Add files using upload-large-folder tool

MANIFEST.txt

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+MANIFEST.txt
+README.md
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+code/rr_label_study/__init__.py
+code/rr_label_study/oven_study.py
+code/scripts/launch_parallel_oven_label_study.py
+code/scripts/repair_oven_episode_dense.py
+code/scripts/run_oven_label_study.py
+code/scripts/run_oven_single_frame.py
+environment/activate_rlbench_runtime.sh
+environment/conda_env_rlbench.yml
+environment/dataset_notes.txt
+environment/pip_freeze_rlbench.txt
+environment/repo_revisions.txt
+environment/setup_same_hardware.sh
+environment/system_info.txt

README.md

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+# VLAdaptorBench
+
+This repository contains the first-pass benchmark setup, metric code, debug history, and single-episode label-validation artifacts for the proposed VLA + adaptor study on `bimanual_take_tray_out_of_oven`.
+
+This pass is a label study, not a policy study. No `pi0.5` integration is included here.
+
+## What is in this upload
+
+- `code/rr_label_study/`
+  - Core study code, including dense replay, visibility metrics, pregrasp/extraction oracles, keyframe extraction, intervention checks, and summary metric computation.
+- `code/scripts/`
+  - Study runners and helper scripts.
+  - `run_oven_label_study.py`: main dense/keyframe study runner.
+  - `launch_parallel_oven_label_study.py`: multi-display worker launcher.
+  - `run_oven_single_frame.py`: isolated single-frame recomputation helper.
+  - `repair_oven_episode_dense.py`: external repair pass for corrupted dense frames.
+- `artifacts/results/`
+  - All result directories produced during debugging and validation.
+  - This includes failed/stale runs, partial runs, smoke runs, the first independent run, and the final repaired run.
+- `environment/`
+  - Machine snapshot, conda env export, pip freeze, upstream repo revisions, runtime activation helper, setup helper, and dataset notes.
+- `MANIFEST.txt`
+  - Flat file listing of the uploaded bundle contents.
+
+## Final validated artifact
+
+The clean single-episode artifact is:
+
+- `artifacts/results/oven_episode0_repaired_v1/episode0.dense.csv`
+- `artifacts/results/oven_episode0_repaired_v1/episode0.keyframes.csv`
+- `artifacts/results/oven_episode0_repaired_v1/episode0.metrics.json`
+- `artifacts/results/oven_episode0_repaired_v1/summary.json`
+
+Key numbers from `oven_episode0_repaired_v1`:
+
+- `phase_switch_rises = 1`
+- `phase_switch_falls = 0`
+- `ppre_cross_frame = 232`
+- `pext_cross_frame = 232`
+- `ready_cross_frame = 234`
+- `phase_cross_frame = 236`
+- `dense_boundary_error_frames = 2`
+- `key_boundary_error_keyframes = 1`
+- `auroc_ppre_ypre = 1.0`
+- `auprc_ppre_ypre = 1.0`
+- `auroc_pext_yext = 1.0`
+- `auprc_pext_yext = 1.0`
+- `auroc_phase_yready = 0.9895`
+- `auprc_phase_yready = 0.9850`
+- `f1_phase_yready = 0.9894`
+- Interventions:
+  - pre-ready `open_more` raises `p_ext` in `2/2`
+  - pre-ready extract succeeds in `0/2`
+  - post-ready extract succeeds in `2/2`
+  - post-ready extra reveal has low gain in `2/2`
+
+## What was done
+
+The work in this bundle is the full benchmark/metric debugging path, including intermediate failures:
+
+1. Built dense replay and label-generation scaffolding around RLBench/PerAct-style demos.
+2. Verified the local RLBench fork was not actuating discrete grippers correctly through `BimanualJointPositionActionMode`.
+3. Patched replay to explicitly apply bimanual discrete gripper state from demonstrations.
+4. Fixed visibility scoring so it does not depend on stale simulator object handles in stored mask images.
+5. Reworked snapshot restoration to bypass RLBench task-state restore issues after grasp events.
+6. Tightened template extraction and extraction scoring for already-grasped/already-extracted states.
+7. Added independent replay evaluation for dense label validation.
+8. Identified remaining corrupted dense frames caused by repeated replay/restore contamination inside one simulator process.
+9. Added an external isolated-frame repair pass and used it to produce the clean `oven_episode0_repaired_v1` artifact.
+
+The result directories preserve this whole path. They are intentionally not pruned.
+
+## Result directory guide
+
+- `oven_smoke_1`, `oven_smoke_30`, `oven_smoke_30_fast`
+  - Early smoke tests.
+- `oven_episode0_full*`
+  - Early full-episode dense runs before the replay/visibility/oracle fixes converged.
+- `oven_to240_*`
+  - Shorter targeted runs used while debugging replay, snapshots, and late-phase drift.
+- `oven_episode0_independent_v1`
+  - First successful independent dense pass. Core metrics were already strong, but the dense CSV still had stale corrupted rows.
+- `oven_episode0_repaired_v1`
+  - Final cleaned artifact after isolated-frame recomputation.
+- `parallel_smoke_2x10`
+  - Worker/Xvfb parallelization smoke test.
+
+## Environment
+
+The run used:
+
+- Ubuntu `22.04.5`
+- Kernel `6.8.0-65-generic`
+- `96` CPU cores visible
+- `503 GiB` RAM visible
+- `NVIDIA A40` with driver `570.195.03`
+- CUDA reported by `nvidia-smi`: `12.8`
+
+See:
+
+- `environment/system_info.txt`
+- `environment/repo_revisions.txt`
+- `environment/conda_env_rlbench.yml`
+- `environment/pip_freeze_rlbench.txt`
+
+## Upstream repos used
+
+Exact upstream revisions are recorded in `environment/repo_revisions.txt`.
+
+The local run used:
+
+- `markusgrotz/RLBench` at `8af748c51287989294e00c9c670e3330a0e35ed5`
+- `markusgrotz/PyRep` at `b8bd1d7a3182adcd570d001649c0849047ebf197`
+- `markusgrotz/peract_bimanual` at `bb0232a6ba3fe116566e9568f0c7af980ed6703d`
+- `markusgrotz/YARR` at `6822ff78602c77878b27d4cfe759ce029c67bffb`
+
+## Reproducing on the same hardware class
+
+1. Read `environment/dataset_notes.txt`.
+2. Run `environment/setup_same_hardware.sh /workspace`.
+3. Source `environment/activate_rlbench_runtime.sh /workspace`.
+4. Run the dense study:
+
+```bash
+python /workspace/VLAdaptorBench_upload/code/scripts/run_oven_label_study.py \
+  --dataset-root /workspace/data/bimanual_take_tray_out_of_oven_train_128 \
+  --result-dir /workspace/tmp_run \
+  --max-episodes 1 \
+  --checkpoint-stride 16 \
+  --template-episode-index 0 \
+  --independent-replay
+```
+
+5. If the dense CSV shows the known replay contamination pattern, run the external repair pass:
+
+```bash
+python /workspace/VLAdaptorBench_upload/code/scripts/repair_oven_episode_dense.py \
+  --dataset-root /workspace/data/bimanual_take_tray_out_of_oven_train_128 \
+  --episode-dir /workspace/data/bimanual_take_tray_out_of_oven_train_128/all_variations/episodes/episode0 \
+  --input-dense-csv /workspace/tmp_run/episode0.dense.csv \
+  --output-dir /workspace/tmp_run_repaired \
+  --checkpoint-stride 16 \
+  --num-workers 4 \
+  --base-display 170 \
+  --template-episode-dir /workspace/data/bimanual_take_tray_out_of_oven_train_128/all_variations/episodes/episode0
+```
+
+## Important note about baselines
+
+On a single deterministic episode, normalized time can become a degenerate perfect predictor for `y_ext`. That is an artifact of single-episode evaluation, not evidence that time is a useful control signal. The cleaned artifact here should be treated as a validated single-episode label trace, not as a multi-episode benchmark conclusion.
+
+## Dataset note
+
+The upstream RLBench demonstration dataset itself is not re-uploaded in this bundle. This repo contains the study code and all artifacts generated from the local run. The expected dataset path is documented in `environment/dataset_notes.txt`.

artifacts/results/oven_episode0_full/episode0.dense.csv

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artifacts/results/oven_episode0_full/episode0.metrics.json

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artifacts/results/oven_episode0_full/summary.json

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artifacts/results/oven_episode0_full/templates.json

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artifacts/results/oven_episode0_full_v2/templates.json

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artifacts/results/oven_episode0_full_v3/episode0.dense.csv

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artifacts/results/oven_episode0_full_v3/episode0.metrics.json

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artifacts/results/oven_episode0_full_v3/summary.json

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artifacts/results/oven_episode0_full_v4/episode0.metrics.json

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artifacts/results/oven_episode0_repaired_v1/repaired_frames/frame_0247.json

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artifacts/results/oven_episode0_repaired_v1/repaired_frames/frame_0249.json

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artifacts/results/oven_episode0_repaired_v1/repaired_frames/frame_0253.json

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artifacts/results/oven_episode0_repaired_v1/repaired_frames/frame_0254.json

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artifacts/results/oven_episode0_repaired_v1/repaired_frames/frame_0255.json

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artifacts/results/oven_episode0_repaired_v1/repaired_frames/frame_0258.json

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artifacts/results/oven_episode0_repaired_v1/repaired_frames/frame_0263.json

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artifacts/results/oven_episode0_repaired_v1/repaired_frames/frame_0278.json

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artifacts/results/oven_episode0_repaired_v1/repaired_frames/frame_0283.json

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artifacts/results/parallel_smoke_2x10/parallel_summary.json

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artifacts/results/parallel_smoke_2x10/parallel_workers.json

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artifacts/results/parallel_smoke_2x10/worker_00/episode0.keyframes.csv

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artifacts/results/parallel_smoke_2x10/worker_01/episode1.dense.csv

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artifacts/results/parallel_smoke_2x10/worker_01/episode1.metrics.json

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artifacts/results/parallel_smoke_2x10/worker_01/summary.json

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artifacts/results/parallel_smoke_2x10/worker_01/templates.json

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artifacts/results/parallel_smoke_2x10/worker_01/worker.log

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+WARNING:root:not sure how _robot_shapes are used is used.
+WARNING:root:Waypoints are not reachable right=(False, 'waypoint0 - DualPanda') left=(True, '')
+ERROR:root:Error when checking waypoints. Exception is: Error in task bimanual_take_tray_out_of_oven. Infeasible episode. Can't reach waypoint ((False, 'waypoint0 - DualPanda'), (True, '')).
+WARNING: QApplication was not created in the main() thread.
+WARNING:root:not sure how _robot_shapes are used is used.
+[episode1] analyzed 10/10 dense frames
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+    "baseline_auprc_whole_vis_yext": NaN,
+    "pre_ready_open_more_increases_pext": 0.0,
+    "pre_ready_open_more_trials": 3.0,
+    "pre_ready_hold_open_increases_pext": 0.0,
+    "pre_ready_hold_open_trials": 3.0,
+    "pre_ready_extract_success": 0.0,
+    "pre_ready_extract_trials": 3.0,
+    "pre_ready_wait_extract_success": 0.0,
+    "pre_ready_wait_trials": 3.0,
+    "post_ready_extract_success": 0.0,
+    "post_ready_extract_trials": 0.0,
+    "post_ready_open_more_low_gain": 0.0,
+    "post_ready_open_more_trials": 0.0,
+    "post_ready_hold_open_low_gain": 0.0,
+    "post_ready_hold_open_trials": 0.0
+  },
+  "median_metrics": {
+    "num_dense_frames": 10.0,
+    "num_keyframes": 0.0,
+    "phase_switch_rises": 0.0,
+    "phase_switch_falls": 0.0,
+    "ppre_cross_frame": -1.0,
+    "pext_cross_frame": -1.0,
+    "phase_cross_frame": -1.0,
+    "ready_cross_frame": -1.0,
+    "dense_boundary_error_frames": NaN,
+    "dense_boundary_error_fraction": NaN,
+    "key_boundary_error_keyframes": NaN,
+    "auroc_vret_ypre_three": NaN,
+    "auprc_vret_ypre_three": NaN,
+    "auroc_vret_ypre_full": NaN,
+    "auprc_vret_ypre_full": NaN,
+    "auroc_ppre_ypre": NaN,
+    "auprc_ppre_ypre": NaN,
+    "auroc_pext_yext": NaN,
+    "auprc_pext_yext": NaN,
+    "auroc_phase_yready": NaN,
+    "auprc_phase_yready": NaN,
+    "f1_phase_yready": NaN,
+    "baseline_auroc_door_yext": NaN,
+    "baseline_auprc_door_yext": NaN,
+    "baseline_auroc_time_yext": NaN,
+    "baseline_auprc_time_yext": NaN,
+    "baseline_auroc_whole_vis_yext": NaN,
+    "baseline_auprc_whole_vis_yext": NaN,
+    "pre_ready_open_more_increases_pext": 0.0,
+    "pre_ready_open_more_trials": 3.0,
+    "pre_ready_hold_open_increases_pext": 0.0,
+    "pre_ready_hold_open_trials": 3.0,
+    "pre_ready_extract_success": 0.0,
+    "pre_ready_extract_trials": 3.0,
+    "pre_ready_wait_extract_success": 0.0,
+    "pre_ready_wait_trials": 3.0,
+    "post_ready_extract_success": 0.0,
+    "post_ready_extract_trials": 0.0,
+    "post_ready_open_more_low_gain": 0.0,
+    "post_ready_open_more_trials": 0.0,
+    "post_ready_hold_open_low_gain": 0.0,
+    "post_ready_hold_open_trials": 0.0
+  },
+  "single_switch_rate": 0.0,
+  "reversion_rate": 0.0,
+  "ordering_ok_rate": 1.0
+}
+[CoppeliaSim:loadinfo]   done.
+[CoppeliaSim:loadinfo]   done.

code/rr_label_study/__init__.py

@@ -0,0 +1,3 @@
+from .oven_study import main, run_study
+
+__all__ = ["main", "run_study"]

code/rr_label_study/oven_study.py

@@ -0,0 +1,1222 @@
+import argparse
+import json
+import math
+import os
+import pickle
+from dataclasses import dataclass, asdict
+from pathlib import Path
+from typing import Dict, Iterable, List, Optional, Sequence, Tuple
+
+import numpy as np
+import pandas as pd
+from natsort import natsorted
+from PIL import Image
+from pyrep.const import ConfigurationPathAlgorithms as Algos
+from pyrep.errors import ConfigurationPathError
+from pyrep.objects.joint import Joint
+from pyrep.objects.shape import Shape
+from rlbench.action_modes.action_mode import BimanualJointPositionActionMode
+from rlbench.action_modes.gripper_action_modes import BimanualDiscrete
+from rlbench.backend.const import DEPTH_SCALE
+from rlbench.backend.utils import image_to_float_array, rgb_handles_to_mask
+from rlbench.bimanual_tasks.bimanual_take_tray_out_of_oven import (
+    BimanualTakeTrayOutOfOven,
+)
+from rlbench.demo import Demo
+from rlbench.environment import Environment
+from rlbench.observation_config import CameraConfig, ObservationConfig
+from sklearn.metrics import (
+    average_precision_score,
+    f1_score,
+    roc_auc_score,
+)
+
+
+FULL_CAMERA_SET = [
+    "front",
+    "overhead",
+    "wrist_right",
+    "wrist_left",
+    "over_shoulder_left",
+    "over_shoulder_right",
+]
+THREE_VIEW_SET = ["front", "wrist_right", "wrist_left"]
+DISPLAY = ":99"
+DEMO_DT = 0.05
+DEFAULT_IMAGE_SIZE = (128, 128)
+DEFAULT_PATH_SCALE = 0.75
+DEFAULT_VISIBILITY_TAU = 0.35
+DEFAULT_PEXT_TAU = 0.45
+DEFAULT_DOOR_SPEED_TAU = 0.08
+DEFAULT_PLAN_TRIALS = 48
+DEFAULT_PLAN_MAX_CONFIGS = 4
+DEFAULT_PLAN_MAX_TIME_MS = 10
+DEFAULT_PLAN_TRIALS_PER_GOAL = 4
+
+
+@dataclass
+class SimulatorSnapshot:
+    task_state: Tuple[bytes, int]
+    right_arm_tree: bytes
+    right_gripper_tree: bytes
+    left_arm_tree: bytes
+    left_gripper_tree: bytes
+    right_grasped: Tuple[str, ...]
+    left_grasped: Tuple[str, ...]
+
+
+@dataclass
+class ReplayState:
+    frame_index: int
+    tray_pose: np.ndarray
+    door_angle: float
+    right_gripper_pose: np.ndarray
+    left_gripper_pose: np.ndarray
+    right_gripper_open: float
+    left_gripper_open: float
+    snapshot: Optional[SimulatorSnapshot] = None
+
+
+@dataclass
+class MotionTemplates:
+    pregrasp_rel_pose: np.ndarray
+    grasp_rel_pose: np.ndarray
+    retreat_rel_poses: List[np.ndarray]
+    grasp_local_center: np.ndarray
+    grasp_region_extents: np.ndarray
+    hold_open_angle: float
+    open_more_delta: float
+    reference_tray_height: float
+
+    def to_json(self) -> Dict[str, object]:
+        return {
+            "pregrasp_rel_pose": self.pregrasp_rel_pose.tolist(),
+            "grasp_rel_pose": self.grasp_rel_pose.tolist(),
+            "retreat_rel_poses": [pose.tolist() for pose in self.retreat_rel_poses],
+            "grasp_local_center": self.grasp_local_center.tolist(),
+            "grasp_region_extents": self.grasp_region_extents.tolist(),
+            "hold_open_angle": float(self.hold_open_angle),
+            "open_more_delta": float(self.open_more_delta),
+            "reference_tray_height": float(self.reference_tray_height),
+        }
+
+
+@dataclass
+class EpisodeArtifacts:
+    episode_name: str
+    dense: pd.DataFrame
+    keyframes: pd.DataFrame
+    metrics: Dict[str, object]
+    template_frames: Dict[str, int]
+
+
+def _configure_runtime() -> None:
+    os.environ["DISPLAY"] = DISPLAY
+    os.environ["COPPELIASIM_ROOT"] = "/workspace/coppelia_sim"
+    ld_library_path = os.environ.get("LD_LIBRARY_PATH", "")
+    if "/workspace/coppelia_sim" not in ld_library_path:
+        os.environ["LD_LIBRARY_PATH"] = (
+            f"{ld_library_path}:/workspace/coppelia_sim"
+            if ld_library_path
+            else "/workspace/coppelia_sim"
+        )
+    os.environ["QT_QPA_PLATFORM_PLUGIN_PATH"] = "/workspace/coppelia_sim"
+    os.environ.setdefault("XDG_RUNTIME_DIR", "/tmp/runtime-root")
+
+
+def _minimal_camera_config() -> Dict[str, CameraConfig]:
+    return {
+        "front": CameraConfig(
+            rgb=False,
+            depth=False,
+            point_cloud=False,
+            mask=False,
+            image_size=DEFAULT_IMAGE_SIZE,
+        )
+    }
+
+
+def _make_observation_config() -> ObservationConfig:
+    return ObservationConfig(
+        camera_configs=_minimal_camera_config(),
+        joint_velocities=True,
+        joint_positions=True,
+        joint_forces=False,
+        gripper_open=True,
+        gripper_pose=True,
+        gripper_matrix=False,
+        gripper_joint_positions=True,
+        gripper_touch_forces=False,
+        task_low_dim_state=False,
+        robot_name="bimanual",
+    )
+
+
+def _launch_replay_env() -> Environment:
+    _configure_runtime()
+    env = Environment(
+        action_mode=BimanualJointPositionActionMode(),
+        obs_config=_make_observation_config(),
+        headless=True,
+        robot_setup="dual_panda",
+    )
+    env.launch()
+    return env
+
+
+def _load_demo(episode_dir: Path) -> Demo:
+    with episode_dir.joinpath("low_dim_obs.pkl").open("rb") as handle:
+        return pickle.load(handle)
+
+
+def _load_descriptions(episode_dir: Path) -> List[str]:
+    with episode_dir.joinpath("variation_descriptions.pkl").open("rb") as handle:
+        return pickle.load(handle)
+
+
+def _episode_dirs(dataset_root: Path) -> List[Path]:
+    episodes_dir = dataset_root.joinpath("all_variations", "episodes")
+    return [
+        episodes_dir.joinpath(name)
+        for name in natsorted(os.listdir(episodes_dir))
+        if episodes_dir.joinpath(name, "low_dim_obs.pkl").exists()
+    ]
+
+
+def _camera_file(episode_dir: Path, camera_name: str, kind: str, frame_index: int) -> Path:
+    if kind == "rgb":
+        return episode_dir.joinpath(f"{camera_name}_rgb", f"rgb_{frame_index:04d}.png")
+    if kind == "depth":
+        return episode_dir.joinpath(
+            f"{camera_name}_depth", f"depth_{frame_index:04d}.png"
+        )
+    if kind == "mask":
+        return episode_dir.joinpath(f"{camera_name}_mask", f"mask_{frame_index:04d}.png")
+    raise ValueError(f"unknown kind: {kind}")
+
+
+def _load_depth_meters(episode_dir: Path, demo: Demo, frame_index: int, camera_name: str) -> np.ndarray:
+    image = Image.open(_camera_file(episode_dir, camera_name, "depth", frame_index))
+    depth = image_to_float_array(image, DEPTH_SCALE)
+    near = demo[frame_index].misc[f"{camera_name}_camera_near"]
+    far = demo[frame_index].misc[f"{camera_name}_camera_far"]
+    return near + depth * (far - near)
+
+
+def _load_mask(episode_dir: Path, frame_index: int, camera_name: str) -> np.ndarray:
+    image = np.array(Image.open(_camera_file(episode_dir, camera_name, "mask", frame_index)))
+    return rgb_handles_to_mask(image)
+
+
+def _capture_snapshot(task) -> SimulatorSnapshot:
+    robot = task._scene.robot
+    return SimulatorSnapshot(
+        task_state=task._task.get_state(),
+        right_arm_tree=robot.right_arm.get_configuration_tree(),
+        right_gripper_tree=robot.right_gripper.get_configuration_tree(),
+        left_arm_tree=robot.left_arm.get_configuration_tree(),
+        left_gripper_tree=robot.left_gripper.get_configuration_tree(),
+        right_grasped=tuple(obj.get_name() for obj in robot.right_gripper.get_grasped_objects()),
+        left_grasped=tuple(obj.get_name() for obj in robot.left_gripper.get_grasped_objects()),
+    )
+
+
+def _restore_snapshot(task, snapshot: SimulatorSnapshot) -> None:
+    robot = task._scene.robot
+    robot.release_gripper()
+    task._pyrep.set_configuration_tree(snapshot.right_arm_tree)
+    task._pyrep.set_configuration_tree(snapshot.right_gripper_tree)
+    task._pyrep.set_configuration_tree(snapshot.left_arm_tree)
+    task._pyrep.set_configuration_tree(snapshot.left_gripper_tree)
+    task._pyrep.set_configuration_tree(snapshot.task_state[0])
+    for name in snapshot.right_grasped:
+        robot.right_gripper.grasp(Shape(name))
+    for name in snapshot.left_grasped:
+        robot.left_gripper.grasp(Shape(name))
+    task._pyrep.step()
+
+
+def _build_joint_action(target_obs) -> np.ndarray:
+    def _joint_vector(value, fallback) -> np.ndarray:
+        array = np.asarray(fallback if value is None else value, dtype=np.float64)
+        if array.ndim != 1 or array.shape[0] != 7:
+            array = np.asarray(fallback, dtype=np.float64)
+        if array.ndim != 1 or array.shape[0] != 7:
+            raise ValueError(f"invalid joint vector shape: {array.shape}")
+        return array
+
+    right = _joint_vector(
+        target_obs.misc.get("right_executed_demo_joint_position_action"),
+        target_obs.right.joint_positions,
+    )
+    left = _joint_vector(
+        target_obs.misc.get("left_executed_demo_joint_position_action"),
+        target_obs.left.joint_positions,
+    )
+    return np.concatenate(
+        [
+            right,
+            np.array([target_obs.right.gripper_open], dtype=np.float64),
+            left,
+            np.array([target_obs.left.gripper_open], dtype=np.float64),
+        ]
+    )
+
+
+class ReplayCache:
+    def __init__(self, task, demo: Demo, checkpoint_stride: int = 16):
+        self.task = task
+        self.demo = demo
+        self.checkpoint_stride = checkpoint_stride
+        self.current_index = 0
+        self.current_obs = None
+        self.checkpoints: Dict[int, SimulatorSnapshot] = {}
+        self.discrete_gripper = BimanualDiscrete()
+
+    def reset(self) -> None:
+        _, self.current_obs = self.task.reset_to_demo(self.demo)
+        self.current_index = 0
+        self.checkpoints = {0: _capture_snapshot(self.task)}
+
+    def step_to(self, target_index: int):
+        if target_index < self.current_index:
+            checkpoint_index = max(i for i in self.checkpoints if i <= target_index)
+            _restore_snapshot(self.task, self.checkpoints[checkpoint_index])
+            self.current_index = checkpoint_index
+            self.current_obs = self._observation_from_scene()
+        while self.current_index < target_index:
+            next_index = self.current_index + 1
+            self.task._action_mode.action(
+                self.task._scene, _build_joint_action(self.demo[next_index])
+            )
+            self._apply_gripper_replay(self.demo[next_index])
+            self.current_obs = self._observation_from_scene()
+            self.current_index = next_index
+            if self.current_index % self.checkpoint_stride == 0:
+                self.checkpoints[self.current_index] = _capture_snapshot(self.task)
+        return self.current_obs
+
+    def current_state(self) -> ReplayState:
+        return ReplayState(
+            frame_index=self.current_index,
+            tray_pose=Shape("tray").get_pose(),
+            door_angle=Joint("oven_door_joint").get_joint_position(),
+            right_gripper_pose=self.current_obs.right.gripper_pose.copy(),
+            left_gripper_pose=self.current_obs.left.gripper_pose.copy(),
+            right_gripper_open=float(self.current_obs.right.gripper_open),
+            left_gripper_open=float(self.current_obs.left.gripper_open),
+            snapshot=None,
+        )
+
+    def snapshot(self) -> SimulatorSnapshot:
+        return _capture_snapshot(self.task)
+
+    def restore(self, snapshot: SimulatorSnapshot) -> None:
+        _restore_snapshot(self.task, snapshot)
+        self.current_obs = self._observation_from_scene()
+
+    def restore_to_index(self, snapshot: SimulatorSnapshot, frame_index: int) -> None:
+        self.restore(snapshot)
+        self.current_index = frame_index
+
+    def _observation_from_scene(self):
+        return self.task._scene.get_observation()
+
+    def _apply_gripper_replay(self, target_obs) -> None:
+        desired = np.array(
+            [target_obs.right.gripper_open, target_obs.left.gripper_open],
+            dtype=np.float64,
+        )
+        current = np.array(
+            [
+                float(all(x > 0.9 for x in self.task._scene.robot.right_gripper.get_open_amount())),
+                float(all(x > 0.9 for x in self.task._scene.robot.left_gripper.get_open_amount())),
+            ],
+            dtype=np.float64,
+        )
+        if not np.allclose(current, desired):
+            self.discrete_gripper.action(self.task._scene, desired)
+            self.current_obs = self._observation_from_scene()
+
+
+def _quat_to_matrix(quat: Sequence[float]) -> np.ndarray:
+    x, y, z, w = quat
+    xx, yy, zz = x * x, y * y, z * z
+    xy, xz, yz = x * y, x * z, y * z
+    wx, wy, wz = w * x, w * y, w * z
+    return np.array(
+        [
+            [1 - 2 * (yy + zz), 2 * (xy - wz), 2 * (xz + wy)],
+            [2 * (xy + wz), 1 - 2 * (xx + zz), 2 * (yz - wx)],
+            [2 * (xz - wy), 2 * (yz + wx), 1 - 2 * (xx + yy)],
+        ],
+        dtype=np.float64,
+    )
+
+
+def _matrix_to_quat(matrix: np.ndarray) -> np.ndarray:
+    trace = np.trace(matrix)
+    if trace > 0.0:
+        s = math.sqrt(trace + 1.0) * 2.0
+        w = 0.25 * s
+        x = (matrix[2, 1] - matrix[1, 2]) / s
+        y = (matrix[0, 2] - matrix[2, 0]) / s
+        z = (matrix[1, 0] - matrix[0, 1]) / s
+    elif matrix[0, 0] > matrix[1, 1] and matrix[0, 0] > matrix[2, 2]:
+        s = math.sqrt(1.0 + matrix[0, 0] - matrix[1, 1] - matrix[2, 2]) * 2.0
+        w = (matrix[2, 1] - matrix[1, 2]) / s
+        x = 0.25 * s
+        y = (matrix[0, 1] + matrix[1, 0]) / s
+        z = (matrix[0, 2] + matrix[2, 0]) / s
+    elif matrix[1, 1] > matrix[2, 2]:
+        s = math.sqrt(1.0 + matrix[1, 1] - matrix[0, 0] - matrix[2, 2]) * 2.0
+        w = (matrix[0, 2] - matrix[2, 0]) / s
+        x = (matrix[0, 1] + matrix[1, 0]) / s
+        y = 0.25 * s
+        z = (matrix[1, 2] + matrix[2, 1]) / s
+    else:
+        s = math.sqrt(1.0 + matrix[2, 2] - matrix[0, 0] - matrix[1, 1]) * 2.0
+        w = (matrix[1, 0] - matrix[0, 1]) / s
+        x = (matrix[0, 2] + matrix[2, 0]) / s
+        y = (matrix[1, 2] + matrix[2, 1]) / s
+        z = 0.25 * s
+    quat = np.array([x, y, z, w], dtype=np.float64)
+    return quat / np.linalg.norm(quat)
+
+
+def _pose_to_matrix(pose: Sequence[float]) -> np.ndarray:
+    matrix = np.eye(4, dtype=np.float64)
+    matrix[:3, :3] = _quat_to_matrix(pose[3:])
+    matrix[:3, 3] = pose[:3]
+    return matrix
+
+
+def _matrix_to_pose(matrix: np.ndarray) -> np.ndarray:
+    return np.concatenate([matrix[:3, 3], _matrix_to_quat(matrix[:3, :3])], axis=0)
+
+
+def _relative_pose(reference_pose: Sequence[float], target_pose: Sequence[float]) -> np.ndarray:
+    reference = _pose_to_matrix(reference_pose)
+    target = _pose_to_matrix(target_pose)
+    rel = np.linalg.inv(reference) @ target
+    return _matrix_to_pose(rel)
+
+
+def _apply_relative_pose(reference_pose: Sequence[float], relative_pose: Sequence[float]) -> np.ndarray:
+    reference = _pose_to_matrix(reference_pose)
+    relative = _pose_to_matrix(relative_pose)
+    world = reference @ relative
+    return _matrix_to_pose(world)
+
+
+def _world_to_local(reference_pose: Sequence[float], point_world: Sequence[float]) -> np.ndarray:
+    reference = _pose_to_matrix(reference_pose)
+    point = np.concatenate([np.asarray(point_world, dtype=np.float64), [1.0]])
+    return (np.linalg.inv(reference) @ point)[:3]
+
+
+def _local_to_world(reference_pose: Sequence[float], point_local: Sequence[float]) -> np.ndarray:
+    reference = _pose_to_matrix(reference_pose)
+    point = np.concatenate([np.asarray(point_local, dtype=np.float64), [1.0]])
+    return (reference @ point)[:3]
+
+
+def _first_transition(demo: Demo, side: str, open_to_closed: bool) -> int:
+    values = [getattr(demo[i], side).gripper_open for i in range(len(demo))]
+    for i in range(1, len(values)):
+        if open_to_closed and values[i - 1] > 0.5 and values[i] < 0.5:
+            return i
+        if not open_to_closed and values[i - 1] < 0.5 and values[i] > 0.5:
+            return i
+    raise RuntimeError(f"no gripper transition found for {side}")
+
+
+def _derive_templates(dataset_root: Path, template_episode_dir: Path) -> Tuple[MotionTemplates, Dict[str, int]]:
+    env = _launch_replay_env()
+    try:
+        demo = _load_demo(template_episode_dir)
+        task = env.get_task(BimanualTakeTrayOutOfOven)
+        cache = ReplayCache(task, demo, checkpoint_stride=8)
+        cache.reset()
+        base_pose = task._task.get_base().get_pose()
+
+        left_close = _first_transition(demo, "left", open_to_closed=True)
+        left_open = _first_transition(demo, "left", open_to_closed=False)
+        pregrasp_index = max(0, left_close - 5)
+        right_close = _first_transition(demo, "right", open_to_closed=True)
+        right_open = _first_transition(demo, "right", open_to_closed=False)
+
+        interesting = sorted(
+            {
+                pregrasp_index,
+                left_close,
+                min(len(demo) - 1, left_close + 10),
+                min(len(demo) - 1, left_close + 20),
+                min(len(demo) - 1, left_close + 30),
+                max(left_close + 1, left_open - 20),
+                max(left_close + 1, left_open - 10),
+                max(left_close + 1, left_open - 5),
+                right_close,
+                right_open,
+                left_open,
+            }
+        )
+        states: Dict[int, ReplayState] = {}
+        for frame_index in interesting:
+            cache.step_to(frame_index)
+            states[frame_index] = cache.current_state()
+
+        pregrasp_rel_pose = _relative_pose(
+            states[pregrasp_index].tray_pose, states[pregrasp_index].left_gripper_pose
+        )
+        grasp_rel_pose = _relative_pose(
+            states[left_close].tray_pose, states[left_close].left_gripper_pose
+        )
+        retreat_rel_poses = [
+            _relative_pose(base_pose, states[index].left_gripper_pose)
+            for index in interesting
+            if index > left_close
+        ]
+        grasp_local_center = _world_to_local(
+            states[left_close].tray_pose, states[left_close].left_gripper_pose[:3]
+        )
+        templates = MotionTemplates(
+            pregrasp_rel_pose=pregrasp_rel_pose,
+            grasp_rel_pose=grasp_rel_pose,
+            retreat_rel_poses=retreat_rel_poses,
+            grasp_local_center=grasp_local_center,
+            grasp_region_extents=np.array([0.03, 0.015, 0.004], dtype=np.float64),
+            hold_open_angle=float(states[right_open].door_angle),
+            open_more_delta=max(
+                0.12,
+                float(states[right_open].door_angle - states[right_close].door_angle) * 0.25,
+            ),
+            reference_tray_height=float(states[left_close].tray_pose[2]),
+        )
+        template_frames = {
+            "pregrasp": pregrasp_index,
+            "grasp": left_close,
+            "right_close": right_close,
+            "right_open": right_open,
+        }
+        return templates, template_frames
+    finally:
+        env.shutdown()
+
+
+def _camera_projection(extrinsics: np.ndarray, intrinsics: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    camera_pos = extrinsics[:3, 3:4]
+    rotation = extrinsics[:3, :3]
+    world_to_camera = np.concatenate([rotation.T, -(rotation.T @ camera_pos)], axis=1)
+    projection = intrinsics @ world_to_camera
+    return projection, world_to_camera
+
+
+def _project_points(points_world: np.ndarray, extrinsics: np.ndarray, intrinsics: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    projection, world_to_camera = _camera_projection(extrinsics, intrinsics)
+    homogeneous = np.concatenate([points_world, np.ones((len(points_world), 1))], axis=1)
+    camera_xyz = (world_to_camera @ homogeneous.T).T
+    image_xyz = (projection @ homogeneous.T).T
+    uv = image_xyz[:, :2] / image_xyz[:, 2:3]
+    return uv, camera_xyz
+
+
+def _sample_grasp_points(templates: MotionTemplates, tray_pose: np.ndarray) -> np.ndarray:
+    center = templates.grasp_local_center
+    extents = templates.grasp_region_extents
+    xs = np.linspace(center[0] - extents[0], center[0] + extents[0], 9)
+    ys = np.linspace(center[1] - extents[1], center[1] + extents[1], 5)
+    zs = np.linspace(center[2] - extents[2], center[2] + extents[2], 3)
+    points_local = np.array([[x, y, z] for x in xs for y in ys for z in zs], dtype=np.float64)
+    return np.array([_local_to_world(tray_pose, point) for point in points_local], dtype=np.float64)
+
+
+def _sample_full_tray_points(tray_pose: np.ndarray) -> np.ndarray:
+    tray = Shape("tray")
+    bbox = np.asarray(tray.get_bounding_box(), dtype=np.float64)
+    xs = np.linspace(bbox[0], bbox[1], 10)
+    ys = np.linspace(bbox[2], bbox[3], 12)
+    zs = np.linspace(bbox[4], bbox[5], 3)
+    points_local = np.array([[x, y, z] for x in xs for y in ys for z in zs], dtype=np.float64)
+    return np.array([_local_to_world(tray_pose, point) for point in points_local], dtype=np.float64)
+
+
+def _visibility_ratio(
+    points_world: np.ndarray,
+    depth_m: np.ndarray,
+    mask: np.ndarray,
+    tray_handle: int,
+    extrinsics: np.ndarray,
+    intrinsics: np.ndarray,
+    depth_tol: float = 0.02,
+) -> float:
+    uv, camera_xyz = _project_points(points_world, extrinsics, intrinsics)
+    height, width = depth_m.shape
+    visible = 0
+    total = 0
+    for (u, v), (_, _, camera_depth) in zip(uv, camera_xyz):
+        if camera_depth <= 0:
+            continue
+        if not (0 <= u < width and 0 <= v < height):
+            continue
+        total += 1
+        px = int(round(u))
+        py = int(round(v))
+        px = min(max(px, 0), width - 1)
+        py = min(max(py, 0), height - 1)
+        observed = float(depth_m[py, px])
+        if abs(observed - camera_depth) <= depth_tol:
+            visible += 1
+    return float(visible / total) if total else 0.0
+
+
+def _union_visibility(values: Iterable[float]) -> float:
+    product = 1.0
+    for value in values:
+        product *= 1.0 - float(value)
+    return 1.0 - product
+
+
+def _keypoint_discovery(demo: Demo, stopping_delta: float = 0.1) -> List[int]:
+    keypoints: List[int] = []
+    right_prev = demo[0].right.gripper_open
+    left_prev = demo[0].left.gripper_open
+    stopped_buffer = 0
+    for i, obs in enumerate(demo._observations):
+        if i < 2 or i >= len(demo) - 1:
+            right_stopped = left_stopped = False
+        else:
+            right_stopped = (
+                np.allclose(obs.right.joint_velocities, 0, atol=stopping_delta)
+                and obs.right.gripper_open == demo[i + 1].right.gripper_open
+                and obs.right.gripper_open == demo[i - 1].right.gripper_open
+                and demo[i - 2].right.gripper_open == demo[i - 1].right.gripper_open
+            )
+            left_stopped = (
+                np.allclose(obs.left.joint_velocities, 0, atol=stopping_delta)
+                and obs.left.gripper_open == demo[i + 1].left.gripper_open
+                and obs.left.gripper_open == demo[i - 1].left.gripper_open
+                and demo[i - 2].left.gripper_open == demo[i - 1].left.gripper_open
+            )
+        stopped = stopped_buffer <= 0 and right_stopped and left_stopped
+        stopped_buffer = 4 if stopped else stopped_buffer - 1
+        last = i == len(demo) - 1
+        state_changed = (
+            obs.right.gripper_open != right_prev or obs.left.gripper_open != left_prev
+        )
+        if i != 0 and (state_changed or last or stopped):
+            keypoints.append(i)
+        right_prev = obs.right.gripper_open
+        left_prev = obs.left.gripper_open
+    if len(keypoints) > 1 and (keypoints[-1] - 1) == keypoints[-2]:
+        keypoints.pop(-2)
+    return keypoints
+
+
+def _plan_path(scene, arm_name: str, pose: np.ndarray, ignore_collisions: bool = False):
+    arm = scene.robot.left_arm if arm_name == "left" else scene.robot.right_arm
+    try:
+        return arm.get_path(
+            pose[:3],
+            quaternion=pose[3:],
+            ignore_collisions=ignore_collisions,
+            trials=DEFAULT_PLAN_TRIALS,
+            max_configs=DEFAULT_PLAN_MAX_CONFIGS,
+            max_time_ms=DEFAULT_PLAN_MAX_TIME_MS,
+            trials_per_goal=DEFAULT_PLAN_TRIALS_PER_GOAL,
+            algorithm=Algos.RRTConnect,
+        )
+    except Exception:
+        return None
+
+
+def _path_length(path) -> float:
+    if path is None:
+        return math.inf
+    try:
+        return float(path._get_path_point_lengths()[-1])
+    except Exception:
+        return math.inf
+
+
+def _pregrasp_candidates(tray_pose: np.ndarray, templates: MotionTemplates) -> List[np.ndarray]:
+    candidates = []
+    base = _apply_relative_pose(tray_pose, templates.pregrasp_rel_pose)
+    candidates.append(base)
+    for dx in (-0.02, 0.02):
+        perturbed = base.copy()
+        perturbed[0] += dx
+        candidates.append(perturbed)
+    return candidates
+
+
+def _extract_sequence_poses(
+    tray_pose: np.ndarray, task_base_pose: np.ndarray, templates: MotionTemplates
+) -> List[np.ndarray]:
+    poses = [
+        _apply_relative_pose(tray_pose, templates.pregrasp_rel_pose),
+        _apply_relative_pose(tray_pose, templates.grasp_rel_pose),
+    ]
+    poses.extend(
+        _apply_relative_pose(task_base_pose, pose) for pose in templates.retreat_rel_poses[:3]
+    )
+    return poses
+
+
+def _extract_height_threshold(templates: MotionTemplates) -> float:
+    return templates.reference_tray_height + 0.06
+
+
+def _extraction_progress_score(current_height: float, templates: MotionTemplates) -> float:
+    threshold = _extract_height_threshold(templates)
+    margin = max(0.0, float(current_height) - threshold)
+    # Saturate smoothly once the tray is clearly lifted above the oven lip.
+    return min(1.0, 0.8 + margin / 0.12)
+
+
+def _pregrasp_score_and_success(task, templates: MotionTemplates) -> Tuple[float, bool]:
+    tray = Shape("tray")
+    if any(
+        grasped.get_name() == tray.get_name()
+        for grasped in task._scene.robot.left_gripper.get_grasped_objects()
+    ):
+        return 1.0, True
+    tray_pose = Shape("tray").get_pose()
+    best = 0.0
+    success = False
+    for pose in _pregrasp_candidates(tray_pose, templates):
+        path = _plan_path(task._scene, "left", pose, ignore_collisions=False)
+        length = _path_length(path)
+        if np.isfinite(length):
+            success = True
+            best = max(best, math.exp(-length / DEFAULT_PATH_SCALE))
+    return best, success
+
+
+def _extract_score_and_success(task, templates: MotionTemplates) -> Tuple[float, bool]:
+    tray = Shape("tray")
+    robot = task._scene.robot
+    snapshot = _capture_snapshot(task)
+    try:
+        total_length = 0.0
+        current_height = float(tray.get_position()[2])
+        already_grasped = any(
+            grasped.get_name() == tray.get_name()
+            for grasped in robot.left_gripper.get_grasped_objects()
+        )
+        if already_grasped and current_height >= _extract_height_threshold(templates):
+            return _extraction_progress_score(current_height, templates), True
+        poses = _extract_sequence_poses(
+            tray.get_pose(), task._task.get_base().get_pose(), templates
+        )
+        approach_poses = [] if already_grasped else poses[:2]
+        retreat_poses = poses[2:] if not already_grasped else poses[2:]
+
+        for pose in approach_poses:
+            path = _plan_path(task._scene, "left", pose, ignore_collisions=False)
+            if path is None:
+                return 0.0, False
+            total_length += _path_length(path)
+            path.set_to_end(disable_dynamics=True)
+            task._pyrep.step()
+        if not already_grasped:
+            robot.left_gripper.grasp(tray)
+        for pose in retreat_poses:
+            path = _plan_path(task._scene, "left", pose, ignore_collisions=True)
+            if path is None:
+                return 0.0, False
+            total_length += _path_length(path)
+            path.set_to_end(disable_dynamics=True)
+            task._pyrep.step()
+        final_height = float(tray.get_position()[2])
+        success = final_height >= _extract_height_threshold(templates)
+        score = max(
+            math.exp(-total_length / (DEFAULT_PATH_SCALE * 2.5)),
+            _extraction_progress_score(final_height, templates) if success else 0.0,
+        )
+        return score, bool(success)
+    finally:
+        _restore_snapshot(task, snapshot)
+
+
+def _wait_branch(task, steps: int = 5) -> None:
+    for _ in range(steps):
+        task._scene.step()
+
+
+def _open_more_branch(task, templates: MotionTemplates) -> None:
+    joint = Joint("oven_door_joint")
+    current = joint.get_joint_position()
+    joint.set_joint_position(current - templates.open_more_delta, disable_dynamics=True)
+    for _ in range(3):
+        task._pyrep.step()
+
+
+def _hold_open_branch(task, templates: MotionTemplates) -> None:
+    joint = Joint("oven_door_joint")
+    current = joint.get_joint_position()
+    joint.set_joint_position(min(current, templates.hold_open_angle), disable_dynamics=True)
+    for _ in range(3):
+        task._pyrep.step()
+
+
+def _frame_metrics(
+    episode_dir: Path,
+    demo: Demo,
+    frame_state: ReplayState,
+    templates: MotionTemplates,
+    tray_handle: int,
+) -> Dict[str, float]:
+    grasp_points = _sample_grasp_points(templates, frame_state.tray_pose)
+    full_tray_points = _sample_full_tray_points(frame_state.tray_pose)
+    camera_values: Dict[str, Dict[str, float]] = {}
+    for camera_name in FULL_CAMERA_SET:
+        depth_m = _load_depth_meters(episode_dir, demo, frame_state.frame_index, camera_name)
+        mask = _load_mask(episode_dir, frame_state.frame_index, camera_name)
+        extrinsics = demo[frame_state.frame_index].misc[f"{camera_name}_camera_extrinsics"]
+        intrinsics = demo[frame_state.frame_index].misc[f"{camera_name}_camera_intrinsics"]
+        camera_values[camera_name] = {
+            "grasp_visibility": _visibility_ratio(
+                grasp_points, depth_m, mask, tray_handle, extrinsics, intrinsics
+            ),
+            "tray_visibility": _visibility_ratio(
+                full_tray_points, depth_m, mask, tray_handle, extrinsics, intrinsics
+            ),
+        }
+
+    values: Dict[str, float] = {}
+    for name, camera_set in {"three_view": THREE_VIEW_SET, "full_view": FULL_CAMERA_SET}.items():
+        values[f"{name}_visibility"] = _union_visibility(
+            camera_values[camera_name]["grasp_visibility"] for camera_name in camera_set
+        )
+        values[f"{name}_whole_tray_visibility"] = _union_visibility(
+            camera_values[camera_name]["tray_visibility"] for camera_name in camera_set
+        )
+    return values
+
+
+def _compute_frame_row_isolated(
+    episode_dir: Path,
+    demo: Demo,
+    templates: MotionTemplates,
+    checkpoint_stride: int,
+    frame_index: int,
+) -> Dict[str, float]:
+    env = _launch_replay_env()
+    try:
+        task = env.get_task(BimanualTakeTrayOutOfOven)
+        cache = ReplayCache(task, demo, checkpoint_stride=checkpoint_stride)
+        cache.reset()
+        cache.step_to(frame_index)
+        state = cache.current_state()
+        visibility = _frame_metrics(episode_dir, demo, state, templates, Shape("tray").get_handle())
+        p_pre, y_pre = _pregrasp_score_and_success(task, templates)
+        p_ext, y_ext = _extract_score_and_success(task, templates)
+        return {
+            "frame_index": frame_index,
+            "time_norm": frame_index / max(1, len(demo) - 1),
+            "door_angle": state.door_angle,
+            "right_gripper_open": state.right_gripper_open,
+            "left_gripper_open": state.left_gripper_open,
+            "p_pre": p_pre,
+            "p_ext": p_ext,
+            "y_pre": float(bool(y_pre)),
+            "y_ext": float(bool(y_ext)),
+            **visibility,
+        }
+    finally:
+        env.shutdown()
+
+
+def _safe_auc(y_true: np.ndarray, y_score: np.ndarray) -> float:
+    if len(np.unique(y_true)) < 2:
+        return float("nan")
+    return float(roc_auc_score(y_true, y_score))
+
+
+def _safe_auprc(y_true: np.ndarray, y_score: np.ndarray) -> float:
+    if len(np.unique(y_true)) < 2:
+        return float("nan")
+    return float(average_precision_score(y_true, y_score))
+
+
+def _first_crossing(values: np.ndarray, threshold: float) -> int:
+    above = np.flatnonzero(values >= threshold)
+    return int(above[0]) if len(above) else -1
+
+
+def _transition_count(binary_values: np.ndarray) -> Tuple[int, int]:
+    diffs = np.diff(binary_values.astype(int))
+    return int(np.sum(diffs == 1)), int(np.sum(diffs == -1))
+
+
+def _keyframe_subset(frame_df: pd.DataFrame, keyframes: Sequence[int]) -> pd.DataFrame:
+    key_df = frame_df.iloc[list(keyframes)].copy()
+    key_df["keyframe_ordinal"] = np.arange(len(key_df))
+    return key_df
+
+
+def _interventional_validity(
+    task,
+    cache: ReplayCache,
+    episode_dir: Path,
+    demo: Demo,
+    templates: MotionTemplates,
+    tray_handle: int,
+    frame_df: pd.DataFrame,
+) -> Dict[str, float]:
+    ready_indices = np.flatnonzero(frame_df["y_ready"].to_numpy(dtype=bool))
+    ready_onset = int(ready_indices[0]) if len(ready_indices) else len(frame_df) // 2
+    initial_snapshot = cache.snapshot()
+    sample_indices = sorted(
+        {
+            max(0, ready_onset - 10),
+            max(0, ready_onset - 5),
+            ready_onset,
+            min(len(frame_df) - 1, ready_onset + 20),
+        }
+    )
+    stats = {
+        "pre_ready_open_more_increases_pext": 0,
+        "pre_ready_open_more_trials": 0,
+        "pre_ready_hold_open_increases_pext": 0,
+        "pre_ready_hold_open_trials": 0,
+        "pre_ready_extract_success": 0,
+        "pre_ready_extract_trials": 0,
+        "pre_ready_wait_extract_success": 0,
+        "pre_ready_wait_trials": 0,
+        "post_ready_extract_success": 0,
+        "post_ready_extract_trials": 0,
+        "post_ready_open_more_low_gain": 0,
+        "post_ready_open_more_trials": 0,
+        "post_ready_hold_open_low_gain": 0,
+        "post_ready_hold_open_trials": 0,
+    }
+    for frame_index in sample_indices:
+        cache.restore_to_index(initial_snapshot, 0)
+        cache.step_to(frame_index)
+        snapshot = cache.snapshot()
+        base_pext, base_extract_success = _extract_score_and_success(task, templates)
+        pre_ready = not bool(frame_df.iloc[frame_index]["y_ready"])
+
+        _restore_snapshot(task, snapshot)
+        _open_more_branch(task, templates)
+        open_pext, _ = _extract_score_and_success(task, templates)
+
+        _restore_snapshot(task, snapshot)
+        _hold_open_branch(task, templates)
+        hold_pext, _ = _extract_score_and_success(task, templates)
+
+        _restore_snapshot(task, snapshot)
+        _wait_branch(task)
+        _, wait_extract_success = _extract_score_and_success(task, templates)
+        _restore_snapshot(task, snapshot)
+
+        if pre_ready:
+            stats["pre_ready_open_more_trials"] += 1
+            stats["pre_ready_hold_open_trials"] += 1
+            stats["pre_ready_extract_trials"] += 1
+            stats["pre_ready_wait_trials"] += 1
+            if open_pext > base_pext:
+                stats["pre_ready_open_more_increases_pext"] += 1
+            if hold_pext > base_pext:
+                stats["pre_ready_hold_open_increases_pext"] += 1
+            if base_extract_success:
+                stats["pre_ready_extract_success"] += 1
+            if wait_extract_success:
+                stats["pre_ready_wait_extract_success"] += 1
+        else:
+            stats["post_ready_extract_trials"] += 1
+            stats["post_ready_open_more_trials"] += 1
+            stats["post_ready_hold_open_trials"] += 1
+            if base_extract_success:
+                stats["post_ready_extract_success"] += 1
+            if (open_pext - base_pext) <= 0.05:
+                stats["post_ready_open_more_low_gain"] += 1
+            if (hold_pext - base_pext) <= 0.05:
+                stats["post_ready_hold_open_low_gain"] += 1
+    return {
+        key: float(value) for key, value in stats.items()
+    }
+
+
+def _analyze_episode(
+    dataset_root: Path,
+    episode_dir: Path,
+    templates: MotionTemplates,
+    template_frames: Dict[str, int],
+    checkpoint_stride: int = 16,
+    max_frames: Optional[int] = None,
+    independent_replay: bool = False,
+) -> EpisodeArtifacts:
+    env = _launch_replay_env()
+    try:
+        demo = _load_demo(episode_dir)
+        descriptions = _load_descriptions(episode_dir)
+        task = env.get_task(BimanualTakeTrayOutOfOven)
+        cache = ReplayCache(task, demo, checkpoint_stride=checkpoint_stride)
+        cache.reset()
+        tray_handle = Shape("tray").get_handle()
+        num_frames = len(demo) if max_frames is None else min(len(demo), max_frames)
+
+        rows: List[Dict[str, float]] = []
+        door_angles: List[float] = []
+        initial_snapshot = cache.checkpoints[0] if independent_replay else None
+        for frame_index in range(num_frames):
+            if independent_replay:
+                cache.restore_to_index(initial_snapshot, 0)
+            cache.step_to(frame_index)
+            frame_snapshot = cache.snapshot() if not independent_replay else None
+            state = cache.current_state()
+            door_angles.append(state.door_angle)
+            visibility = _frame_metrics(episode_dir, demo, state, templates, tray_handle)
+            p_pre, y_pre = _pregrasp_score_and_success(task, templates)
+            p_ext, y_ext = _extract_score_and_success(task, templates)
+            rows.append(
+                {
+                    "frame_index": frame_index,
+                    "time_norm": frame_index / max(1, num_frames - 1),
+                    "door_angle": state.door_angle,
+                    "right_gripper_open": state.right_gripper_open,
+                    "left_gripper_open": state.left_gripper_open,
+                    "p_pre": p_pre,
+                    "p_ext": p_ext,
+                    "y_pre": float(bool(y_pre)),
+                    "y_ext": float(bool(y_ext)),
+                    **visibility,
+                }
+            )
+            if frame_snapshot is not None:
+                cache.restore(frame_snapshot)
+            if (frame_index + 1) % 25 == 0 or (frame_index + 1) == num_frames:
+                print(
+                    f"[{episode_dir.name}] analyzed {frame_index + 1}/{num_frames} dense frames",
+                    flush=True,
+                )
+
+        frame_df = pd.DataFrame(rows)
+        door_speed = np.gradient(frame_df["door_angle"].to_numpy(), DEMO_DT)
+        frame_df["door_speed_abs"] = np.abs(door_speed)
+        y_ext_binary = frame_df["y_ext"].to_numpy(dtype=bool)
+        y_ready = np.zeros(len(frame_df), dtype=bool)
+        for i in range(len(frame_df)):
+            window = y_ext_binary[i : i + 3]
+            if len(window) == 3 and np.all(window) and frame_df.iloc[i]["door_speed_abs"] <= DEFAULT_DOOR_SPEED_TAU:
+                y_ready[i] = True
+        if np.any(y_ready):
+            first_ready = int(np.flatnonzero(y_ready)[0])
+            y_ready[first_ready:] = True
+        frame_df["y_ready"] = y_ready.astype(float)
+        phase_trigger = (
+            (frame_df["full_view_visibility"] >= DEFAULT_VISIBILITY_TAU)
+            & (frame_df["p_ext"] >= DEFAULT_PEXT_TAU)
+            & (frame_df["door_speed_abs"] <= DEFAULT_DOOR_SPEED_TAU)
+        ).to_numpy(dtype=bool)
+        phase_switch = np.zeros(len(frame_df), dtype=bool)
+        if np.any(phase_trigger):
+            first_phase = int(np.flatnonzero(phase_trigger)[0])
+            phase_switch[first_phase:] = True
+        frame_df["phase_switch"] = phase_switch.astype(float)
+
+        keyframes = [index for index in _keypoint_discovery(demo) if index < num_frames]
+        key_df = _keyframe_subset(frame_df, keyframes)
+
+        y_pre_arr = frame_df["y_pre"].to_numpy(dtype=int)
+        y_ext_arr = frame_df["y_ext"].to_numpy(dtype=int)
+        y_ready_arr = frame_df["y_ready"].to_numpy(dtype=int)
+        p_pre_arr = frame_df["p_pre"].to_numpy(dtype=float)
+        p_ext_arr = frame_df["p_ext"].to_numpy(dtype=float)
+        phase_arr = frame_df["phase_switch"].to_numpy(dtype=int)
+        full_vis = frame_df["full_view_visibility"].to_numpy(dtype=float)
+        whole_vis = frame_df["full_view_whole_tray_visibility"].to_numpy(dtype=float)
+        door_angle_arr = frame_df["door_angle"].to_numpy(dtype=float)
+        time_arr = frame_df["time_norm"].to_numpy(dtype=float)
+
+        ppre_cross = _first_crossing(p_pre_arr, DEFAULT_PEXT_TAU)
+        pext_cross = _first_crossing(p_ext_arr, DEFAULT_PEXT_TAU)
+        phase_cross = _first_crossing(frame_df["phase_switch"].to_numpy(dtype=float), 0.5)
+        ready_cross = _first_crossing(y_ready_arr.astype(float), 0.5)
+        phase_rises, phase_falls = _transition_count(phase_arr)
+
+        key_phase_cross = _first_crossing(key_df["phase_switch"].to_numpy(dtype=float), 0.5)
+        key_ready_cross = _first_crossing(key_df["y_ready"].to_numpy(dtype=float), 0.5)
+
+        interventions = _interventional_validity(
+            task, cache, episode_dir, demo, templates, tray_handle, frame_df
+        )
+        metrics = {
+            "episode_name": episode_dir.name,
+            "description": descriptions[0],
+            "num_dense_frames": int(num_frames),
+            "num_keyframes": int(len(key_df)),
+            "phase_switch_rises": int(phase_rises),
+            "phase_switch_falls": int(phase_falls),
+            "ppre_cross_frame": int(ppre_cross),
+            "pext_cross_frame": int(pext_cross),
+            "phase_cross_frame": int(phase_cross),
+            "ready_cross_frame": int(ready_cross),
+            "ordering_ok": bool(ppre_cross == -1 or pext_cross == -1 or ppre_cross <= pext_cross),
+            "dense_boundary_error_frames": float(abs(phase_cross - ready_cross))
+            if phase_cross >= 0 and ready_cross >= 0
+            else float("nan"),
+            "dense_boundary_error_fraction": float(abs(phase_cross - ready_cross) / len(frame_df))
+            if phase_cross >= 0 and ready_cross >= 0
+            else float("nan"),
+            "key_boundary_error_keyframes": float(abs(key_phase_cross - key_ready_cross))
+            if key_phase_cross >= 0 and key_ready_cross >= 0
+            else float("nan"),
+            "auroc_vret_ypre_three": _safe_auc(y_pre_arr, frame_df["three_view_visibility"].to_numpy(dtype=float)),
+            "auprc_vret_ypre_three": _safe_auprc(y_pre_arr, frame_df["three_view_visibility"].to_numpy(dtype=float)),
+            "auroc_vret_ypre_full": _safe_auc(y_pre_arr, frame_df["full_view_visibility"].to_numpy(dtype=float)),
+            "auprc_vret_ypre_full": _safe_auprc(y_pre_arr, frame_df["full_view_visibility"].to_numpy(dtype=float)),
+            "auroc_ppre_ypre": _safe_auc(y_pre_arr, p_pre_arr),
+            "auprc_ppre_ypre": _safe_auprc(y_pre_arr, p_pre_arr),
+            "auroc_pext_yext": _safe_auc(y_ext_arr, p_ext_arr),
+            "auprc_pext_yext": _safe_auprc(y_ext_arr, p_ext_arr),
+            "auroc_phase_yready": _safe_auc(y_ready_arr, frame_df["phase_switch"].to_numpy(dtype=float)),
+            "auprc_phase_yready": _safe_auprc(y_ready_arr, frame_df["phase_switch"].to_numpy(dtype=float)),
+            "f1_phase_yready": float(f1_score(y_ready_arr, phase_arr))
+            if np.any(y_ready_arr) and np.any(phase_arr)
+            else float("nan"),
+            "baseline_auroc_door_yext": _safe_auc(y_ext_arr, door_angle_arr),
+            "baseline_auprc_door_yext": _safe_auprc(y_ext_arr, door_angle_arr),
+            "baseline_auroc_time_yext": _safe_auc(y_ext_arr, time_arr),
+            "baseline_auprc_time_yext": _safe_auprc(y_ext_arr, time_arr),
+            "baseline_auroc_whole_vis_yext": _safe_auc(y_ext_arr, whole_vis),
+            "baseline_auprc_whole_vis_yext": _safe_auprc(y_ext_arr, whole_vis),
+            **interventions,
+        }
+        return EpisodeArtifacts(
+            episode_name=episode_dir.name,
+            dense=frame_df,
+            keyframes=key_df,
+            metrics=metrics,
+            template_frames=template_frames,
+        )
+    finally:
+        env.shutdown()
+
+
+def _aggregate_summary(episode_metrics: List[Dict[str, object]]) -> Dict[str, object]:
+    frame = pd.DataFrame(episode_metrics)
+    numeric = frame.select_dtypes(include=[np.number])
+    summary = {
+        "num_episodes": int(len(frame)),
+        "mean_metrics": numeric.mean(numeric_only=True).to_dict(),
+        "median_metrics": numeric.median(numeric_only=True).to_dict(),
+        "single_switch_rate": float((frame["phase_switch_rises"] == 1).mean())
+        if len(frame)
+        else float("nan"),
+        "reversion_rate": float((frame["phase_switch_falls"] > 0).mean())
+        if len(frame)
+        else float("nan"),
+        "ordering_ok_rate": float(frame["ordering_ok"].mean()) if len(frame) else float("nan"),
+    }
+    return summary
+
+
+def run_study(
+    dataset_root: str,
+    result_dir: str,
+    max_episodes: Optional[int] = None,
+    checkpoint_stride: int = 16,
+    max_frames: Optional[int] = None,
+    episode_offset: int = 0,
+    template_episode_index: int = 0,
+    independent_replay: bool = False,
+) -> Dict[str, object]:
+    dataset_path = Path(dataset_root)
+    result_path = Path(result_dir)
+    result_path.mkdir(parents=True, exist_ok=True)
+
+    all_episode_dirs = _episode_dirs(dataset_path)
+    if not all_episode_dirs:
+        raise RuntimeError(f"no episodes available under {dataset_root}")
+    if not (0 <= template_episode_index < len(all_episode_dirs)):
+        raise ValueError(
+            f"template_episode_index {template_episode_index} outside available range 0..{len(all_episode_dirs) - 1}"
+        )
+
+    episode_dirs = all_episode_dirs[episode_offset:]
+    if max_episodes is not None:
+        episode_dirs = episode_dirs[:max_episodes]
+    if not episode_dirs:
+        raise RuntimeError(
+            f"no episodes selected under {dataset_root} with offset={episode_offset} max_episodes={max_episodes}"
+        )
+
+    template_episode_dir = all_episode_dirs[template_episode_index]
+    templates, template_frames = _derive_templates(dataset_path, template_episode_dir)
+    with result_path.joinpath("templates.json").open("w", encoding="utf-8") as handle:
+        json.dump(
+            {
+                "templates": templates.to_json(),
+                "template_episode": template_episode_dir.name,
+                "template_frames": template_frames,
+                "episode_offset": episode_offset,
+            },
+            handle,
+            indent=2,
+        )
+
+    episode_metrics: List[Dict[str, object]] = []
+    for episode_dir in episode_dirs:
+        artifacts = _analyze_episode(
+            dataset_path,
+            episode_dir,
+            templates,
+            template_frames,
+            checkpoint_stride=checkpoint_stride,
+            max_frames=max_frames,
+            independent_replay=independent_replay,
+        )
+        artifacts.dense.to_csv(result_path.joinpath(f"{episode_dir.name}.dense.csv"), index=False)
+        artifacts.keyframes.to_csv(
+            result_path.joinpath(f"{episode_dir.name}.keyframes.csv"), index=False
+        )
+        with result_path.joinpath(f"{episode_dir.name}.metrics.json").open(
+            "w", encoding="utf-8"
+        ) as handle:
+            json.dump(artifacts.metrics, handle, indent=2)
+        episode_metrics.append(artifacts.metrics)
+
+    summary = _aggregate_summary(episode_metrics)
+    with result_path.joinpath("summary.json").open("w", encoding="utf-8") as handle:
+        json.dump(summary, handle, indent=2)
+    return summary
+
+
+def main(argv: Optional[Sequence[str]] = None) -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset-root",
+        default="/workspace/data/bimanual_take_tray_out_of_oven_train_128",
+    )
+    parser.add_argument(
+        "--result-dir",
+        default="/workspace/reveal_retrieve_label_study/results/oven_first_pass",
+    )
+    parser.add_argument("--max-episodes", type=int, default=1)
+    parser.add_argument("--checkpoint-stride", type=int, default=16)
+    parser.add_argument("--max-frames", type=int)
+    parser.add_argument("--episode-offset", type=int, default=0)
+    parser.add_argument("--template-episode-index", type=int, default=0)
+    parser.add_argument("--independent-replay", action="store_true")
+    args = parser.parse_args(argv)
+
+    summary = run_study(
+        dataset_root=args.dataset_root,
+        result_dir=args.result_dir,
+        max_episodes=args.max_episodes,
+        checkpoint_stride=args.checkpoint_stride,
+        max_frames=args.max_frames,
+        episode_offset=args.episode_offset,
+        template_episode_index=args.template_episode_index,
+        independent_replay=args.independent_replay,
+    )
+    print(json.dumps(summary, indent=2))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

code/scripts/launch_parallel_oven_label_study.py

@@ -0,0 +1,223 @@
+import argparse
+import json
+import math
+import os
+import signal
+import subprocess
+import sys
+import time
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from rr_label_study.oven_study import _aggregate_summary, _episode_dirs
+
+
+def _chunk_specs(
+    total_episodes: int,
+    episode_offset: int,
+    max_episodes: Optional[int],
+    num_workers: int,
+) -> List[Tuple[int, int]]:
+    remaining = max(0, total_episodes - episode_offset)
+    if max_episodes is not None:
+        remaining = min(remaining, max_episodes)
+    if remaining <= 0:
+        return []
+    worker_count = min(num_workers, remaining)
+    chunk_size = math.ceil(remaining / worker_count)
+    specs: List[Tuple[int, int]] = []
+    for worker_index in range(worker_count):
+        start = episode_offset + worker_index * chunk_size
+        count = min(chunk_size, episode_offset + remaining - start)
+        if count > 0:
+            specs.append((start, count))
+    return specs
+
+
+def _launch_xvfb(display_num: int, log_path: Path) -> subprocess.Popen:
+    log_handle = log_path.open("w", encoding="utf-8")
+    return subprocess.Popen(
+        [
+            "Xvfb",
+            f":{display_num}",
+            "-screen",
+            "0",
+            "1280x1024x24",
+            "+extension",
+            "GLX",
+            "+render",
+            "-noreset",
+        ],
+        stdout=log_handle,
+        stderr=subprocess.STDOUT,
+        start_new_session=True,
+    )
+
+
+def _launch_worker(
+    worker_dir: Path,
+    display_num: int,
+    dataset_root: str,
+    episode_offset: int,
+    max_episodes: int,
+    checkpoint_stride: int,
+    template_episode_index: int,
+    max_frames: Optional[int],
+) -> Tuple[subprocess.Popen, subprocess.Popen]:
+    worker_dir.mkdir(parents=True, exist_ok=True)
+    xvfb = _launch_xvfb(display_num, worker_dir.joinpath("xvfb.log"))
+    time.sleep(1.0)
+
+    runtime_dir = Path(f"/tmp/rr_label_study_display_{display_num}")
+    runtime_dir.mkdir(parents=True, exist_ok=True)
+
+    command = [
+        sys.executable,
+        str(PROJECT_ROOT.joinpath("scripts", "run_oven_label_study.py")),
+        "--dataset-root",
+        dataset_root,
+        "--result-dir",
+        str(worker_dir),
+        "--episode-offset",
+        str(episode_offset),
+        "--max-episodes",
+        str(max_episodes),
+        "--checkpoint-stride",
+        str(checkpoint_stride),
+        "--template-episode-index",
+        str(template_episode_index),
+    ]
+    if max_frames is not None:
+        command.extend(["--max-frames", str(max_frames)])
+
+    env = os.environ.copy()
+    env["DISPLAY"] = f":{display_num}"
+    env["XDG_RUNTIME_DIR"] = str(runtime_dir)
+
+    worker_log = worker_dir.joinpath("worker.log").open("w", encoding="utf-8")
+    process = subprocess.Popen(
+        command,
+        stdout=worker_log,
+        stderr=subprocess.STDOUT,
+        env=env,
+        cwd=str(PROJECT_ROOT),
+        start_new_session=True,
+    )
+    return xvfb, process
+
+
+def _stop_process(process: subprocess.Popen) -> None:
+    if process.poll() is not None:
+        return
+    try:
+        os.killpg(process.pid, signal.SIGTERM)
+    except ProcessLookupError:
+        return
+    try:
+        process.wait(timeout=10)
+    except subprocess.TimeoutExpired:
+        try:
+            os.killpg(process.pid, signal.SIGKILL)
+        except ProcessLookupError:
+            pass
+
+
+def _collect_metrics(base_result_dir: Path) -> List[Dict[str, object]]:
+    metrics: List[Dict[str, object]] = []
+    for metrics_path in sorted(base_result_dir.glob("worker_*/episode*.metrics.json")):
+        with metrics_path.open("r", encoding="utf-8") as handle:
+            metrics.append(json.load(handle))
+    return metrics
+
+
+def main(argv: Optional[List[str]] = None) -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset-root",
+        default="/workspace/data/bimanual_take_tray_out_of_oven_train_128",
+    )
+    parser.add_argument(
+        "--result-dir",
+        default="/workspace/reveal_retrieve_label_study/results/oven_parallel",
+    )
+    parser.add_argument("--num-workers", type=int, default=4)
+    parser.add_argument("--episode-offset", type=int, default=0)
+    parser.add_argument("--max-episodes", type=int)
+    parser.add_argument("--checkpoint-stride", type=int, default=16)
+    parser.add_argument("--template-episode-index", type=int, default=0)
+    parser.add_argument("--base-display", type=int, default=110)
+    parser.add_argument("--max-frames", type=int)
+    args = parser.parse_args(argv)
+
+    dataset_root = Path(args.dataset_root)
+    all_episodes = _episode_dirs(dataset_root)
+    chunk_specs = _chunk_specs(
+        total_episodes=len(all_episodes),
+        episode_offset=args.episode_offset,
+        max_episodes=args.max_episodes,
+        num_workers=args.num_workers,
+    )
+    if not chunk_specs:
+        raise RuntimeError("no episodes selected for parallel run")
+
+    result_dir = Path(args.result_dir)
+    result_dir.mkdir(parents=True, exist_ok=True)
+
+    workers: List[Tuple[subprocess.Popen, subprocess.Popen]] = []
+    worker_meta: List[Dict[str, object]] = []
+    try:
+        for worker_index, (episode_offset, episode_count) in enumerate(chunk_specs):
+            display_num = args.base_display + worker_index
+            worker_dir = result_dir.joinpath(f"worker_{worker_index:02d}")
+            xvfb, process = _launch_worker(
+                worker_dir=worker_dir,
+                display_num=display_num,
+                dataset_root=args.dataset_root,
+                episode_offset=episode_offset,
+                max_episodes=episode_count,
+                checkpoint_stride=args.checkpoint_stride,
+                template_episode_index=args.template_episode_index,
+                max_frames=args.max_frames,
+            )
+            workers.append((xvfb, process))
+            worker_meta.append(
+                {
+                    "worker_index": worker_index,
+                    "display_num": display_num,
+                    "episode_offset": episode_offset,
+                    "episode_count": episode_count,
+                }
+            )
+
+        for meta, (_, process) in zip(worker_meta, workers):
+            return_code = process.wait()
+            meta["return_code"] = return_code
+            if return_code != 0:
+                worker_index = int(meta["worker_index"])
+                worker_log = result_dir.joinpath(f"worker_{worker_index:02d}", "worker.log")
+                raise RuntimeError(
+                    f"worker {worker_index} failed with code {return_code}; see {worker_log}"
+                )
+    finally:
+        for xvfb, process in workers:
+            _stop_process(process)
+            _stop_process(xvfb)
+
+    episode_metrics = _collect_metrics(result_dir)
+    summary = _aggregate_summary(episode_metrics)
+    with result_dir.joinpath("parallel_workers.json").open("w", encoding="utf-8") as handle:
+        json.dump(worker_meta, handle, indent=2)
+    with result_dir.joinpath("parallel_summary.json").open("w", encoding="utf-8") as handle:
+        json.dump(summary, handle, indent=2)
+
+    print(json.dumps(summary, indent=2))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

code/scripts/repair_oven_episode_dense.py

@@ -0,0 +1,375 @@
+from pathlib import Path
+import argparse
+import json
+import math
+import os
+import pickle
+import signal
+import subprocess
+import sys
+import time
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+from sklearn.metrics import f1_score
+
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from rr_label_study.oven_study import (
+    BimanualTakeTrayOutOfOven,
+    DEFAULT_DOOR_SPEED_TAU,
+    DEFAULT_PEXT_TAU,
+    DEFAULT_VISIBILITY_TAU,
+    ReplayCache,
+    Shape,
+    _aggregate_summary,
+    _derive_templates,
+    _first_crossing,
+    _interventional_validity,
+    _keyframe_subset,
+    _keypoint_discovery,
+    _launch_replay_env,
+    _load_demo,
+    _load_descriptions,
+    _safe_auc,
+    _safe_auprc,
+    _transition_count,
+)
+
+
+def _launch_xvfb(display_num: int, log_path: Path) -> subprocess.Popen:
+    log_handle = log_path.open("w", encoding="utf-8")
+    return subprocess.Popen(
+        [
+            "Xvfb",
+            f":{display_num}",
+            "-screen",
+            "0",
+            "1280x1024x24",
+            "+extension",
+            "GLX",
+            "+render",
+            "-noreset",
+        ],
+        stdout=log_handle,
+        stderr=subprocess.STDOUT,
+        start_new_session=True,
+    )
+
+
+def _stop_process(process: Optional[subprocess.Popen]) -> None:
+    if process is None or process.poll() is not None:
+        return
+    try:
+        os.killpg(process.pid, signal.SIGTERM)
+    except ProcessLookupError:
+        return
+    try:
+        process.wait(timeout=10)
+    except subprocess.TimeoutExpired:
+        try:
+            os.killpg(process.pid, signal.SIGKILL)
+        except ProcessLookupError:
+            pass
+
+
+def _spawn_frame_job(
+    display_num: int,
+    episode_dir: Path,
+    templates_pkl: Path,
+    frame_index: int,
+    checkpoint_stride: int,
+    output_json: Path,
+) -> subprocess.Popen:
+    runtime_dir = Path(f"/tmp/rr_label_study_frame_display_{display_num}")
+    runtime_dir.mkdir(parents=True, exist_ok=True)
+    env = os.environ.copy()
+    env["DISPLAY"] = f":{display_num}"
+    env["COPPELIASIM_ROOT"] = "/workspace/coppelia_sim"
+    env["LD_LIBRARY_PATH"] = f"/workspace/coppelia_sim:{env.get('LD_LIBRARY_PATH', '')}"
+    env["QT_QPA_PLATFORM_PLUGIN_PATH"] = "/workspace/coppelia_sim"
+    env["XDG_RUNTIME_DIR"] = str(runtime_dir)
+    return subprocess.Popen(
+        [
+            sys.executable,
+            str(PROJECT_ROOT.joinpath("scripts", "run_oven_single_frame.py")),
+            "--episode-dir",
+            str(episode_dir),
+            "--templates-pkl",
+            str(templates_pkl),
+            "--frame-index",
+            str(frame_index),
+            "--checkpoint-stride",
+            str(checkpoint_stride),
+            "--output-json",
+            str(output_json),
+        ],
+        stdout=subprocess.DEVNULL,
+        stderr=subprocess.DEVNULL,
+        cwd=str(PROJECT_ROOT),
+        env=env,
+        start_new_session=True,
+    )
+
+
+def _recompute_episode_metrics(
+    frame_df: pd.DataFrame,
+    episode_dir: Path,
+    demo,
+    descriptions: List[str],
+    templates,
+    template_frames: Dict[str, int],
+    checkpoint_stride: int,
+) -> Tuple[pd.DataFrame, Dict[str, object]]:
+    door_speed = np.gradient(frame_df["door_angle"].to_numpy(), 0.05)
+    frame_df["door_speed_abs"] = np.abs(door_speed)
+    y_ext_binary = frame_df["y_ext"].to_numpy(dtype=bool)
+    y_ready = np.zeros(len(frame_df), dtype=bool)
+    for i in range(len(frame_df)):
+        window = y_ext_binary[i : i + 3]
+        if len(window) == 3 and np.all(window) and frame_df.iloc[i]["door_speed_abs"] <= DEFAULT_DOOR_SPEED_TAU:
+            y_ready[i] = True
+    if np.any(y_ready):
+        first_ready = int(np.flatnonzero(y_ready)[0])
+        y_ready[first_ready:] = True
+    frame_df["y_ready"] = y_ready.astype(float)
+
+    phase_trigger = (
+        (frame_df["full_view_visibility"] >= DEFAULT_VISIBILITY_TAU)
+        & (frame_df["p_ext"] >= DEFAULT_PEXT_TAU)
+        & (frame_df["door_speed_abs"] <= DEFAULT_DOOR_SPEED_TAU)
+    ).to_numpy(dtype=bool)
+    phase_switch = np.zeros(len(frame_df), dtype=bool)
+    if np.any(phase_trigger):
+        first_phase = int(np.flatnonzero(phase_trigger)[0])
+        phase_switch[first_phase:] = True
+    frame_df["phase_switch"] = phase_switch.astype(float)
+
+    keyframes = [index for index in _keypoint_discovery(demo) if index < len(frame_df)]
+    key_df = _keyframe_subset(frame_df, keyframes)
+
+    y_pre_arr = frame_df["y_pre"].to_numpy(dtype=int)
+    y_ext_arr = frame_df["y_ext"].to_numpy(dtype=int)
+    y_ready_arr = frame_df["y_ready"].to_numpy(dtype=int)
+    p_pre_arr = frame_df["p_pre"].to_numpy(dtype=float)
+    p_ext_arr = frame_df["p_ext"].to_numpy(dtype=float)
+    phase_arr = frame_df["phase_switch"].to_numpy(dtype=int)
+    whole_vis = frame_df["full_view_whole_tray_visibility"].to_numpy(dtype=float)
+    door_angle_arr = frame_df["door_angle"].to_numpy(dtype=float)
+    time_arr = frame_df["time_norm"].to_numpy(dtype=float)
+
+    ppre_cross = _first_crossing(p_pre_arr, DEFAULT_PEXT_TAU)
+    pext_cross = _first_crossing(p_ext_arr, DEFAULT_PEXT_TAU)
+    phase_cross = _first_crossing(frame_df["phase_switch"].to_numpy(dtype=float), 0.5)
+    ready_cross = _first_crossing(y_ready_arr.astype(float), 0.5)
+    phase_rises, phase_falls = _transition_count(phase_arr)
+    key_phase_cross = _first_crossing(key_df["phase_switch"].to_numpy(dtype=float), 0.5)
+    key_ready_cross = _first_crossing(key_df["y_ready"].to_numpy(dtype=float), 0.5)
+
+    env = _launch_replay_env()
+    try:
+        task = env.get_task(BimanualTakeTrayOutOfOven)
+        cache = ReplayCache(task, demo, checkpoint_stride=checkpoint_stride)
+        cache.reset()
+        interventions = _interventional_validity(
+            task,
+            cache,
+            episode_dir,
+            demo,
+            templates,
+            Shape("tray").get_handle(),
+            frame_df,
+        )
+    finally:
+        env.shutdown()
+
+    metrics = {
+        "episode_name": episode_dir.name,
+        "description": descriptions[0],
+        "num_dense_frames": int(len(frame_df)),
+        "num_keyframes": int(len(key_df)),
+        "phase_switch_rises": int(phase_rises),
+        "phase_switch_falls": int(phase_falls),
+        "ppre_cross_frame": int(ppre_cross),
+        "pext_cross_frame": int(pext_cross),
+        "phase_cross_frame": int(phase_cross),
+        "ready_cross_frame": int(ready_cross),
+        "ordering_ok": bool(ppre_cross == -1 or pext_cross == -1 or ppre_cross <= pext_cross),
+        "dense_boundary_error_frames": float(abs(phase_cross - ready_cross))
+        if phase_cross >= 0 and ready_cross >= 0
+        else float("nan"),
+        "dense_boundary_error_fraction": float(abs(phase_cross - ready_cross) / len(frame_df))
+        if phase_cross >= 0 and ready_cross >= 0
+        else float("nan"),
+        "key_boundary_error_keyframes": float(abs(key_phase_cross - key_ready_cross))
+        if key_phase_cross >= 0 and key_ready_cross >= 0
+        else float("nan"),
+        "auroc_vret_ypre_three": _safe_auc(
+            y_pre_arr, frame_df["three_view_visibility"].to_numpy(dtype=float)
+        ),
+        "auprc_vret_ypre_three": _safe_auprc(
+            y_pre_arr, frame_df["three_view_visibility"].to_numpy(dtype=float)
+        ),
+        "auroc_vret_ypre_full": _safe_auc(
+            y_pre_arr, frame_df["full_view_visibility"].to_numpy(dtype=float)
+        ),
+        "auprc_vret_ypre_full": _safe_auprc(
+            y_pre_arr, frame_df["full_view_visibility"].to_numpy(dtype=float)
+        ),
+        "auroc_ppre_ypre": _safe_auc(y_pre_arr, p_pre_arr),
+        "auprc_ppre_ypre": _safe_auprc(y_pre_arr, p_pre_arr),
+        "auroc_pext_yext": _safe_auc(y_ext_arr, p_ext_arr),
+        "auprc_pext_yext": _safe_auprc(y_ext_arr, p_ext_arr),
+        "auroc_phase_yready": _safe_auc(
+            y_ready_arr, frame_df["phase_switch"].to_numpy(dtype=float)
+        ),
+        "auprc_phase_yready": _safe_auprc(
+            y_ready_arr, frame_df["phase_switch"].to_numpy(dtype=float)
+        ),
+        "f1_phase_yready": float(f1_score(y_ready_arr, phase_arr))
+        if np.any(y_ready_arr) and np.any(phase_arr)
+        else float("nan"),
+        "baseline_auroc_door_yext": _safe_auc(y_ext_arr, door_angle_arr),
+        "baseline_auprc_door_yext": _safe_auprc(y_ext_arr, door_angle_arr),
+        "baseline_auroc_time_yext": _safe_auc(y_ext_arr, time_arr),
+        "baseline_auprc_time_yext": _safe_auprc(y_ext_arr, time_arr),
+        "baseline_auroc_whole_vis_yext": _safe_auc(y_ext_arr, whole_vis),
+        "baseline_auprc_whole_vis_yext": _safe_auprc(y_ext_arr, whole_vis),
+        **interventions,
+    }
+    return key_df, metrics
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset-root", required=True)
+    parser.add_argument("--episode-dir", required=True)
+    parser.add_argument("--input-dense-csv", required=True)
+    parser.add_argument("--output-dir", required=True)
+    parser.add_argument("--checkpoint-stride", type=int, default=16)
+    parser.add_argument("--num-workers", type=int, default=4)
+    parser.add_argument("--base-display", type=int, default=170)
+    parser.add_argument("--template-episode-dir")
+    args = parser.parse_args()
+
+    dataset_root = Path(args.dataset_root)
+    episode_dir = Path(args.episode_dir)
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    base_df = pd.read_csv(args.input_dense_csv)
+    demo = _load_demo(episode_dir)
+    descriptions = _load_descriptions(episode_dir)
+
+    template_episode_dir = (
+        Path(args.template_episode_dir) if args.template_episode_dir else episode_dir
+    )
+    templates, template_frames = _derive_templates(dataset_root, template_episode_dir)
+    templates_pkl = output_dir.joinpath("templates.pkl")
+    with templates_pkl.open("wb") as handle:
+        pickle.dump(templates, handle)
+    with output_dir.joinpath("templates.json").open("w", encoding="utf-8") as handle:
+        json.dump(
+            {
+                "templates": templates.to_json(),
+                "template_episode": template_episode_dir.name,
+                "template_frames": template_frames,
+            },
+            handle,
+            indent=2,
+        )
+
+    left_closed = np.array([float(demo[i].left.gripper_open) < 0.5 for i in range(len(base_df))])
+    onset_candidates = np.flatnonzero(
+        (base_df["p_ext"].to_numpy(dtype=float) >= DEFAULT_PEXT_TAU)
+        | (base_df["y_ext"].to_numpy(dtype=float) > 0.5)
+    )
+    onset = int(onset_candidates[0]) if len(onset_candidates) else 0
+    suspicious = np.flatnonzero(
+        (np.arange(len(base_df)) >= onset)
+        & left_closed
+        & (
+            (base_df["p_pre"].to_numpy(dtype=float) < 0.9)
+            | (base_df["y_ext"].to_numpy(dtype=float) < 0.5)
+        )
+    ).tolist()
+
+    frame_json_dir = output_dir.joinpath("repaired_frames")
+    frame_json_dir.mkdir(parents=True, exist_ok=True)
+    xvfb_procs: List[subprocess.Popen] = []
+    displays = [args.base_display + i for i in range(min(args.num_workers, max(1, len(suspicious))))]
+    try:
+        for display_num in displays:
+            xvfb_procs.append(_launch_xvfb(display_num, output_dir.joinpath(f"xvfb_{display_num}.log")))
+        time.sleep(1.0)
+
+        pending = suspicious[:]
+        active: Dict[int, Tuple[int, subprocess.Popen, Path]] = {}
+        while pending or active:
+            free_displays = [display for display in displays if display not in active]
+            while pending and free_displays:
+                display_num = free_displays.pop(0)
+                frame_index = pending.pop(0)
+                output_json = frame_json_dir.joinpath(f"frame_{frame_index:04d}.json")
+                process = _spawn_frame_job(
+                    display_num=display_num,
+                    episode_dir=episode_dir,
+                    templates_pkl=templates_pkl,
+                    frame_index=frame_index,
+                    checkpoint_stride=args.checkpoint_stride,
+                    output_json=output_json,
+                )
+                active[display_num] = (frame_index, process, output_json)
+            time.sleep(0.5)
+            finished = []
+            for display_num, (frame_index, process, output_json) in active.items():
+                return_code = process.poll()
+                if return_code is None:
+                    continue
+                if return_code != 0 or not output_json.exists():
+                    raise RuntimeError(
+                        f"frame {frame_index} repair failed on display :{display_num}"
+                    )
+                finished.append(display_num)
+            for display_num in finished:
+                active.pop(display_num)
+    finally:
+        for _, process, _ in list(active.values()) if "active" in locals() else []:
+            _stop_process(process)
+        for xvfb in xvfb_procs:
+            _stop_process(xvfb)
+
+    corrected_df = base_df.copy()
+    for frame_index in suspicious:
+        row_path = frame_json_dir.joinpath(f"frame_{frame_index:04d}.json")
+        with row_path.open("r", encoding="utf-8") as handle:
+            row = json.load(handle)
+        for key, value in row.items():
+            corrected_df.at[frame_index, key] = value
+
+    key_df, metrics = _recompute_episode_metrics(
+        frame_df=corrected_df,
+        episode_dir=episode_dir,
+        demo=demo,
+        descriptions=descriptions,
+        templates=templates,
+        template_frames=template_frames,
+        checkpoint_stride=args.checkpoint_stride,
+    )
+
+    corrected_df.to_csv(output_dir.joinpath(f"{episode_dir.name}.dense.csv"), index=False)
+    key_df.to_csv(output_dir.joinpath(f"{episode_dir.name}.keyframes.csv"), index=False)
+    with output_dir.joinpath(f"{episode_dir.name}.metrics.json").open("w", encoding="utf-8") as handle:
+        json.dump(metrics, handle, indent=2)
+    summary = _aggregate_summary([metrics])
+    with output_dir.joinpath("summary.json").open("w", encoding="utf-8") as handle:
+        json.dump(summary, handle, indent=2)
+    print(json.dumps({"suspicious_frames": suspicious, "summary": summary}, indent=2))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

code/scripts/run_oven_label_study.py

@@ -0,0 +1,13 @@
+from pathlib import Path
+import sys
+
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from rr_label_study.oven_study import main
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

code/scripts/run_oven_single_frame.py

@@ -0,0 +1,43 @@
+from pathlib import Path
+import argparse
+import json
+import pickle
+import sys
+
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from rr_label_study.oven_study import _compute_frame_row_isolated, _load_demo
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--episode-dir", required=True)
+    parser.add_argument("--templates-pkl", required=True)
+    parser.add_argument("--frame-index", type=int, required=True)
+    parser.add_argument("--checkpoint-stride", type=int, default=16)
+    parser.add_argument("--output-json", required=True)
+    args = parser.parse_args()
+
+    episode_dir = Path(args.episode_dir)
+    with Path(args.templates_pkl).open("rb") as handle:
+        templates = pickle.load(handle)
+    demo = _load_demo(episode_dir)
+    row = _compute_frame_row_isolated(
+        episode_dir=episode_dir,
+        demo=demo,
+        templates=templates,
+        checkpoint_stride=args.checkpoint_stride,
+        frame_index=args.frame_index,
+    )
+    output_path = Path(args.output_json)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    with output_path.open("w", encoding="utf-8") as handle:
+        json.dump(row, handle)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

environment/activate_rlbench_runtime.sh

@@ -0,0 +1,26 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT="${1:-/workspace}"
+DISPLAY_NUM="${DISPLAY_NUM:-99}"
+MINIFORGE_DIR="${ROOT}/miniforge3"
+ENV_PREFIX="${MINIFORGE_DIR}/envs/rlbench"
+
+source "${MINIFORGE_DIR}/etc/profile.d/conda.sh"
+conda activate "${ENV_PREFIX}"
+
+export DISPLAY=":${DISPLAY_NUM}"
+export COPPELIASIM_ROOT="${ROOT}/coppelia_sim"
+export LD_LIBRARY_PATH="${COPPELIASIM_ROOT}:${LD_LIBRARY_PATH:-}"
+export QT_QPA_PLATFORM_PLUGIN_PATH="${COPPELIASIM_ROOT}"
+export XDG_RUNTIME_DIR="/tmp/runtime-root"
+export PYTHONPATH="${ROOT}/rlbench:${ROOT}/pyrep:${ROOT}/yarr:${ROOT}/peract_bimanual:${PYTHONPATH:-}"
+
+mkdir -p "${XDG_RUNTIME_DIR}"
+
+if ! pgrep -f "Xvfb :${DISPLAY_NUM}" >/dev/null 2>&1; then
+  Xvfb ":${DISPLAY_NUM}" -screen 0 1280x1024x24 +extension GLX +render -noreset >/tmp/xvfb_${DISPLAY_NUM}.log 2>&1 &
+  sleep 1
+fi
+
+echo "Activated rlbench runtime on DISPLAY=${DISPLAY}"

environment/conda_env_rlbench.yml

@@ -0,0 +1,202 @@
+name: rlbench
+channels:
+  - conda-forge
+dependencies:
+  - _openmp_mutex=4.5
+  - alsa-lib=1.2.15.3
+  - brotli=1.2.0
+  - brotli-bin=1.2.0
+  - brotli-python=1.0.9
+  - bzip2=1.0.8
+  - ca-certificates=2026.2.25
+  - cairo=1.18.4
+  - certifi=2024.8.30
+  - cffi=1.17.0
+  - charset-normalizer=3.4.0
+  - contourpy=1.1.1
+  - cycler=0.12.1
+  - cyrus-sasl=2.1.28
+  - cython=3.0.11
+  - dbus=1.16.2
+  - font-ttf-dejavu-sans-mono=2.37
+  - font-ttf-inconsolata=3.000
+  - font-ttf-source-code-pro=2.038
+  - font-ttf-ubuntu=0.83
+  - fontconfig=2.17.1
+  - fonts-conda-ecosystem=1
+  - fonts-conda-forge=1
+  - fonttools=4.53.1
+  - freetype=2.14.3
+  - fribidi=1.0.16
+  - glib=2.86.4
+  - glib-tools=2.86.4
+  - graphite2=1.3.14
+  - gst-plugins-base=1.26.10
+  - gstreamer=1.26.10
+  - h2=4.1.0
+  - harfbuzz=13.2.1
+  - hpack=4.0.0
+  - hyperframe=6.0.1
+  - icu=78.3
+  - idna=3.10
+  - importlib-resources=6.4.5
+  - importlib_resources=6.4.5
+  - joblib=1.4.2
+  - keyutils=1.6.3
+  - kiwisolver=1.4.5
+  - krb5=1.22.2
+  - lame=3.100
+  - lcms2=2.18
+  - ld_impl_linux-64=2.45.1
+  - lerc=4.1.0
+  - libblas=3.9.0
+  - libbrotlicommon=1.2.0
+  - libbrotlidec=1.2.0
+  - libbrotlienc=1.2.0
+  - libcap=2.77
+  - libcblas=3.9.0
+  - libclang-cpp22.1=22.1.0
+  - libclang13=22.1.0
+  - libcups=2.3.3
+  - libdeflate=1.25
+  - libdrm=2.4.125
+  - libedit=3.1.20250104
+  - libegl=1.7.0
+  - libevent=2.1.12
+  - libexpat=2.7.5
+  - libffi=3.5.2
+  - libflac=1.5.0
+  - libfreetype=2.14.3
+  - libfreetype6=2.14.3
+  - libgcc=15.2.0
+  - libgcc-ng=15.2.0
+  - libgfortran=15.2.0
+  - libgfortran-ng=15.2.0
+  - libgfortran5=15.2.0
+  - libgl=1.7.0
+  - libglib=2.86.4
+  - libglvnd=1.7.0
+  - libglx=1.7.0
+  - libgomp=15.2.0
+  - libiconv=1.18
+  - libjpeg-turbo=3.1.2
+  - liblapack=3.9.0
+  - libllvm22=22.1.2
+  - liblzma=5.8.2
+  - liblzma-devel=5.8.2
+  - libnsl=2.0.1
+  - libntlm=1.8
+  - libogg=1.3.5
+  - libopenblas=0.3.25
+  - libopus=1.6.1
+  - libpciaccess=0.18
+  - libpng=1.6.56
+  - libpq=18.3
+  - libsndfile=1.2.2
+  - libsqlite=3.52.0
+  - libstdcxx=15.2.0
+  - libstdcxx-ng=15.2.0
+  - libsystemd0=260.1
+  - libtiff=4.7.1
+  - libuuid=2.42
+  - libvorbis=1.3.7
+  - libwebp-base=1.6.0
+  - libxcb=1.17.0
+  - libxcrypt=4.4.36
+  - libxkbcommon=1.13.1
+  - libxml2=2.15.2
+  - libxml2-16=2.15.2
+  - libzlib=1.3.2
+  - matplotlib=3.7.3
+  - matplotlib-base=3.7.3
+  - mpg123=1.32.9
+  - munkres=1.1.4
+  - natsort=8.4.0
+  - ncurses=6.5
+  - nspr=4.38
+  - nss=3.118
+  - numpy=1.24.4
+  - openjpeg=2.5.4
+  - openldap=2.6.10
+  - openssl=3.6.1
+  - packaging=26.0
+  - pandas=2.0.3
+  - pango=1.56.4
+  - pcre2=10.47
+  - pip=24.3.1
+  - pixman=0.46.4
+  - platformdirs=4.3.6
+  - ply=3.11
+  - pooch=1.8.2
+  - pthread-stubs=0.4
+  - pulseaudio-client=17.0
+  - pycparser=2.22
+  - pyparsing=3.1.4
+  - pyqt=5.15.9
+  - pyqt5-sip=12.12.2
+  - pysocks=1.7.1
+  - python=3.8.20
+  - python-dateutil=2.9.0
+  - python-tzdata=2024.2
+  - python_abi=3.8
+  - pytz=2024.2
+  - pyyaml=6.0.2
+  - qt-main=5.15.15
+  - readline=8.3
+  - requests=2.32.3
+  - scikit-learn=1.3.2
+  - scipy=1.10.1
+  - setuptools=75.3.0
+  - sip=6.7.12
+  - six=1.16.0
+  - threadpoolctl=3.5.0
+  - tk=8.6.13
+  - toml=0.10.2
+  - tomli=2.0.2
+  - tornado=6.4.1
+  - unicodedata2=15.1.0
+  - urllib3=2.2.3
+  - wheel=0.45.1
+  - xcb-util=0.4.1
+  - xcb-util-image=0.4.0
+  - xcb-util-keysyms=0.4.1
+  - xcb-util-renderutil=0.3.10
+  - xcb-util-wm=0.4.2
+  - xkeyboard-config=2.47
+  - xorg-libice=1.1.2
+  - xorg-libsm=1.2.6
+  - xorg-libx11=1.8.13
+  - xorg-libxau=1.0.12
+  - xorg-libxdamage=1.1.6
+  - xorg-libxdmcp=1.1.5
+  - xorg-libxext=1.3.7
+  - xorg-libxfixes=6.0.2
+  - xorg-libxrender=0.9.12
+  - xorg-libxshmfence=1.3.3
+  - xorg-libxxf86vm=1.1.7
+  - xz=5.8.2
+  - xz-gpl-tools=5.8.2
+  - xz-tools=5.8.2
+  - yaml=0.2.5
+  - zipp=3.21.0
+  - zstandard=0.19.0
+  - zstd=1.5.7
+  - pip:
+      - click==8.1.8
+      - click-prompt==0.5.1
+      - html-testrunner==1.2.1
+      - jinja2==3.1.6
+      - markdown-it-py==3.0.0
+      - markupsafe==2.1.5
+      - mdurl==0.1.2
+      - pillow==9.5.0
+      - prompt-toolkit==3.0.52
+      - pygments==2.19.2
+      - pyquaternion==0.9.9
+      - questionary==1.10.0
+      - rich==14.3.3
+      - rich-click==1.9.7
+      - rlbench==1.2.1
+      - typing-extensions==4.13.2
+      - wcwidth==0.6.0
+prefix: /workspace/miniforge3/envs/rlbench

environment/dataset_notes.txt

@@ -0,0 +1,17 @@
+Expected benchmark dataset path:
+
+/workspace/data/bimanual_take_tray_out_of_oven_train_128
+
+This upload does not include the upstream RLBench demonstration dataset itself.
+
+What is included here:
+- all custom study code written for this work
+- all generated result directories from the local run
+- environment manifests
+- setup helpers and runtime helpers
+
+What is not included here:
+- the original benchmark RGB-D demo dataset
+- CoppeliaSim binaries
+
+The final repaired single-episode study artifact in this upload was computed against the local dataset extraction used on the A40 RunPod machine described in `system_info.txt`.

environment/pip_freeze_rlbench.txt

@@ -0,0 +1,64 @@
+Brotli @ file:///home/conda/feedstock_root/build_artifacts/brotli-split_1666788425425/work
+certifi @ file:///home/conda/feedstock_root/build_artifacts/certifi_1725278078093/work/certifi
+cffi @ file:///home/conda/feedstock_root/build_artifacts/cffi_1723018376978/work
+charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1728479282467/work
+click==8.1.8
+click-prompt==0.5.1
+contourpy @ file:///home/conda/feedstock_root/build_artifacts/contourpy_1695554215751/work
+cycler @ file:///home/conda/feedstock_root/build_artifacts/cycler_1696677705766/work
+Cython @ file:///home/conda/feedstock_root/build_artifacts/cython_1722872323968/work
+fonttools @ file:///home/conda/feedstock_root/build_artifacts/fonttools_1720359039462/work
+h2 @ file:///home/conda/feedstock_root/build_artifacts/h2_1634280454336/work
+hpack==4.0.0
+html-testRunner==1.2.1
+hyperframe @ file:///home/conda/feedstock_root/build_artifacts/hyperframe_1619110129307/work
+idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1726459485162/work
+importlib_resources @ file:///home/conda/feedstock_root/build_artifacts/importlib_resources_1725921340658/work
+Jinja2==3.1.6
+joblib @ file:///home/conda/feedstock_root/build_artifacts/joblib_1714665484399/work
+kiwisolver @ file:///home/conda/feedstock_root/build_artifacts/kiwisolver_1695379923772/work
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-suite_1695076686224/work
+mdurl==0.1.2
+munkres==1.1.4
+natsort @ file:///home/conda/feedstock_root/build_artifacts/natsort_1687263093793/work
+numpy @ file:///home/conda/feedstock_root/build_artifacts/numpy_1687808301083/work
+packaging @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_packaging_1769093650/work
+pandas @ file:///home/conda/feedstock_root/build_artifacts/pandas_1688740514018/work
+Pillow==9.5.0
+platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1726613481435/work
+ply @ file:///home/conda/feedstock_root/build_artifacts/ply_1712242996588/work
+pooch @ file:///home/conda/feedstock_root/build_artifacts/pooch_1717777836653/work
+prompt_toolkit==3.0.52
+pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1711811537435/work
+Pygments==2.19.2
+pyparsing @ file:///home/conda/feedstock_root/build_artifacts/pyparsing_1724616129934/work
+PyQt5==5.15.9
+PyQt5-sip==12.12.2
+pyquaternion==0.9.9
+-e git+https://github.com/markusgrotz/PyRep.git@b8bd1d7a3182adcd570d001649c0849047ebf197#egg=PyRep
+PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work
+python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1709299778482/work
+pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1726055524169/work
+PyYAML @ file:///home/conda/feedstock_root/build_artifacts/pyyaml_1723018227672/work
+questionary==1.10.0
+requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1717057054362/work
+rich==14.3.3
+rich-click==1.9.7
+-e git+https://github.com/markusgrotz/RLBench.git@8af748c51287989294e00c9c670e3330a0e35ed5#egg=rlbench
+scikit-learn @ file:///home/conda/feedstock_root/build_artifacts/scikit-learn_1701931997682/work
+scipy @ file:///home/conda/feedstock_root/build_artifacts/scipy-split_1683900206454/work/base/dist/scipy-1.10.1-cp38-cp38-linux_x86_64.whl#sha256=13ab5c17dedeb97a65ba90de1c51475f2d3ae0fe50bc43e9b018cc2943541de4
+sip @ file:///home/conda/feedstock_root/build_artifacts/sip_1697300436403/work
+six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work
+threadpoolctl @ file:///home/conda/feedstock_root/build_artifacts/threadpoolctl_1714400101435/work
+toml @ file:///home/conda/feedstock_root/build_artifacts/toml_1604308577558/work
+tomli @ file:///home/conda/feedstock_root/build_artifacts/tomli_1727974628237/work
+tornado @ file:///home/conda/feedstock_root/build_artifacts/tornado_1717722826518/work
+typing_extensions==4.13.2
+tzdata @ file:///home/conda/feedstock_root/build_artifacts/python-tzdata_1727140567071/work
+unicodedata2 @ file:///home/conda/feedstock_root/build_artifacts/unicodedata2_1695847997538/work
+urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1726496430923/work
+wcwidth==0.6.0
+zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1731262100163/work
+zstandard @ file:///home/conda/feedstock_root/build_artifacts/zstandard_1667296101734/work

environment/repo_revisions.txt

@@ -0,0 +1,21 @@
+Upstream repositories used in this run
+
+repo_path=/workspace/rlbench
+remote=https://github.com/markusgrotz/RLBench.git
+commit=8af748c51287989294e00c9c670e3330a0e35ed5
+status:
+
+repo_path=/workspace/pyrep
+remote=https://github.com/markusgrotz/PyRep.git
+commit=b8bd1d7a3182adcd570d001649c0849047ebf197
+status:
+
+repo_path=/workspace/peract_bimanual
+remote=https://github.com/markusgrotz/peract_bimanual.git
+commit=bb0232a6ba3fe116566e9568f0c7af980ed6703d
+status:
+
+repo_path=/workspace/yarr
+remote=https://github.com/markusgrotz/YARR.git
+commit=6822ff78602c77878b27d4cfe759ce029c67bffb
+status:

environment/setup_same_hardware.sh

@@ -0,0 +1,58 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT="${1:-/workspace}"
+MINIFORGE_DIR="${ROOT}/miniforge3"
+ENV_PREFIX="${MINIFORGE_DIR}/envs/rlbench"
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+
+echo "[1/6] optional system packages"
+echo "Install these on Ubuntu 22.04 if they are missing:"
+echo "  sudo apt-get update"
+echo "  sudo apt-get install -y git wget curl xvfb git-lfs libgl1 libglib2.0-0 libsm6 libxext6 libxrender1 libxrandr2 libxi6 libxcursor1 libxinerama1 libxxf86vm1 libfontconfig1 libfreetype6"
+
+echo "[2/6] install Miniforge if needed"
+if [[ ! -x "${MINIFORGE_DIR}/bin/conda" ]]; then
+  wget -O "${ROOT}/Miniforge3-Linux-x86_64.sh" \
+    "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh"
+  bash "${ROOT}/Miniforge3-Linux-x86_64.sh" -b -p "${MINIFORGE_DIR}"
+fi
+
+source "${MINIFORGE_DIR}/etc/profile.d/conda.sh"
+
+echo "[3/6] create or update rlbench environment"
+if [[ ! -d "${ENV_PREFIX}" ]]; then
+  conda env create -p "${ENV_PREFIX}" -f "${SCRIPT_DIR}/conda_env_rlbench.yml"
+else
+  conda env update -p "${ENV_PREFIX}" -f "${SCRIPT_DIR}/conda_env_rlbench.yml" --prune
+fi
+conda activate "${ENV_PREFIX}"
+
+echo "[4/6] clone exact upstream repos"
+mkdir -p "${ROOT}"
+if [[ ! -d "${ROOT}/rlbench/.git" ]]; then
+  git clone https://github.com/markusgrotz/RLBench.git "${ROOT}/rlbench"
+fi
+if [[ ! -d "${ROOT}/pyrep/.git" ]]; then
+  git clone https://github.com/markusgrotz/PyRep.git "${ROOT}/pyrep"
+fi
+if [[ ! -d "${ROOT}/peract_bimanual/.git" ]]; then
+  git clone https://github.com/markusgrotz/peract_bimanual.git "${ROOT}/peract_bimanual"
+fi
+if [[ ! -d "${ROOT}/yarr/.git" ]]; then
+  git clone https://github.com/markusgrotz/YARR.git "${ROOT}/yarr"
+fi
+
+git -C "${ROOT}/rlbench" checkout 8af748c51287989294e00c9c670e3330a0e35ed5
+git -C "${ROOT}/pyrep" checkout b8bd1d7a3182adcd570d001649c0849047ebf197
+git -C "${ROOT}/peract_bimanual" checkout bb0232a6ba3fe116566e9568f0c7af980ed6703d
+git -C "${ROOT}/yarr" checkout 6822ff78602c77878b27d4cfe759ce029c67bffb
+
+echo "[5/6] install editable repos used by the rlbench env"
+python -m pip install -e "${ROOT}/pyrep"
+python -m pip install -e "${ROOT}/rlbench"
+
+echo "[6/6] CoppeliaSim and dataset"
+echo "Provide a CoppeliaSim extraction at ${ROOT}/coppelia_sim."
+echo "Provide the benchmark dataset at ${ROOT}/data/bimanual_take_tray_out_of_oven_train_128."
+echo "Then source: ${SCRIPT_DIR}/activate_rlbench_runtime.sh ${ROOT}"

environment/system_info.txt

@@ -0,0 +1,34 @@
+Machine profile captured on 2026-04-06 UTC
+96
+
+               total        used        free      shared  buff/cache   available
+Mem:           503Gi        67Gi       227Gi       3.5Gi       207Gi       427Gi
+Swap:             0B          0B          0B
+
+Linux cf69f4749f03 6.8.0-65-generic #68~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Jul 15 18:06:34 UTC 2 x86_64 x86_64 x86_64 GNU/Linux
+
+Distributor ID:	Ubuntu
+Description:	Ubuntu 22.04.5 LTS
+Release:	22.04
+Codename:	jammy
+
+Mon Apr  6 22:56:07 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 570.195.03             Driver Version: 570.195.03     CUDA Version: 12.8     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA A40                     On  |   00000000:53:00.0 Off |                    0 |
+|  0%   33C    P8             32W /  300W |       0MiB /  46068MiB |      0%      Default |
+|                                         |                        |                  N/A |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|  No running processes found                                                             |
++-----------------------------------------------------------------------------------------+