - Where :meth:`compute` reduces across all episodes to one aggregate value per - metric, this returns one ``{metric_name: value}`` dict per episode — each metric's - compute func is fed that single episode's recorded array (a one-element list). - - Returns: - A list with one metric dict per episode, in recorded order. - """ - dataset_path = get_metric_recorder_dataset_path(self._env) - num_episodes = get_num_episodes(dataset_path) - - # Recorded data arrives grouped by metric (each term -> one array per episode). - # Read it once here, then transpose into one metric dict per episode below. - episode_arrays_by_term = { - term_name: get_recorded_metric_data(dataset_path, term_cfg.recorder_term_name) - for term_name, term_cfg in zip(self._term_names, self._term_cfgs) - } - - per_episode_metrics: list[dict[str, Any]] = [] - for episode_index in range(num_episodes): - episode_metrics: dict[str, Any] = {} - for term_name, term_cfg in zip(self._term_names, self._term_cfgs): - # compute_metric_func reduces a list of per-episode arrays; give it just this one. - episode_array = episode_arrays_by_term[term_name][episode_index] - episode_metrics[term_name] = term_cfg.compute_metric_func([episode_array], **term_cfg.params) - per_episode_metrics.append(episode_metrics) - return per_episode_metrics From 33b27e539f1ca69843c30cf790bdf7b977dc2b33 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 16:24:29 +0200 Subject: [PATCH 55/74] Move sensitivity deps to runtime (sbi, scipy, matplotlib) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit These are imported at module level by analysis/sensitivity, so they belong in install_requires, not the [dev] extra — otherwise importing the package fails without [dev]. The Docker image already installs via pip install -e .[dev], which pulls install_requires too, so the image is unchanged; [dev] now holds only dev tools. Signed-off-by: Clemens Volk --- setup.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 55a9280ea4..582f669ec2 100644 --- a/setup.py +++ b/setup.py @@ -16,13 +16,16 @@ "pytest", "pydantic>=2.0", "openai>=2.0", + # Sensitivity analysis (isaaclab_arena.analysis.sensitivity), imported at module level. + "sbi", + "scipy", + "matplotlib", ] DEV_DEPS = [ "jupyter", "debugpy", "tenacity", - "sbi", ] setup( From adf430d1bb95c329c3ec3720726a84681370ac6a Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 16:26:33 +0200 Subject: [PATCH 56/74] Require at least one --observation value (nargs=+) nargs='*' let '--observation' be passed with zero values, producing an empty tensor that reaches sbi as a cryptic shape mismatch. nargs='+' makes argparse reject it clearly; omitting the flag still falls back to the default observation (success). Signed-off-by: Clemens Volk --- isaaclab_arena/analysis/sensitivity/generate_report.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/isaaclab_arena/analysis/sensitivity/generate_report.py b/isaaclab_arena/analysis/sensitivity/generate_report.py index 8063071ec2..089b7480fc 100644 --- a/isaaclab_arena/analysis/sensitivity/generate_report.py +++ b/isaaclab_arena/analysis/sensitivity/generate_report.py @@ -80,7 +80,7 @@ def main(): parser.add_argument( "--observation", type=float, - nargs="*", + nargs="+", default=None, help=( "Outcome values to condition on, one per --outcome (in order). " From 88ff6b89490b99d6508d446b168da21ae07a6a65 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 16:45:57 +0200 Subject: [PATCH 57/74] Docs: collapse the synthetic demo to one example Show only the 'rich' run (3 continuous + 2 categorical) and mention the other --kind values in a line, instead of three near-identical commands. Signed-off-by: Clemens Volk --- .../concepts/policy/concept_sensitivity_analysis.rst | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst index c254b9d08c..28ae374b38 100644 --- a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst +++ b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst @@ -123,14 +123,11 @@ without Isaac Sim — useful for seeing the output shape and for validating the .. code-block:: bash - # mixed (MNPE): one continuous + one categorical factor - python -m isaaclab_arena.analysis.sensitivity.synthetic --kind mixed --output eval/demo.png + # rich: three continuous + two categorical factors (MNPE) + python -m isaaclab_arena.analysis.sensitivity.synthetic --kind rich --output eval/demo.png - # continuous (NPE): two continuous factors - python -m isaaclab_arena.analysis.sensitivity.synthetic --kind continuous --output eval/demo.png - - # rich: three continuous + two categorical factors - python -m isaaclab_arena.analysis.sensitivity.synthetic --kind rich --output eval/demo.png +``--kind`` also accepts ``mixed`` (one continuous + one categorical) and ``continuous`` +(two continuous factors, which exercises the NPE path). Reading the output ------------------ From 2b43c8de2c404b342ce49417b42f5fb5058d09bf Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Thu, 11 Jun 2026 16:58:23 +0200 Subject: [PATCH 58/74] Merge the two MNPE synthetic datasets into make_mixed_dataset MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 'rich' wasn't descriptive and duplicated the MNPE case. Fold it into make_mixed_dataset (now 3 continuous + 2 categorical — 'mixed' = mixed factor types) and drop make_rich_dataset. make_continuous_dataset stays for the NPE path. Two builders, one per estimator, both named for what they are. The MNPE test now asserts all five planted effects; --kind is {mixed, continuous}. Signed-off-by: Clemens Volk --- .../policy/concept_sensitivity_analysis.rst | 7 ++- .../analysis/sensitivity/synthetic.py | 32 ++++-------- .../tests/test_sensitivity_analysis.py | 52 ++++++++++++------- 3 files changed, 44 insertions(+), 47 deletions(-) diff --git a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst index 28ae374b38..c4c66641d3 100644 --- a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst +++ b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst @@ -123,11 +123,10 @@ without Isaac Sim — useful for seeing the output shape and for validating the .. code-block:: bash - # rich: three continuous + two categorical factors (MNPE) - python -m isaaclab_arena.analysis.sensitivity.synthetic --kind rich --output eval/demo.png + # mixed: three continuous + two categorical factors (MNPE) + python -m isaaclab_arena.analysis.sensitivity.synthetic --kind mixed --output eval/demo.png -``--kind`` also accepts ``mixed`` (one continuous + one categorical) and ``continuous`` -(two continuous factors, which exercises the NPE path). +``--kind`` also accepts ``continuous`` (continuous-only factors, which exercises the NPE path). Reading the output ------------------ diff --git a/isaaclab_arena/analysis/sensitivity/synthetic.py b/isaaclab_arena/analysis/sensitivity/synthetic.py index b72fe9d271..463337d4d9 100644 --- a/isaaclab_arena/analysis/sensitivity/synthetic.py +++ b/isaaclab_arena/analysis/sensitivity/synthetic.py @@ -125,27 +125,13 @@ def make_continuous_dataset(seed: int, num_episodes: int = 2000) -> SensitivityD return _build_dataset([(LIGHT, light), (GRASP_OFFSET, grasp_offset)], success) -def make_mixed_dataset(seed: int, num_episodes: int = 2000) -> SensitivityDataset: - """Continuous light_intensity + categorical table_material driving success. +def make_mixed_dataset(seed: int, num_episodes: int = 3000) -> SensitivityDataset: + """Mixed continuous + categorical factors driving success (MNPE path). - Uses the MNPE path. Both effects are planted: brighter light and "better" materials - (oak > walnut > bamboo) raise success, so conditioning the posterior on success should - favor high light values and oak. - """ - torch.manual_seed(seed) - light = LIGHT.sample(num_episodes) - material = MATERIAL.sample(num_episodes) - success = _sample_success(LIGHT.logit(light) + MATERIAL.logit(material)) - return _build_dataset([(LIGHT, light), (MATERIAL, material)], success) - - -def make_rich_dataset(seed: int, num_episodes: int = 3000) -> SensitivityDataset: - """A realistic mix — three continuous + two categorical factors — driving success (MNPE). - - Mirrors the kind of data a real sweep produces: several continuous factors on different - scales (light, mass, camera distance) and several categoricals (object type, table material). - Every effect is planted (brighter / lighter / closer / cube / oak raise success), so the - posterior conditioned on success should recover all of them at once. + A realistic multi-factor sweep: three continuous factors on different scales (light, + mass, camera distance) and two categoricals (object type, table material). Every effect + is planted (brighter / lighter / closer / cube / oak raise success), so the posterior + conditioned on success should recover all of them at once. """ torch.manual_seed(seed) light = LIGHT.sample(num_episodes) @@ -183,9 +169,9 @@ def _demo(): parser = argparse.ArgumentParser(description="Run the sensitivity pipeline on a synthetic dataset and plot it.") parser.add_argument( "--kind", - choices=["mixed", "continuous", "rich"], + choices=["mixed", "continuous"], default="mixed", - help="'mixed' (1 cont + 1 cat, MNPE), 'continuous' (2 cont, NPE), or 'rich' (3 cont + 2 cat, MNPE).", + help="'mixed' (continuous + categorical, MNPE) or 'continuous' (continuous-only, NPE).", ) parser.add_argument( "--output", @@ -196,7 +182,7 @@ def _demo(): parser.add_argument("--num-episodes", type=int, default=2000) args = parser.parse_args() - builder = {"mixed": make_mixed_dataset, "continuous": make_continuous_dataset, "rich": make_rich_dataset}[args.kind] + builder = {"mixed": make_mixed_dataset, "continuous": make_continuous_dataset}[args.kind] dataset = builder(seed=args.seed, num_episodes=args.num_episodes) analyzer = SensitivityAnalyzer(dataset) analyzer.fit() diff --git a/isaaclab_arena/tests/test_sensitivity_analysis.py b/isaaclab_arena/tests/test_sensitivity_analysis.py index 5c16829a34..e57ce379ea 100644 --- a/isaaclab_arena/tests/test_sensitivity_analysis.py +++ b/isaaclab_arena/tests/test_sensitivity_analysis.py @@ -5,11 +5,11 @@ """End-to-end sensitivity-analysis tests on synthetic data with a known ground truth. -Each test fits a SensitivityAnalyzer on a dataset whose factor→outcome relationship is -planted by the synthetic module (brighter light, smaller grasp offset, and oak raise -success), then asserts the posterior conditioned on success recovers that relationship. The -data is built in memory, so these run on CPU without Isaac Sim. They cover both estimator -paths: MNPE for mixed schemas, NPE for continuous-only (with 2-D theta). +Each test fits a SensitivityAnalyzer on a dataset whose factor→outcome relationships are +planted by the synthetic module (brighter / lighter / closer / cube / oak raise success), +then asserts the posterior conditioned on success recovers them. The data is built in +memory, so these run on CPU without Isaac Sim. They cover both estimator paths: MNPE for a +mixed schema, NPE for a continuous-only one (2-D theta). """ from __future__ import annotations @@ -19,26 +19,39 @@ from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer from isaaclab_arena.analysis.sensitivity.synthetic import ( + CAMERA_DISTANCE, GRASP_OFFSET, LIGHT, MATERIAL, + OBJECT_MASS, + OBJECT_TYPE, make_continuous_dataset, make_mixed_dataset, ) _NUM_SAMPLES = 5000 -_LIGHT_MIDPOINT = 0.5 * (LIGHT.value_range[0] + LIGHT.value_range[1]) -_OFFSET_MIDPOINT = 0.5 * (GRASP_OFFSET.value_range[0] + GRASP_OFFSET.value_range[1]) - def _factor_samples(analyzer: SensitivityAnalyzer, samples: torch.Tensor, factor_name: str) -> np.ndarray: """Pull one factor's column out of a posterior-sample tensor as a 1-D numpy array.""" return samples[:, analyzer.dataset.factor_columns[factor_name]].squeeze(-1).cpu().numpy() -def test_mnpe_recovers_light_and_material_effects(): - """Mixed continuous + categorical (MNPE): recover the light trend and the material ranking.""" +def _midpoint(factor) -> float: + """Midpoint of a continuous factor's range — the threshold a recovered mean should beat.""" + low, high = factor.value_range + return 0.5 * (low + high) + + +def _most_likely_choice(analyzer, samples, factor_name: str, choices: list[str]) -> str: + """The categorical choice the posterior favors (mode over rounded integer-coded samples).""" + codes = np.clip(np.round(_factor_samples(analyzer, samples, factor_name)), 0, len(choices) - 1).astype(int) + probabilities = np.bincount(codes, minlength=len(choices)) / len(codes) + return choices[int(probabilities.argmax())] + + +def test_mnpe_recovers_all_planted_effects(): + """Mixed continuous + categorical (MNPE): recover every planted effect at once.""" dataset = make_mixed_dataset(seed=0) analyzer = SensitivityAnalyzer(dataset) assert analyzer._select_inference_class().__name__ == "MNPE", "mixed schema should select MNPE" @@ -47,15 +60,14 @@ def test_mnpe_recovers_light_and_material_effects(): analyzer.fit() samples = analyzer.sample_posterior(num_samples=_NUM_SAMPLES) # conditions on success=1 by default - # Continuous effect: brighter light is planted to raise success. - assert _factor_samples(analyzer, samples, "light_intensity").mean() > _LIGHT_MIDPOINT + # Continuous effects: brighter light, a lighter object, and a closer camera raise success. + assert _factor_samples(analyzer, samples, "light_intensity").mean() > _midpoint(LIGHT) + assert _factor_samples(analyzer, samples, "object_mass").mean() < _midpoint(OBJECT_MASS) + assert _factor_samples(analyzer, samples, "camera_distance").mean() < _midpoint(CAMERA_DISTANCE) - # Categorical effect: oak is the planted best material, bamboo the worst. - materials = MATERIAL.choices - material_codes = np.clip(np.round(_factor_samples(analyzer, samples, "table_material")), 0, len(materials) - 1) - probabilities = np.bincount(material_codes.astype(int), minlength=len(materials)) / len(material_codes) - assert materials[int(probabilities.argmax())] == "oak", f"expected oak most likely, got {probabilities}" - assert probabilities[materials.index("oak")] > probabilities[materials.index("bamboo")] + # Categorical effects: cube and oak are the planted best choices. + assert _most_likely_choice(analyzer, samples, "object_type", OBJECT_TYPE.choices) == "cube" + assert _most_likely_choice(analyzer, samples, "table_material", MATERIAL.choices) == "oak" def test_npe_recovers_two_continuous_effects(): @@ -69,6 +81,6 @@ def test_npe_recovers_two_continuous_effects(): samples = analyzer.sample_posterior(num_samples=_NUM_SAMPLES) # conditions on success=1 by default # Brighter light raises success → light posterior skews high. - assert _factor_samples(analyzer, samples, "light_intensity").mean() > _LIGHT_MIDPOINT + assert _factor_samples(analyzer, samples, "light_intensity").mean() > _midpoint(LIGHT) # A smaller grasp offset raises success → offset posterior skews low. - assert _factor_samples(analyzer, samples, "grasp_offset").mean() < _OFFSET_MIDPOINT + assert _factor_samples(analyzer, samples, "grasp_offset").mean() < _midpoint(GRASP_OFFSET) From 48b58bf639c74028a64d27aac2b584d5728365eb Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 12 Jun 2026 17:36:01 +0200 Subject: [PATCH 59/74] Assert continuous factors carry a range before normalizing SensitivityAnalyzer.__init__ indexed factor.range[0] directly, so a continuous factor with range=None (e.g. a dataset built via the constructor rather than from_files, where the range is never inferred) raised an opaque TypeError. Add an assert with a message pointing at how to supply the range. Signed-off-by: Clemens Volk --- isaaclab_arena/analysis/sensitivity/analyzer.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/isaaclab_arena/analysis/sensitivity/analyzer.py b/isaaclab_arena/analysis/sensitivity/analyzer.py index 58b0f987cf..f5e047dc1a 100644 --- a/isaaclab_arena/analysis/sensitivity/analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/analyzer.py @@ -35,6 +35,12 @@ def __init__(self, dataset: SensitivityDataset): self.posterior = None continuous_factors = [factor for factor in dataset.schema.factors if factor.type == "continuous"] self._num_continuous = len(continuous_factors) + for factor in continuous_factors: + assert factor.range is not None, ( + f"Continuous factor {factor.name!r} has no range to normalize against. Declare a" + " range in factors.yaml, or build the dataset via from_files()/from_file() so the" + " range is inferred from the data before constructing the analyzer." + ) self._continuous_low = torch.tensor([factor.range[0][0] for factor in continuous_factors]) self._continuous_high = torch.tensor([factor.range[0][1] for factor in continuous_factors]) From ff0034390ee0a1c8c55783beb6e15bb343bd13ba Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 12 Jun 2026 17:37:49 +0200 Subject: [PATCH 60/74] Seed the sensitivity report RNG for reproducibility MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit generate_report() trains the estimator and samples the posterior, both of which consume torch's global RNG, but nothing seeded it — so report output varied run-to-run. Seed torch once before fitting (new --seed, default 0; pass None to leave the RNG untouched). Signed-off-by: Clemens Volk --- .../analysis/sensitivity/generate_report.py | 21 ++++++++++++++++++- 1 file changed, 20 insertions(+), 1 deletion(-) diff --git a/isaaclab_arena/analysis/sensitivity/generate_report.py b/isaaclab_arena/analysis/sensitivity/generate_report.py index 089b7480fc..a746ceb3a2 100644 --- a/isaaclab_arena/analysis/sensitivity/generate_report.py +++ b/isaaclab_arena/analysis/sensitivity/generate_report.py @@ -21,6 +21,7 @@ def generate_report( output_path: str | Path, outcome_names: list[str] | tuple[str, ...] = ("success",), observation: list[float] | None = None, + seed: int | None = 0, ) -> Path: """Build a sensitivity report from a factors.yaml / episode_summary.jsonl pair. @@ -34,10 +35,17 @@ def generate_report( outcome_names: Which per-episode outcome(s) to condition on. observation: Outcome values to condition on, one per outcome name. Defaults to conditioning on success (1) for every (binary) outcome. + seed: Seed for torch's global RNG, set once before fitting so the estimator training + and posterior sampling are reproducible. Pass ``None`` to leave the RNG untouched. Returns: The resolved output path. """ + # Estimator training (fit) and posterior sampling both draw from torch's global RNG in + # sequence, so seeding once here makes the whole report reproducible. + if seed is not None: + torch.manual_seed(seed) + dataset = SensitivityDataset.from_files(Path(factors_yaml_path), Path(jsonl_path), outcome_names) analyzer = SensitivityAnalyzer(dataset) analyzer.fit() @@ -87,10 +95,21 @@ def main(): "Outcomes are binary, so use 1 for success or 0 for failure. Defaults to 1 (success)." ), ) + parser.add_argument( + "--seed", + type=int, + default=0, + help="Seed for torch's global RNG, so estimator training + sampling are reproducible. Default: 0.", + ) args = parser.parse_args() generate_report( - args.factors_yaml, args.episode_summary, args.output, outcome_names=args.outcome, observation=args.observation + args.factors_yaml, + args.episode_summary, + args.output, + outcome_names=args.outcome, + observation=args.observation, + seed=args.seed, ) From 1cec2873b41f67e1f77bb99fea15e6b8a4e23b1e Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 12 Jun 2026 17:39:53 +0200 Subject: [PATCH 61/74] Drop the unused SensitivityDataset.prior property The analyzer builds its own _normalized_prior() over the normalized [0,1] space and never reads dataset.prior; nothing else referenced it. Remove the property and the now-orphaned BoxUniform import. Signed-off-by: Clemens Volk --- .../analysis/sensitivity/dataset.py | 36 ------------------- 1 file changed, 36 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py index 924117ba4f..21bafdd3de 100644 --- a/isaaclab_arena/analysis/sensitivity/dataset.py +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -12,8 +12,6 @@ from pathlib import Path from typing import Literal -from sbi.utils import BoxUniform - @dataclass class FactorSpec: @@ -216,40 +214,6 @@ def has_categorical_factors(self) -> bool: """True iff the schema declares at least one categorical factor.""" return any(factor.type == "categorical" for factor in self.schema.factors) - @property - def prior(self): - """Uniform prior (sbi.utils.BoxUniform) over all factor dims. - - Continuous factors use their [low, high] range; categorical factors use - [0, num_choices - 1] over their integer codes, in continuous-first order. - """ - low_bounds: list[float] = [] - high_bounds: list[float] = [] - - # Continuous bounds (one [low, high] per dim). - for factor in self.schema.factors: - if factor.type != "continuous": - continue - assert factor.range is not None, f"Factor {factor.name!r} has no range and was not inferred" - for dim_low, dim_high in factor.range: - low_bounds.append(float(dim_low)) - high_bounds.append(float(dim_high)) - - # Categorical factor bounds: [0, num_choices - 1] per factor (one column). - for factor in self.schema.factors: - if factor.type != "categorical": - continue - assert ( - factor.choices is not None and len(factor.choices) > 0 - ), f"Categorical factor {factor.name!r} has no `choices:` block" - low_bounds.append(0.0) - high_bounds.append(float(len(factor.choices) - 1)) - - return BoxUniform( - low=torch.tensor(low_bounds, dtype=torch.float32), - high=torch.tensor(high_bounds, dtype=torch.float32), - ) - def _validate_rows( schema: FactorSchema, rows: list[dict], outcome_names: list[str] | tuple[str, ...], jsonl_path: str | Path From 9599d519d8a0b7e21da1ab40be18a649c7f5014f Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 12 Jun 2026 17:50:22 +0200 Subject: [PATCH 62/74] Test factors.yaml / episode_summary.jsonl parsing The existing tests only build datasets in memory; nothing covered the file parsing path. Add two parse-only tests for SensitivityDataset.from_files: one asserting the mixed-schema theta/x layout (continuous-first, categorical integer-coded by choice index) and outcome casting, and one asserting that a continuous factor with no declared range gets [min, max] inferred from the data. Signed-off-by: Clemens Volk --- .../tests/test_sensitivity_analysis.py | 66 +++++++++++++++++++ 1 file changed, 66 insertions(+) diff --git a/isaaclab_arena/tests/test_sensitivity_analysis.py b/isaaclab_arena/tests/test_sensitivity_analysis.py index e57ce379ea..9180f09c78 100644 --- a/isaaclab_arena/tests/test_sensitivity_analysis.py +++ b/isaaclab_arena/tests/test_sensitivity_analysis.py @@ -14,10 +14,12 @@ from __future__ import annotations +import json import numpy as np import torch from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer +from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset from isaaclab_arena.analysis.sensitivity.synthetic import ( CAMERA_DISTANCE, GRASP_OFFSET, @@ -84,3 +86,67 @@ def test_npe_recovers_two_continuous_effects(): assert _factor_samples(analyzer, samples, "light_intensity").mean() > _midpoint(LIGHT) # A smaller grasp offset raises success → offset posterior skews low. assert _factor_samples(analyzer, samples, "grasp_offset").mean() < _midpoint(GRASP_OFFSET) + + +def _write_jsonl(path, rows: list[dict]) -> None: + """Write one JSON object per line to ``path``.""" + path.write_text("\n".join(json.dumps(row) for row in rows) + "\n", encoding="utf-8") + + +def test_from_files_parses_mixed_schema_and_builds_tensors(tmp_path): + """from_files parses a factors.yaml + episode_summary.jsonl into the expected theta / x layout.""" + factors_yaml = tmp_path / "factors.yaml" + factors_yaml.write_text( + "factors:\n" + " light_intensity:\n" + " type: continuous\n" + " range: [[0.0, 1000.0]]\n" + " pick_up_object:\n" + " type: categorical\n" + " choices: [cube, can]\n", + encoding="utf-8", + ) + jsonl = tmp_path / "episode_summary.jsonl" + _write_jsonl( + jsonl, + [ + {"arena_env_args": {"light_intensity": 250.0, "pick_up_object": "cube"}, "outcomes": {"success": 1}}, + {"arena_env_args": {"light_intensity": 750.0, "pick_up_object": "can"}, "outcomes": {"success": 0}}, + {"arena_env_args": {"light_intensity": 500.0, "pick_up_object": "cube"}, "outcomes": {"success": 1}}, + ], + ) + + dataset = SensitivityDataset.from_files(factors_yaml, jsonl, outcome_names=["success"]) + + # Schema parsed with the declared structure. + factors_by_name = {factor.name: factor for factor in dataset.schema.factors} + assert factors_by_name["light_intensity"].type == "continuous" + assert factors_by_name["light_intensity"].range == [[0.0, 1000.0]] + assert factors_by_name["pick_up_object"].type == "categorical" + assert factors_by_name["pick_up_object"].choices == ["cube", "can"] + + # Continuous-first theta layout; categorical integer-coded by its index into choices. + assert dataset.theta.shape == (3, 2) + assert dataset.x.shape == (3, 1) + assert dataset.factor_columns == {"light_intensity": slice(0, 1), "pick_up_object": slice(1, 2)} + assert dataset.theta[:, 0].tolist() == [250.0, 750.0, 500.0] + assert dataset.theta[:, 1].tolist() == [0.0, 1.0, 0.0] # cube -> 0, can -> 1 + assert dataset.x[:, 0].tolist() == [1.0, 0.0, 1.0] + + +def test_from_files_infers_missing_continuous_range(tmp_path): + """A continuous factor with no declared range gets [min, max] inferred from the observed values.""" + factors_yaml = tmp_path / "factors.yaml" + factors_yaml.write_text("factors:\n light_intensity:\n type: continuous\n", encoding="utf-8") + jsonl = tmp_path / "episode_summary.jsonl" + _write_jsonl( + jsonl, + [ + {"arena_env_args": {"light_intensity": 30.0}, "outcomes": {"success": 0}}, + {"arena_env_args": {"light_intensity": 90.0}, "outcomes": {"success": 1}}, + ], + ) + + dataset = SensitivityDataset.from_files(factors_yaml, jsonl, outcome_names=["success"]) + + assert dataset.schema.factors[0].range == [[30.0, 90.0]] From 804759f26a5637fe2986821bbdd503d8009d84bf Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 12 Jun 2026 17:58:23 +0200 Subject: [PATCH 63/74] Model FactorSpec.type as a FactorType enum Replace the Literal["continuous", "categorical"] with a FactorType(str, Enum) plus __post_init__ coercion, giving a single source of truth for the valid values, fail-fast at construction, and autocomplete. Being a str-Enum, it compares equal to its string value, so the existing == "continuous" comparisons and type="continuous" constructions keep working unchanged. Signed-off-by: Clemens Volk --- isaaclab_arena/analysis/sensitivity/dataset.py | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py index 21bafdd3de..2251e93e9b 100644 --- a/isaaclab_arena/analysis/sensitivity/dataset.py +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -9,8 +9,15 @@ import torch import yaml from dataclasses import dataclass +from enum import Enum from pathlib import Path -from typing import Literal + + +class FactorType(str, Enum): + """Whether a factor's values are continuous (numeric range) or categorical (labelled choices).""" + + CONTINUOUS = "continuous" + CATEGORICAL = "categorical" @dataclass @@ -22,11 +29,15 @@ class FactorSpec: """ name: str - type: Literal["continuous", "categorical"] + type: FactorType dim: int = 1 range: list[list[float]] | None = None # one [low, high] pair per dim, continuous only choices: list[str] | None = None # categorical only + def __post_init__(self) -> None: + # Accept the raw string form (from YAML / callers) and normalize to the enum. + self.type = FactorType(self.type) + @dataclass class FactorSchema: From 308580d023cf55e302eff53fef7e32f44ee42c64 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 12 Jun 2026 18:00:52 +0200 Subject: [PATCH 64/74] Type FactorSpec.range as list[tuple[float, float]] Each range entry is a fixed (low, high) pair, so list[tuple[float, float]] documents the arity better than list[list[float]]. __post_init__ coerces the lists that YAML/JSON deliver to tuples (and the range-inference path emits a tuple) so the annotation matches the runtime values. Signed-off-by: Clemens Volk --- isaaclab_arena/analysis/sensitivity/dataset.py | 7 +++++-- isaaclab_arena/tests/test_sensitivity_analysis.py | 4 ++-- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py index 2251e93e9b..94ca09630e 100644 --- a/isaaclab_arena/analysis/sensitivity/dataset.py +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -31,12 +31,15 @@ class FactorSpec: name: str type: FactorType dim: int = 1 - range: list[list[float]] | None = None # one [low, high] pair per dim, continuous only + range: list[tuple[float, float]] | None = None # one (low, high) pair per dim, continuous only choices: list[str] | None = None # categorical only def __post_init__(self) -> None: # Accept the raw string form (from YAML / callers) and normalize to the enum. self.type = FactorType(self.type) + # Normalize each (low, high) pair to a tuple (YAML/JSON deliver them as lists). + if self.range is not None: + self.range = [tuple(pair) for pair in self.range] @dataclass @@ -266,7 +269,7 @@ def _infer_missing_factor_ranges(schema: FactorSchema, rows: list[dict]) -> None f" factor {factor.name!r} has dim={factor.dim}" ) observed_values = [float(row["arena_env_args"][factor.name]) for row in rows] - factor.range = [[min(observed_values), max(observed_values)]] + factor.range = [(min(observed_values), max(observed_values))] def _build_factor_tensor(schema: FactorSchema, rows: list[dict]) -> torch.Tensor: diff --git a/isaaclab_arena/tests/test_sensitivity_analysis.py b/isaaclab_arena/tests/test_sensitivity_analysis.py index 9180f09c78..24d35c714b 100644 --- a/isaaclab_arena/tests/test_sensitivity_analysis.py +++ b/isaaclab_arena/tests/test_sensitivity_analysis.py @@ -121,7 +121,7 @@ def test_from_files_parses_mixed_schema_and_builds_tensors(tmp_path): # Schema parsed with the declared structure. factors_by_name = {factor.name: factor for factor in dataset.schema.factors} assert factors_by_name["light_intensity"].type == "continuous" - assert factors_by_name["light_intensity"].range == [[0.0, 1000.0]] + assert factors_by_name["light_intensity"].range == [(0.0, 1000.0)] assert factors_by_name["pick_up_object"].type == "categorical" assert factors_by_name["pick_up_object"].choices == ["cube", "can"] @@ -149,4 +149,4 @@ def test_from_files_infers_missing_continuous_range(tmp_path): dataset = SensitivityDataset.from_files(factors_yaml, jsonl, outcome_names=["success"]) - assert dataset.schema.factors[0].range == [[30.0, 90.0]] + assert dataset.schema.factors[0].range == [(30.0, 90.0)] From 6f4a007b1d31a3a6d2c5325b6aecd78a0e05319e Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 12 Jun 2026 18:05:03 +0200 Subject: [PATCH 65/74] Move synthetic dataset generator into the tests package synthetic.py is test infrastructure (planted-ground-truth generators the unit tests assert against) plus a convenience CLI, not part of the shipped analysis.sensitivity API. Move it to isaaclab_arena/tests/sensitivity_synthetic.py, next to its only consumer, and update the test import and the python -m paths in its docstring and the docs. Keeps it off the core API surface without making the tests depend on the examples package. Signed-off-by: Clemens Volk --- docs/pages/concepts/policy/concept_sensitivity_analysis.rst | 2 +- .../sensitivity/synthetic.py => tests/sensitivity_synthetic.py} | 2 +- isaaclab_arena/tests/test_sensitivity_analysis.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) rename isaaclab_arena/{analysis/sensitivity/synthetic.py => tests/sensitivity_synthetic.py} (98%) diff --git a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst index c4c66641d3..133ea11187 100644 --- a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst +++ b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst @@ -124,7 +124,7 @@ without Isaac Sim — useful for seeing the output shape and for validating the .. code-block:: bash # mixed: three continuous + two categorical factors (MNPE) - python -m isaaclab_arena.analysis.sensitivity.synthetic --kind mixed --output eval/demo.png + python -m isaaclab_arena.tests.sensitivity_synthetic --kind mixed --output eval/demo.png ``--kind`` also accepts ``continuous`` (continuous-only factors, which exercises the NPE path). diff --git a/isaaclab_arena/analysis/sensitivity/synthetic.py b/isaaclab_arena/tests/sensitivity_synthetic.py similarity index 98% rename from isaaclab_arena/analysis/sensitivity/synthetic.py rename to isaaclab_arena/tests/sensitivity_synthetic.py index 463337d4d9..056b6ef50f 100644 --- a/isaaclab_arena/analysis/sensitivity/synthetic.py +++ b/isaaclab_arena/tests/sensitivity_synthetic.py @@ -164,7 +164,7 @@ def _demo(): Runs the pipeline end to end on generated data: simulate → fit → plot, with no eval data needed. Run as:: - python -m isaaclab_arena.analysis.sensitivity.synthetic --kind mixed --output eval/demo.png + python -m isaaclab_arena.tests.sensitivity_synthetic --kind mixed --output eval/demo.png """ parser = argparse.ArgumentParser(description="Run the sensitivity pipeline on a synthetic dataset and plot it.") parser.add_argument( diff --git a/isaaclab_arena/tests/test_sensitivity_analysis.py b/isaaclab_arena/tests/test_sensitivity_analysis.py index 24d35c714b..cf6d50a799 100644 --- a/isaaclab_arena/tests/test_sensitivity_analysis.py +++ b/isaaclab_arena/tests/test_sensitivity_analysis.py @@ -20,7 +20,7 @@ from isaaclab_arena.analysis.sensitivity.analyzer import SensitivityAnalyzer from isaaclab_arena.analysis.sensitivity.dataset import SensitivityDataset -from isaaclab_arena.analysis.sensitivity.synthetic import ( +from isaaclab_arena.tests.sensitivity_synthetic import ( CAMERA_DISTANCE, GRASP_OFFSET, LIGHT, From 68cca974a4ad1da6cb99295b549da561837d5dc7 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 12 Jun 2026 18:07:28 +0200 Subject: [PATCH 66/74] Clarify FactorSchema docstring State that the schema describes what can vary, not the per-episode values, and drop the wordy outcome-conditioning aside (per review). Signed-off-by: Clemens Volk --- isaaclab_arena/analysis/sensitivity/dataset.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/isaaclab_arena/analysis/sensitivity/dataset.py b/isaaclab_arena/analysis/sensitivity/dataset.py index 94ca09630e..c4bac0a610 100644 --- a/isaaclab_arena/analysis/sensitivity/dataset.py +++ b/isaaclab_arena/analysis/sensitivity/dataset.py @@ -46,9 +46,9 @@ def __post_init__(self) -> None: class FactorSchema: """Parsed factors.yaml — the list of factors that were varied. - Factors carry structure (continuous vs categorical, range/choices) needed to build theta. - Outcomes are not part of the schema: they are always read as floats and *which* outcome to - condition on is a query, chosen at analysis time (see SensitivityDataset.from_files). + The schema describes what *can* vary (continuous vs categorical, range/choices), not the + values taken in any given episode. Outcomes are not part of the schema; which outcome to + condition on is chosen at analysis time. """ factors: list[FactorSpec] From b1a25b794575fc86779b5e089d6ad214f947b65f Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 12 Jun 2026 18:08:34 +0200 Subject: [PATCH 67/74] Define theta and x in the SensitivityAnalyzer docstring State sbi's convention explicitly: theta is the per-episode factor values the posterior is inferred over, x is the per-episode outcomes a query conditions on. Signed-off-by: Clemens Volk --- isaaclab_arena/analysis/sensitivity/analyzer.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/isaaclab_arena/analysis/sensitivity/analyzer.py b/isaaclab_arena/analysis/sensitivity/analyzer.py index f5e047dc1a..988239d468 100644 --- a/isaaclab_arena/analysis/sensitivity/analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/analyzer.py @@ -21,7 +21,9 @@ class SensitivityAnalyzer: - MNPE when any factor is categorical (it handles mixed continuous + categorical theta). - NPE when every factor is continuous. - It then trains on the full (theta, x) and samples the joint posterior at a chosen + Following sbi's convention, ``theta`` is the per-episode factor values (the inputs the + posterior is inferred over) and ``x`` is the per-episode outcomes (the observations a query + conditions on). It trains on the full (theta, x) and samples the joint posterior at a chosen observation. The single observation conditions on *all* outcome columns at once, so a query like "which factors produced success?" is answered for every factor jointly. From 8a24daa40d92938f57019efe3262745f2e9b8823 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 12 Jun 2026 18:13:55 +0200 Subject: [PATCH 68/74] Tighten sensitivity docs wording Address review nits on the sensitivity concept page: give an example outcome at first mention (success rate), call the continuous-factor plot a probability density curve, and drop the CPU aside from the synthetic-data section. Signed-off-by: Clemens Volk --- .../concepts/policy/concept_sensitivity_analysis.rst | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst index 133ea11187..5c33c22fea 100644 --- a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst +++ b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst @@ -4,8 +4,8 @@ Sensitivity Analysis The sensitivity-analysis toolbox answers a single question about a policy: *which environment conditions drive success?* Given the per-episode results of an evaluation sweep — where factors such as lighting, object mass, or table material were -varied — it fits a posterior over those factors conditioned on the outcome and renders -one figure summarising which factor values are associated with success. +varied — it fits a posterior over those factors conditioned on the outcome (e.g. success +rate) and renders one figure summarising which factor values are associated with success. Why a joint posterior, not a success rate per factor? ----------------------------------------------------- @@ -41,8 +41,8 @@ neural posterior estimators. The flow is: 3. **Inference.** ``SensitivityAnalyzer`` loads the pair, trains an estimator on the full ``(theta, x)`` jointly, and samples the joint posterior conditioned on a chosen observation (by default, success). -4. **Report.** A smooth density curve for each continuous factor and a probability bar chart - for each categorical factor. +4. **Report.** A probability density curve for each continuous factor and a probability bar + chart for each categorical factor. Inputs ------ @@ -117,8 +117,8 @@ defaults to ``1`` (success). Trying it on synthetic data --------------------------- -A synthetic simulator with a *known* ground truth lets you run the whole pipeline on CPU, -without Isaac Sim — useful for seeing the output shape and for validating the toolbox +A synthetic simulator with a *known* ground truth lets you run the whole pipeline without +Isaac Sim — useful for seeing the output shape and for validating the toolbox (the recovered posterior should reflect the planted relationship): .. code-block:: bash From 0778af288773d9a8751f9bfed78fe329d098d7f5 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 12 Jun 2026 18:15:55 +0200 Subject: [PATCH 69/74] Define the posterior in the sensitivity docs Add an intro paragraph stating what the posterior is (the prior over swept factor values reweighted by how often each led to the chosen outcome) and separating the two ideas a reviewer asked about: joint (captures interactions/confounds) vs posterior (conditions on the outcome). Signed-off-by: Clemens Volk --- .../pages/concepts/policy/concept_sensitivity_analysis.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst index 5c33c22fea..4bfb725baa 100644 --- a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst +++ b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst @@ -7,6 +7,13 @@ evaluation sweep — where factors such as lighting, object mass, or table mater varied — it fits a posterior over those factors conditioned on the outcome (e.g. success rate) and renders one figure summarising which factor values are associated with success. +Two distinct ideas are at work. *Joint* means all factors are modelled together rather than +one at a time, which is what captures interactions and confounds (see the next section). +*Posterior* means the result is conditioned on the outcome: starting from the prior — the +factor values the sweep actually drew, uniform over the declared ranges — it reweights them +by how often each led to the chosen outcome. So the figure answers *given success, which +factor values were in play?*, not merely *how were the factors distributed in the sweep?* + Why a joint posterior, not a success rate per factor? ----------------------------------------------------- From f317570bbfd71f338072c7ac04e6009bec7112ee Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 12 Jun 2026 18:18:42 +0200 Subject: [PATCH 70/74] Mark per-episode recording as a follow-up in the sensitivity docs The eval-side writer (episode_writer / episode_summary.jsonl) is not part of this version. Drop the present-tense claims that it records during evaluation, and add a TODO noting it lands in a follow-up and that this version runs on synthetic data or an externally-produced JSONL. Signed-off-by: Clemens Volk --- .../policy/concept_sensitivity_analysis.rst | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst index 4bfb725baa..47f5ed3c74 100644 --- a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst +++ b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst @@ -39,8 +39,8 @@ How it works The toolbox is a thin analysis layer over `sbi `_'s neural posterior estimators. The flow is: -1. **Per-episode recording.** During evaluation, ``episode_writer`` appends one row per - episode to an ``episode_summary.jsonl`` file. +1. **Per-episode input.** The analysis reads an ``episode_summary.jsonl`` — one row per + episode, holding that episode's factor values and outcomes. 2. **Schema.** A ``factors.yaml`` declares the *factors* — which ``arena_env_args`` columns were varied and whether each is continuous or categorical, plus the continuous ranges that were swept (so the analyzer's prior matches the simulation). It does **not** list @@ -51,6 +51,12 @@ neural posterior estimators. The flow is: 4. **Report.** A probability density curve for each continuous factor and a probability bar chart for each categorical factor. +.. todo:: + + The eval-runner writer (``episode_writer``) that emits ``episode_summary.jsonl`` during + evaluation is not part of this version — it lands in a follow-up. For now, run the analysis + on synthetic data (see below) or on a JSONL produced externally. + Inputs ------ @@ -67,8 +73,8 @@ were swept). Outcomes are not declared here — they're selected at analysis tim type: categorical choices: [oak, walnut, bamboo] -**episode_summary.jsonl** is produced by the eval runner — one JSON object per episode. It -carries every measured outcome; the analysis picks which one(s) to condition on: +**episode_summary.jsonl** holds one JSON object per episode. It carries every measured +outcome; the analysis picks which one(s) to condition on: .. code-block:: json From 934999aaf652820096655d72147c2cfd0e74bfa9 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 12 Jun 2026 18:19:24 +0200 Subject: [PATCH 71/74] Gloss theta and x in the sensitivity docs Introduce the terms at first use in the Inference step: theta is the factor values, x the per-episode outcomes (sbi's terms). Signed-off-by: Clemens Volk --- docs/pages/concepts/policy/concept_sensitivity_analysis.rst | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst index 47f5ed3c74..093dae30bc 100644 --- a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst +++ b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst @@ -46,8 +46,9 @@ neural posterior estimators. The flow is: that were swept (so the analyzer's prior matches the simulation). It does **not** list outcomes — *which* outcome to condition on is chosen at analysis time, not saved here. 3. **Inference.** ``SensitivityAnalyzer`` loads the pair, trains an estimator on the full - ``(theta, x)`` jointly, and samples the joint posterior conditioned on a chosen - observation (by default, success). + ``(theta, x)`` jointly — sbi's terms for the factor values (``theta``) and the per-episode + outcomes (``x``) — and samples the joint posterior conditioned on a chosen observation + (by default, success). 4. **Report.** A probability density curve for each continuous factor and a probability bar chart for each categorical factor. From 3c39843d80a97c5b0873a6ddcd15c3456bbfb920 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 12 Jun 2026 18:24:00 +0200 Subject: [PATCH 72/74] Clarify the report-reading interpretation in the sensitivity docs Add the precise reading (among successful episodes, the probability density that the factor took each value) alongside the intuitive 'which values were responsible', and drop the unexplained 'light-gated' jargon. Signed-off-by: Clemens Volk --- .../concepts/policy/concept_sensitivity_analysis.rst | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst index 093dae30bc..b3b726366e 100644 --- a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst +++ b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst @@ -149,11 +149,13 @@ Reading the output Add a sample report figure here and walk through reading it. -Each panel is the posterior over one factor *conditioned on success* — "given the policy -succeeded, which values of this factor were responsible?" For a continuous factor, mass -concentrated at one end of its range means success favoured that end (e.g. a curve rising -toward bright light → the policy is light-gated). For a categorical factor, the tallest -bar is the value most associated with success. +Each panel is the posterior over one factor *conditioned on success*. Intuitively it answers +"given the policy succeeded, which values of this factor were responsible?" More precisely, +among the successful episodes it shows the probability density that the factor took each +value. For a continuous factor, mass concentrated at one end of its range means success +favoured that end — e.g. a curve rising toward bright light means successful episodes were +almost all bright ones, i.e. the policy needs bright light to succeed. +For a categorical factor, the tallest bar is the value most associated with success. Current scope ------------- From bc917dd5750b4db0a609c59a59e135b5772d3a23 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 12 Jun 2026 18:26:30 +0200 Subject: [PATCH 73/74] Note the planned vector-factor scalarization in the sensitivity docs Extend the Current scope vector-factor bullet: when dim>1 factors land, the plan is to record scalar reductions (norm / distance-to-reference) alongside the raw vector so a pose or RGB factor becomes analysable scalar columns. Signed-off-by: Clemens Volk --- docs/pages/concepts/policy/concept_sensitivity_analysis.rst | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst index b3b726366e..82793670f8 100644 --- a/docs/pages/concepts/policy/concept_sensitivity_analysis.rst +++ b/docs/pages/concepts/policy/concept_sensitivity_analysis.rst @@ -162,7 +162,9 @@ Current scope - Outcomes are treated as **binary** (0/1). Conditioning defaults to success; a continuous outcome is rejected with a clear error rather than silently averaged. -- Continuous **vector** factors (``dim > 1``) are reserved for a future extension. +- Continuous **vector** factors (``dim > 1``) are reserved for a future extension. The likely + approach is to record scalar reductions (e.g. a norm or distance-to-reference) alongside the + raw vector, so a pose or RGB factor becomes one or more analysable scalar columns. - The estimators run on CPU and do not require Isaac Sim, so a report can be generated anywhere the evaluation JSONL is available. - The analysis assumes the ``episode_summary.jsonl`` is a single coherent slice — one From 90069122e4e476052d582c9c1a0a856242cfc818 Mon Sep 17 00:00:00 2001 From: Clemens Volk Date: Fri, 12 Jun 2026 18:33:05 +0200 Subject: [PATCH 74/74] Signpost the continuous-first theta layout in the analyzer _normalize/_denormalize slice theta[:, :self._num_continuous] assuming continuous factors lead. Add a comment noting that layout is established by SensitivityDataset and FactorSchema.factor_columns, so the invariant is documented at the slice. Signed-off-by: Clemens Volk --- isaaclab_arena/analysis/sensitivity/analyzer.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/isaaclab_arena/analysis/sensitivity/analyzer.py b/isaaclab_arena/analysis/sensitivity/analyzer.py index 988239d468..cca176797a 100644 --- a/isaaclab_arena/analysis/sensitivity/analyzer.py +++ b/isaaclab_arena/analysis/sensitivity/analyzer.py @@ -36,6 +36,9 @@ def __init__(self, dataset: SensitivityDataset): self.dataset = dataset self.posterior = None continuous_factors = [factor for factor in dataset.schema.factors if factor.type == "continuous"] + # theta is laid out continuous-first then categorical — built that way by + # SensitivityDataset and defined by FactorSchema.factor_columns — so the leading + # self._num_continuous columns are the continuous factors that _normalize/_denormalize slice. self._num_continuous = len(continuous_factors) for factor in continuous_factors: assert factor.range is not None, (