Add iter22 pregrasp repair reruns and updated benchmark code

Upload the repaired oven metric code, refreshed README, and iter22 episode 0/1 dense outputs with visualization GIF suites.

.gitattributes

@@ -854,3 +854,9 @@ artifacts/results/metric_iter19_ep0_independent/visualizations/frames/visibility
 artifacts/results/metric_iter19_ep1_independent/visualizations/episode1_all_metrics.gif filter=lfs diff=lfs merge=lfs -text
 artifacts/results/metric_iter19_ep1_independent/visualizations/episode1_path_quality_focus.gif filter=lfs diff=lfs merge=lfs -text
 artifacts/results/metric_iter19_ep1_independent/visualizations/episode1_visibility_focus.gif filter=lfs diff=lfs merge=lfs -text
+artifacts/results/metric_iter22_ep0_pregrasp_repair/visualizations/episode0_all_metrics.gif filter=lfs diff=lfs merge=lfs -text
+artifacts/results/metric_iter22_ep0_pregrasp_repair/visualizations/episode0_path_quality_focus.gif filter=lfs diff=lfs merge=lfs -text
+artifacts/results/metric_iter22_ep0_pregrasp_repair/visualizations/episode0_visibility_focus.gif filter=lfs diff=lfs merge=lfs -text
+artifacts/results/metric_iter22_ep1_pregrasp_repair/visualizations/episode1_all_metrics.gif filter=lfs diff=lfs merge=lfs -text
+artifacts/results/metric_iter22_ep1_pregrasp_repair/visualizations/episode1_path_quality_focus.gif filter=lfs diff=lfs merge=lfs -text
+artifacts/results/metric_iter22_ep1_pregrasp_repair/visualizations/episode1_visibility_focus.gif filter=lfs diff=lfs merge=lfs -text

README.md

@@ -14,7 +14,9 @@ This is still a label-validation repository, not a policy repository. No `pi0.5`
 
 ## Current Status
 
-The latest work in this upload fixes the main simulator-state bugs that were still contaminating the oven metrics:
+The latest work in this upload is the `metric_iter22_*_pregrasp_repair` rerun for episodes 0 and 1.
+
+That rerun fixes the main simulator-state bugs that were still contaminating the oven metrics:
 
 1. The reveal-to-retrieve transition used to occur too late, effectively at grasp time.
 2. The visibility metric used to drop to zero around frame 232 even when the tray grasp region was clearly visible in `wrist_left`.
@@ -38,7 +40,17 @@ The current code addresses those issues by:
 - sampling intervention states earlier in the reveal phase so pre-ready extract checks are not contaminated by borderline near-ready states
 - confirming extraction feasibility with repeated planner checks inside the extract oracle so one lucky planner sample is less likely to flip a label
 
-The old `iter4_*` outputs are still useful historical checkpoints, but the current authoritative path is the new independent-replay episode-0 rerun under `artifacts/results/oven_episode0_iter6_independent_full/`.
+The old `iter4_*`, `iter6_*`, and `iter19_*` outputs are still useful historical checkpoints, but the current authoritative outputs are:
+
+- `artifacts/results/metric_iter22_ep0_pregrasp_repair/`
+- `artifacts/results/metric_iter22_ep1_pregrasp_repair/`
+
+The main improvement from the `iter22` rerun is that the reveal-to-retrieve signal now flips well before grasp:
+
+- episode 0: `p_pre` first crosses at frame `138`, `phase_switch` at `143`, `y_retrieve` at `235`
+- episode 1: `p_pre` first crosses at frame `135`, `phase_switch` at `135`, `y_retrieve` at `228`
+
+The main remaining limitation is `p_pre` flicker after the first crossing, especially on episode 0. The current repo state should therefore be treated as a repaired benchmark snapshot, not the final metric design.
 
 ## What Is In This Upload
 
@@ -49,6 +61,8 @@ The old `iter4_*` outputs are still useful historical checkpoints, but the curre
   - Study runners and helpers.
   - `run_oven_label_study.py`: dense/keyframe study runner.
   - `launch_parallel_oven_label_study.py`: multi-display worker launcher.
+  - `recompute_oven_pregrasp_parallel.py`: targeted dense rerun for repaired `p_pre` labels.
+  - `run_oven_pregrasp_batch.py`: sequential per-worker pregrasp recomputation helper.
   - `refresh_saved_oven_study.py`: recompute keyframes, per-episode metrics, intervention stats, and summary JSONs from saved dense CSVs after metric-code changes.
   - `run_oven_single_frame.py`: single-frame recomputation helper.
   - `run_oven_frame_batch.py`: new sequential batch recomputation helper used to avoid late-frame drift.
@@ -70,6 +84,8 @@ The old `iter4_*` outputs are still useful historical checkpoints, but the curre
 The latest code changes are in:
 
 - `code/rr_label_study/oven_study.py`
+- `code/scripts/recompute_oven_pregrasp_parallel.py`
+- `code/scripts/run_oven_pregrasp_batch.py`
 - `code/scripts/repair_oven_episode_dense.py`
 - `code/scripts/run_oven_frame_batch.py`
 - `code/scripts/render_oven_metric_frame.py`
@@ -141,6 +157,21 @@ This fixed the major replay bug where post-oracle restores could leave the arm,
 
 The current trustworthy artifacts are:
 
+- `artifacts/results/metric_iter22_ep0_pregrasp_repair/episode0.dense.csv`
+- `artifacts/results/metric_iter22_ep0_pregrasp_repair/episode0.keyframes.csv`
+- `artifacts/results/metric_iter22_ep0_pregrasp_repair/episode0.metrics.json`
+- `artifacts/results/metric_iter22_ep0_pregrasp_repair/summary.json`
+- `artifacts/results/metric_iter22_ep0_pregrasp_repair/visualizations/episode0_all_metrics.gif`
+- `artifacts/results/metric_iter22_ep0_pregrasp_repair/visualizations/episode0_visibility_focus.gif`
+- `artifacts/results/metric_iter22_ep0_pregrasp_repair/visualizations/episode0_path_quality_focus.gif`
+- `artifacts/results/metric_iter22_ep1_pregrasp_repair/episode1.dense.csv`
+- `artifacts/results/metric_iter22_ep1_pregrasp_repair/episode1.keyframes.csv`
+- `artifacts/results/metric_iter22_ep1_pregrasp_repair/episode1.metrics.json`
+- `artifacts/results/metric_iter22_ep1_pregrasp_repair/summary.json`
+- `artifacts/results/metric_iter22_ep1_pregrasp_repair/visualizations/episode1_all_metrics.gif`
+- `artifacts/results/metric_iter22_ep1_pregrasp_repair/visualizations/episode1_visibility_focus.gif`
+- `artifacts/results/metric_iter22_ep1_pregrasp_repair/visualizations/episode1_path_quality_focus.gif`
+
 - `artifacts/results/oven_episode0_iter4_templates/templates.json`
 - `artifacts/results/oven_episode0_iter4_templates/templates.pkl`
 - `artifacts/results/oven_episode0_iter4_batch/iter4_batch_comparison.csv`

artifacts/results/metric_iter22_ep0_pregrasp_repair/episode0.dense.csv

@@ -0,0 +1,330 @@
+frame_index,time_norm,door_angle,right_gripper_open,left_gripper_open,pregrasp_progress,pregrasp_distance,p_pre,p_ext,y_pre_raw,y_ext_raw,y_pre,y_ext,three_view_visibility,three_view_whole_tray_visibility,full_view_visibility,full_view_whole_tray_visibility,door_speed_abs,pregrasp_speed,y_pre_progress_seed,phase_score,approach_active,y_retrieve,y_ready,phase_switch
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artifacts/results/metric_iter22_ep0_pregrasp_repair/episode0.metrics.json

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artifacts/results/metric_iter22_ep0_pregrasp_repair/summary.json

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artifacts/results/metric_iter22_ep0_pregrasp_repair/templates.json

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artifacts/results/metric_iter22_ep0_pregrasp_repair/templates.pkl

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artifacts/results/metric_iter22_ep0_pregrasp_repair/visualizations/README.md

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+# Visualizations
+
+- `episode0_visibility_focus.gif`: three-view visibility montage over dense frames 131-171.
+- `episode0_path_quality_focus.gif`: path-quality montage over dense frames 135-223.
+- `episode0_all_metrics.gif`: full episode overlay GIF over dense frames 0-328, sampled every 2 frames.
+- `frames/visibility_focus/`: per-frame PNGs with scene overlays, x-ray projections, and tray-mask views.
+- `frames/path_quality_focus/`: per-frame PNGs with demo wrist trails plus planned pregrasp/grasp/retreat path overlays.
+- `frames/all_metrics/`: per-frame PNGs with the episode-wide metric bars and phase banner.
+
+Legend highlights:
+- Visibility: blue = sampled tray surface, magenta = sampled grasp region, green = depth-consistent visible grasp samples.
+- Path quality: cyan/purple = recent left/right demo wrist trails, yellow = pregrasp, orange = grasp, green = retreat / extraction path.
+- All metrics: red banner = reveal phase, green banner = retrieve phase.

artifacts/results/metric_iter22_ep1_pregrasp_repair/episode1.dense.csv

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artifacts/results/metric_iter22_ep1_pregrasp_repair/summary.json

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+      1.9109265849692747e-06,
+      0.17527583241462708,
+      4.563004040392116e-06,
+      -0.8731929659843445,
+      1.0853158528334461e-05,
+      1.221557378768921,
+      0.7853907346725464
+    ],
+    "hold_open_angle": 0.7280260324478149,
+    "open_more_delta": 0.12,
+    "reference_tray_height": 1.0471735000610352,
+    "mask_handle_ids": [
+      226
+    ]
+  }
+}

artifacts/results/metric_iter22_ep1_pregrasp_repair/templates.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e45e3e2905677398379a48aebb36080e3f78bed249db66d295f60525f19e9e8
+size 2179

artifacts/results/metric_iter22_ep1_pregrasp_repair/visualizations/README.md

@@ -0,0 +1,13 @@
+# Visualizations
+
+- `episode1_visibility_focus.gif`: three-view visibility montage over dense frames 123-163.
+- `episode1_path_quality_focus.gif`: path-quality montage over dense frames 127-215.
+- `episode1_all_metrics.gif`: full episode overlay GIF over dense frames 0-311, sampled every 2 frames.
+- `frames/visibility_focus/`: per-frame PNGs with scene overlays, x-ray projections, and tray-mask views.
+- `frames/path_quality_focus/`: per-frame PNGs with demo wrist trails plus planned pregrasp/grasp/retreat path overlays.
+- `frames/all_metrics/`: per-frame PNGs with the episode-wide metric bars and phase banner.
+
+Legend highlights:
+- Visibility: blue = sampled tray surface, magenta = sampled grasp region, green = depth-consistent visible grasp samples.
+- Path quality: cyan/purple = recent left/right demo wrist trails, yellow = pregrasp, orange = grasp, green = retreat / extraction path.
+- All metrics: red banner = reveal phase, green banner = retrieve phase.

code/rr_label_study/oven_study.py

@@ -11,11 +11,12 @@ 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.const import ConfigurationPathAlgorithms as Algos, ObjectType
 from pyrep.errors import ConfigurationPathError
 from pyrep.objects.joint import Joint
 from pyrep.objects.object import Object
 from pyrep.objects.shape import Shape
+from pyrep.objects.vision_sensor import VisionSensor
 from rlbench.action_modes.action_mode import BimanualJointPositionActionMode
 from rlbench.action_modes.gripper_action_modes import BimanualDiscrete
 from rlbench.backend.const import DEPTH_SCALE
@@ -62,10 +63,14 @@ DEFAULT_PLAN_MAX_TIME_MS = 10
 DEFAULT_PLAN_TRIALS_PER_GOAL = 4
 DEFAULT_PLAN_ATTEMPTS = 2
 DEFAULT_PLAN_MIN_SUCCESSES = 2
+DEFAULT_PREGRASP_ASSIST_PATH_SCALE = 3.0
+DEFAULT_DOOR_ASSIST_MIN_OPEN_DELTA = 0.01
 DEFAULT_READY_PERSISTENCE = 3
 DEFAULT_RETRIEVE_PERSISTENCE = 3
 DEFAULT_PREGRASP_PERSISTENCE = 3
-DEFAULT_MASK_HANDLE_COUNT = 2
+DEFAULT_MASK_HANDLE_COUNT = 8
+DEFAULT_VISIBILITY_POINT_TOLERANCE = 0.03
+DEFAULT_GRASP_SECONDARY_FACE_RATIO = 0.35
 
 
 @dataclass
@@ -105,6 +110,7 @@ class MotionTemplates:
     retreat_rel_poses: List[np.ndarray]
     grasp_local_center: np.ndarray
     grasp_region_extents: np.ndarray
+    retriever_home_joints: np.ndarray
     hold_open_angle: float
     open_more_delta: float
     reference_tray_height: float
@@ -119,6 +125,7 @@ class MotionTemplates:
             "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(),
+            "retriever_home_joints": self.retriever_home_joints.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),
@@ -136,6 +143,9 @@ class MotionTemplates:
             ],
             grasp_local_center=np.asarray(payload["grasp_local_center"], dtype=np.float64),
             grasp_region_extents=np.asarray(payload["grasp_region_extents"], dtype=np.float64),
+            retriever_home_joints=np.asarray(
+                payload.get("retriever_home_joints", [0.0] * 7), dtype=np.float64
+            ),
             hold_open_angle=float(payload["hold_open_angle"]),
             open_more_delta=float(payload["open_more_delta"]),
             reference_tray_height=float(payload["reference_tray_height"]),
@@ -563,6 +573,101 @@ def _local_to_world(reference_pose: Sequence[float], point_local: Sequence[float
     return (reference @ point)[:3]
 
 
+def _tray_bbox_local() -> np.ndarray:
+    return np.asarray(Shape("tray").get_bounding_box(), dtype=np.float64)
+
+
+def _dedupe_points(points: Iterable[np.ndarray], precision: int = 5) -> List[np.ndarray]:
+    unique: List[np.ndarray] = []
+    seen = set()
+    for point in points:
+        array = np.asarray(point, dtype=np.float64)
+        key = tuple(np.round(array, precision))
+        if key in seen:
+            continue
+        seen.add(key)
+        unique.append(array)
+    return unique
+
+
+def _sample_box_face_points_local(
+    bbox: np.ndarray,
+    axis: int,
+    face_index: int,
+    center: np.ndarray,
+    extents: np.ndarray,
+    primary_samples: int,
+    secondary_samples: int,
+) -> List[np.ndarray]:
+    center = np.asarray(center, dtype=np.float64)
+    extents = np.asarray(extents, dtype=np.float64)
+    tangent_axes = [idx for idx in range(3) if idx != axis]
+    tangent_ranges: List[np.ndarray] = []
+    for tangent_idx, tangent_axis in enumerate(tangent_axes):
+        lo = max(bbox[tangent_axis * 2], center[tangent_axis] - extents[tangent_axis])
+        hi = min(bbox[tangent_axis * 2 + 1], center[tangent_axis] + extents[tangent_axis])
+        if hi < lo:
+            lo = hi = float(np.clip(center[tangent_axis], bbox[tangent_axis * 2], bbox[tangent_axis * 2 + 1]))
+        tangent_ranges.append(
+            np.linspace(lo, hi, primary_samples if tangent_idx == 0 else secondary_samples)
+        )
+
+    face_value = bbox[axis * 2 + face_index]
+    points: List[np.ndarray] = []
+    for first in tangent_ranges[0]:
+        for second in tangent_ranges[1]:
+            point = center.copy()
+            point[axis] = face_value
+            point[tangent_axes[0]] = first
+            point[tangent_axes[1]] = second
+            points.append(point)
+    return points
+
+
+def _grasp_surface_specs(
+    templates: MotionTemplates, bbox: np.ndarray
+) -> Tuple[np.ndarray, List[Tuple[int, int]]]:
+    center = np.asarray(templates.grasp_local_center, dtype=np.float64).copy()
+    mins = np.asarray([bbox[0], bbox[2], bbox[4]], dtype=np.float64)
+    maxs = np.asarray([bbox[1], bbox[3], bbox[5]], dtype=np.float64)
+    center = np.clip(center, mins, maxs)
+
+    approach = (
+        np.asarray(templates.grasp_rel_pose[:3], dtype=np.float64)
+        - np.asarray(templates.pregrasp_rel_pose[:3], dtype=np.float64)
+    )
+    if np.linalg.norm(approach) < 1e-6:
+        approach = center - 0.5 * (mins + maxs)
+
+    ranked_axes = np.argsort(-np.abs(approach))
+    primary_mag = float(abs(approach[ranked_axes[0]])) if len(ranked_axes) else 0.0
+    specs: List[Tuple[int, int]] = []
+    for rank, axis in enumerate(ranked_axes.tolist()):
+        magnitude = float(abs(approach[axis]))
+        if magnitude < 1e-6:
+            continue
+        if rank > 0 and magnitude < max(1e-6, primary_mag * DEFAULT_GRASP_SECONDARY_FACE_RATIO):
+            continue
+        move_positive = float(approach[axis]) >= 0.0
+        face_index = 0 if move_positive else 1
+        spec = (int(axis), int(face_index))
+        if spec not in specs:
+            specs.append(spec)
+        if len(specs) >= 2:
+            break
+
+    if specs:
+        return center, specs
+
+    # Fallback to the face closest to the demonstrated grasp center.
+    face_distances = []
+    for axis in range(3):
+        face_distances.append((abs(center[axis] - bbox[axis * 2]), axis, 0))
+        face_distances.append((abs(center[axis] - bbox[axis * 2 + 1]), axis, 1))
+    _, axis, face_index = min(face_distances, key=lambda item: item[0])
+    return center, [(int(axis), int(face_index))]
+
+
 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)):
@@ -687,6 +792,7 @@ def _derive_templates(dataset_root: Path, template_episode_dir: Path) -> Tuple[M
             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),
+            retriever_home_joints=np.asarray(demo[0].left.joint_positions, dtype=np.float64),
             hold_open_angle=float(states[right_open].door_angle),
             open_more_delta=max(
                 0.12,
@@ -733,22 +839,59 @@ def _project_points(points_world: np.ndarray, extrinsics: np.ndarray, intrinsics
 
 
 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)
+    bbox = _tray_bbox_local()
+    center, face_specs = _grasp_surface_specs(templates, bbox)
+    extents = np.asarray(templates.grasp_region_extents, dtype=np.float64)
+    points_local: List[np.ndarray] = []
+    for axis, face_index in face_specs:
+        points_local.extend(
+            _sample_box_face_points_local(
+                bbox=bbox,
+                axis=axis,
+                face_index=face_index,
+                center=center,
+                extents=extents,
+                primary_samples=9,
+                secondary_samples=5,
+            )
+        )
+    points_local = _dedupe_points(points_local)
     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)
+    bbox = _tray_bbox_local()
+    center = np.asarray(
+        [
+            0.5 * (bbox[0] + bbox[1]),
+            0.5 * (bbox[2] + bbox[3]),
+            0.5 * (bbox[4] + bbox[5]),
+        ],
+        dtype=np.float64,
+    )
+    extents = np.asarray(
+        [
+            0.5 * (bbox[1] - bbox[0]),
+            0.5 * (bbox[3] - bbox[2]),
+            0.5 * (bbox[5] - bbox[4]),
+        ],
+        dtype=np.float64,
+    )
+    points_local: List[np.ndarray] = []
+    for axis in range(3):
+        for face_index in [0, 1]:
+            points_local.extend(
+                _sample_box_face_points_local(
+                    bbox=bbox,
+                    axis=axis,
+                    face_index=face_index,
+                    center=center,
+                    extents=extents,
+                    primary_samples=8,
+                    secondary_samples=8,
+                )
+            )
+    points_local = _dedupe_points(points_local)
     return np.array([_local_to_world(tray_pose, point) for point in points_local], dtype=np.float64)
 
 
@@ -761,37 +904,95 @@ def _infer_tray_mask_handle_ids(
     max_handles: int = DEFAULT_MASK_HANDLE_COUNT,
 ) -> List[int]:
     counts: Dict[int, int] = {}
+    fallback_counts: Dict[int, int] = {}
     unique_frames = sorted({int(frame_index) for frame_index in reference_frames})
     for frame_index in unique_frames:
         cache.step_to(frame_index)
         grasp_points = _sample_grasp_points(templates, Shape("tray").get_pose())
+        tray_points = _sample_full_tray_points(Shape("tray").get_pose())
         for camera_name in FULL_CAMERA_SET:
             mask = _load_mask(episode_dir, frame_index, camera_name)
-            extrinsics = demo[frame_index].misc[f"{camera_name}_camera_extrinsics"]
-            intrinsics = demo[frame_index].misc[f"{camera_name}_camera_intrinsics"]
-            uv, camera_xyz = _project_points(grasp_points, extrinsics, intrinsics)
-            height, width = mask.shape
-            for (u, v), (_, _, camera_depth) in zip(uv, camera_xyz):
-                if camera_depth <= 0 or not (0 <= u < width and 0 <= v < height):
-                    continue
-                px = min(max(int(round(float(u))), 0), width - 1)
-                py = min(max(int(round(float(v))), 0), height - 1)
-                handle = int(mask[py, px])
-                if handle == 0:
-                    continue
-                counts[handle] = counts.get(handle, 0) + 1
-    if not counts:
+            point_cloud, extrinsics, intrinsics = _camera_point_cloud(
+                episode_dir, demo, frame_index, camera_name
+            )
+            for points_world in [grasp_points, tray_points]:
+                projected, visible = _visibility_projection_details(
+                    points_world, point_cloud, extrinsics, intrinsics
+                )
+                for px, py in visible:
+                    handle = int(mask[py, px])
+                    if handle != 0:
+                        counts[handle] = counts.get(handle, 0) + 1
+                for px, py in projected:
+                    handle = int(mask[py, px])
+                    if handle != 0:
+                        fallback_counts[handle] = fallback_counts.get(handle, 0) + 1
+
+    ranked_source = counts if counts else fallback_counts
+    if not ranked_source:
         return []
-    ranked = sorted(counts.items(), key=lambda item: item[1], reverse=True)
+    ranked = sorted(ranked_source.items(), key=lambda item: item[1], reverse=True)
     top_count = ranked[0][1]
     selected = [
         handle
         for handle, count in ranked
-        if count >= max(4, int(math.ceil(top_count * 0.5)))
+        if count >= max(2, int(math.ceil(top_count * 0.15)))
     ][:max_handles]
     return selected if selected else [ranked[0][0]]
 
 
+def _camera_point_cloud(
+    episode_dir: Path,
+    demo: Demo,
+    frame_index: int,
+    camera_name: str,
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    depth = _load_depth_meters(episode_dir, demo, frame_index, camera_name)
+    extrinsics = demo[frame_index].misc[f"{camera_name}_camera_extrinsics"]
+    intrinsics = demo[frame_index].misc[f"{camera_name}_camera_intrinsics"]
+    point_cloud = VisionSensor.pointcloud_from_depth_and_camera_params(
+        depth, extrinsics, intrinsics
+    )
+    return point_cloud, extrinsics, intrinsics
+
+
+def _visibility_projection_details(
+    points_world: np.ndarray,
+    point_cloud_world: np.ndarray,
+    extrinsics: np.ndarray,
+    intrinsics: np.ndarray,
+    tolerance: float = DEFAULT_VISIBILITY_POINT_TOLERANCE,
+) -> Tuple[List[Tuple[int, int]], List[Tuple[int, int]]]:
+    uv, camera_xyz = _project_points(points_world, extrinsics, intrinsics)
+    height, width = point_cloud_world.shape[:2]
+    projected: List[Tuple[int, int]] = []
+    visible: List[Tuple[int, int]] = []
+    for point_world, (u, v), (_, _, camera_depth) in zip(points_world, uv, camera_xyz):
+        if camera_depth <= 0 or not (0 <= u < width and 0 <= v < height):
+            continue
+        px = min(max(int(round(float(u))), 0), width - 1)
+        py = min(max(int(round(float(v))), 0), height - 1)
+        projected.append((px, py))
+        scene_point = np.asarray(point_cloud_world[py, px], dtype=np.float64)
+        if not np.isfinite(scene_point).all():
+            continue
+        if float(np.linalg.norm(scene_point - point_world)) <= tolerance:
+            visible.append((px, py))
+    return projected, visible
+
+
+def _point_visibility_ratio_from_point_cloud(
+    points_world: np.ndarray,
+    point_cloud_world: np.ndarray,
+    extrinsics: np.ndarray,
+    intrinsics: np.ndarray,
+) -> float:
+    projected, visible = _visibility_projection_details(
+        points_world, point_cloud_world, extrinsics, intrinsics
+    )
+    return float(len(visible) / len(projected)) if projected else 0.0
+
+
 def _mask_visibility_ratio(
     points_world: np.ndarray,
     mask: np.ndarray,
@@ -963,6 +1164,43 @@ def _pregrasp_candidates(tray_pose: np.ndarray, templates: MotionTemplates) -> L
     return _dedupe_pose_list(candidates)
 
 
+def _late_pregrasp_goal_poses(
+    tray_pose: np.ndarray, templates: MotionTemplates
+) -> List[np.ndarray]:
+    final_goal = _apply_relative_pose(tray_pose, templates.pregrasp_rel_pose)
+    goals = [final_goal]
+    for dx in (-0.02, 0.02):
+        perturbed = final_goal.copy()
+        perturbed[0] += dx
+        goals.append(perturbed)
+    return _dedupe_pose_list(goals)
+
+
+def _pregrasp_corridor_rel_poses(templates: MotionTemplates) -> List[np.ndarray]:
+    rel_poses = [
+        np.asarray(rel_pose, dtype=np.float64)
+        for rel_pose in templates.approach_rel_poses
+    ]
+    if not rel_poses:
+        return [np.asarray(templates.pregrasp_rel_pose, dtype=np.float64)]
+
+    pregrasp_rel_pose = np.asarray(templates.pregrasp_rel_pose, dtype=np.float64)
+    stop_index = int(
+        np.argmin(
+            [
+                float(np.linalg.norm(rel_pose[:3] - pregrasp_rel_pose[:3]))
+                for rel_pose in rel_poses
+            ]
+        )
+    )
+    corridor = rel_poses[: stop_index + 1]
+    if not corridor:
+        corridor = [pregrasp_rel_pose]
+    elif float(np.linalg.norm(corridor[-1][:3] - pregrasp_rel_pose[:3])) > 1e-6:
+        corridor.append(pregrasp_rel_pose)
+    return _dedupe_pose_list(corridor)
+
+
 def _extract_sequence_poses(
     tray_pose: np.ndarray, task_base_pose: np.ndarray, templates: MotionTemplates
 ) -> List[np.ndarray]:
@@ -1002,6 +1240,100 @@ def _extraction_progress_score(current_height: float, templates: MotionTemplates
     return float(min(1.0, 0.8 + margin / 0.12))
 
 
+def _arm_external_collision_names(scene, arm_name: str) -> List[str]:
+    arm = scene.robot.left_arm if arm_name == "left" else scene.robot.right_arm
+    gripper = scene.robot.left_gripper if arm_name == "left" else scene.robot.right_gripper
+    robot_shapes = {
+        shape.get_name() for shape in arm.get_objects_in_tree(object_type=ObjectType.SHAPE)
+    }
+    grasped = {obj.get_name() for obj in gripper.get_grasped_objects()}
+    collisions: List[str] = []
+    for shape in scene.pyrep.get_objects_in_tree(object_type=ObjectType.SHAPE):
+        name = shape.get_name()
+        if not shape.is_collidable() or name in robot_shapes or name in grasped:
+            continue
+        try:
+            if arm.check_arm_collision(shape):
+                collisions.append(name)
+        except Exception:
+            continue
+    return sorted(set(collisions))
+
+
+def _allowed_door_collision_names(scene) -> set:
+    allowed = set()
+    for shape in scene.pyrep.get_objects_in_tree(object_type=ObjectType.SHAPE):
+        name = shape.get_name()
+        if name.startswith("oven_door") or name.startswith("oven_knob"):
+            allowed.add(name)
+    return allowed
+
+
+def _execute_path_with_collision_trace(task, path, arm_name: str) -> List[str]:
+    collisions = set()
+    path.set_to_start(disable_dynamics=True)
+    task._pyrep.step()
+    steps = 0
+    while True:
+        done = path.step()
+        task._pyrep.step()
+        collisions.update(_arm_external_collision_names(task._scene, arm_name))
+        steps += 1
+        if done or steps > 400:
+            break
+    return sorted(collisions)
+
+
+def _door_assisted_pregrasp_score(
+    task,
+    target_pose: np.ndarray,
+    base_snapshot: Optional[SimulatorSnapshot] = None,
+    before_angle: Optional[float] = None,
+    allowed_collisions: Optional[set] = None,
+) -> Tuple[float, bool]:
+    snapshot = base_snapshot if base_snapshot is not None else _capture_snapshot(task)
+    try:
+        if before_angle is None:
+            before_angle = float(Joint("oven_door_joint").get_joint_position())
+        path = _plan_path(
+            task._scene,
+            "left",
+            target_pose,
+            ignore_collisions=True,
+        )
+        length = _path_length(path)
+        if path is None or not np.isfinite(length):
+            return 0.0, False
+
+        collision_names = _execute_path_with_collision_trace(task, path, "left")
+        allowed = (
+            _allowed_door_collision_names(task._scene)
+            if allowed_collisions is None
+            else allowed_collisions
+        )
+        bad_collisions = [name for name in collision_names if name not in allowed]
+        door_open_delta = before_angle - float(Joint("oven_door_joint").get_joint_position())
+        if bad_collisions or door_open_delta <= DEFAULT_DOOR_ASSIST_MIN_OPEN_DELTA:
+            return 0.0, False
+
+        base_score = float(
+            math.exp(-float(length) / DEFAULT_PREGRASP_ASSIST_PATH_SCALE)
+        )
+        strict_path = _plan_path(
+            task._scene,
+            "left",
+            target_pose,
+            ignore_collisions=False,
+        )
+        strict_length = _path_length(strict_path)
+        if strict_path is not None and np.isfinite(strict_length):
+            return float(0.5 + 0.5 * base_score), False
+
+        return base_score, False
+    finally:
+        _restore_snapshot(task, snapshot)
+
+
 def _pregrasp_score_and_success(task, templates: MotionTemplates) -> Tuple[float, bool]:
     tray = Shape("tray")
     if any(
@@ -1009,68 +1341,37 @@ def _pregrasp_score_and_success(task, templates: MotionTemplates) -> Tuple[float
         for grasped in task._scene.robot.left_gripper.get_grasped_objects()
     ):
         return 1.0, True
-    tray_pose = Shape("tray").get_pose()
-    current_pose = np.asarray(task._scene.robot.left_gripper.get_pose(), dtype=np.float64)
-    progress, distance_to_pregrasp = _pregrasp_progress_and_distance(
-        current_pose, tray_pose, templates
-    )
-    best = progress
-    success = False
-    late_approach_poses = [
-        _apply_relative_pose(tray_pose, rel_pose)
-        for rel_pose in templates.approach_rel_poses[-min(1, len(templates.approach_rel_poses)) :]
-    ]
-    corridor_targets = _dedupe_pose_list(late_approach_poses)
-    if not corridor_targets:
-        corridor_targets = [_apply_relative_pose(tray_pose, templates.pregrasp_rel_pose)]
-
-    snapshot = _capture_snapshot(task)
-    try:
-        for start_index, start_pose in enumerate(corridor_targets):
-            start_distance = float(np.linalg.norm(current_pose[:3] - start_pose[:3]))
-            if start_index > 0 and start_distance > 0.08:
-                continue
-
-            _restore_snapshot(task, snapshot)
-            stage_scores: List[float] = []
-            reliable_stage_count = 0
-            stage_success = True
-
-            for target_pose in corridor_targets[start_index:]:
-                live_pose = np.asarray(task._scene.robot.left_gripper.get_pose(), dtype=np.float64)
-                proximity = math.exp(-float(np.linalg.norm(live_pose[:3] - target_pose[:3])) / 0.09)
-                path, length, reliability = _stable_plan(
-                    task._scene,
-                    "left",
-                    target_pose,
-                    ignore_collisions=False,
-                )
-                if path is None or not np.isfinite(length):
-                    stage_scores.append(0.25 * proximity)
-                    stage_success = False
-                    break
-
-                planner_score = reliability * math.exp(-length / DEFAULT_PATH_SCALE)
-                stage_scores.append(0.7 * planner_score + 0.3 * proximity)
-                if not _plan_is_reliable(reliability):
-                    stage_success = False
-                    break
-
-                path.set_to_end(disable_dynamics=True)
-                task._pyrep.step()
-                reliable_stage_count += 1
-
-            if stage_scores:
-                normalized_stage_score = float(np.mean(stage_scores))
-                best = max(best, 0.35 * progress + 0.65 * normalized_stage_score)
-            else:
-                best = max(best, 0.75 * progress)
-
-            if stage_success and reliable_stage_count == len(corridor_targets[start_index:]):
-                success = True
-    finally:
-        _restore_snapshot(task, snapshot)
-    return best, success
+    tray_pose = tray.get_pose()
+    goal_poses = _late_pregrasp_goal_poses(tray_pose, templates)
+    best_score = 0.0
+    best_success = False
+    base_snapshot: Optional[SimulatorSnapshot] = None
+    before_angle = float(Joint("oven_door_joint").get_joint_position())
+    allowed_collisions: Optional[set] = None
+    for target_pose in goal_poses:
+        path = _plan_path(
+            task._scene,
+            "left",
+            target_pose,
+            ignore_collisions=False,
+        )
+        length = _path_length(path)
+        if path is not None and np.isfinite(length):
+            return 1.0, True
+        if base_snapshot is None:
+            base_snapshot = _capture_snapshot(task)
+            allowed_collisions = _allowed_door_collision_names(task._scene)
+        assisted_score, assisted_success = _door_assisted_pregrasp_score(
+            task,
+            target_pose,
+            base_snapshot=base_snapshot,
+            before_angle=before_angle,
+            allowed_collisions=allowed_collisions,
+        )
+        if assisted_score > best_score:
+            best_score = assisted_score
+        best_success = best_success or assisted_success
+    return float(best_score), bool(best_success)
 
 
 def _extract_score_and_success(task, templates: MotionTemplates) -> Tuple[float, bool]:
@@ -1171,15 +1472,15 @@ def _frame_metrics(
     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:
-        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"]
+        point_cloud, extrinsics, intrinsics = _camera_point_cloud(
+            episode_dir, demo, frame_state.frame_index, camera_name
+        )
         camera_values[camera_name] = {
-            "grasp_visibility": _mask_visibility_ratio(
-                grasp_points, mask, templates.mask_handle_ids, extrinsics, intrinsics
+            "grasp_visibility": _point_visibility_ratio_from_point_cloud(
+                grasp_points, point_cloud, extrinsics, intrinsics
             ),
-            "tray_visibility": _mask_visibility_ratio(
-                full_tray_points, mask, templates.mask_handle_ids, extrinsics, intrinsics
+            "tray_visibility": _point_visibility_ratio_from_point_cloud(
+                full_tray_points, point_cloud, extrinsics, intrinsics
             ),
         }
 
@@ -1213,6 +1514,39 @@ def _compute_frame_row_isolated(
     return rows[0]
 
 
+def _build_frame_row(
+    episode_dir: Path,
+    demo: Demo,
+    templates: MotionTemplates,
+    task,
+    state: ReplayState,
+) -> Dict[str, float]:
+    visibility = _frame_metrics(episode_dir, demo, state, templates)
+    pregrasp_progress, pregrasp_distance = _pregrasp_progress_and_distance(
+        np.asarray(state.left_gripper_pose, dtype=np.float64),
+        np.asarray(state.tray_pose, dtype=np.float64),
+        templates,
+    )
+    p_pre, y_pre = _pregrasp_score_and_success(task, templates)
+    p_ext, y_ext = _extract_score_and_success(task, templates)
+    return {
+        "frame_index": int(state.frame_index),
+        "time_norm": float(state.frame_index / max(1, len(demo) - 1)),
+        "door_angle": float(state.door_angle),
+        "right_gripper_open": float(state.right_gripper_open),
+        "left_gripper_open": float(state.left_gripper_open),
+        "pregrasp_progress": float(pregrasp_progress),
+        "pregrasp_distance": float(pregrasp_distance),
+        "p_pre": float(p_pre),
+        "p_ext": float(p_ext),
+        "y_pre_raw": float(bool(y_pre)),
+        "y_ext_raw": float(bool(y_ext)),
+        "y_pre": float(bool(y_pre)),
+        "y_ext": float(bool(y_ext)),
+        **visibility,
+    }
+
+
 def _compute_frame_rows_sequential(
     episode_dir: Path,
     demo: Demo,
@@ -1230,38 +1564,37 @@ def _compute_frame_rows_sequential(
             cache.step_to(frame_index)
             frame_snapshot = cache.snapshot()
             state = cache.current_state()
-            visibility = _frame_metrics(episode_dir, demo, state, templates)
-            pregrasp_progress, pregrasp_distance = _pregrasp_progress_and_distance(
-                np.asarray(state.left_gripper_pose, dtype=np.float64),
-                np.asarray(state.tray_pose, dtype=np.float64),
-                templates,
-            )
-            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, len(demo) - 1),
-                    "door_angle": state.door_angle,
-                    "right_gripper_open": state.right_gripper_open,
-                    "left_gripper_open": state.left_gripper_open,
-                    "pregrasp_progress": pregrasp_progress,
-                    "pregrasp_distance": pregrasp_distance,
-                    "p_pre": p_pre,
-                    "p_ext": p_ext,
-                    "y_pre_raw": float(bool(y_pre)),
-                    "y_ext_raw": float(bool(y_ext)),
-                    "y_pre": float(bool(y_pre)),
-                    "y_ext": float(bool(y_ext)),
-                    **visibility,
-                }
-            )
+            rows.append(_build_frame_row(episode_dir, demo, templates, task, state))
             cache.restore(frame_snapshot)
         return rows
     finally:
         env.shutdown()
 
 
+def _compute_frame_rows_independent(
+    episode_dir: Path,
+    demo: Demo,
+    templates: MotionTemplates,
+    checkpoint_stride: int,
+    frame_indices: Sequence[int],
+) -> List[Dict[str, float]]:
+    env = _launch_replay_env()
+    try:
+        task = env.get_task(BimanualTakeTrayOutOfOven)
+        cache = ReplayCache(task, demo, checkpoint_stride=checkpoint_stride)
+        cache.reset()
+        initial_snapshot = cache.snapshot()
+        rows: List[Dict[str, float]] = []
+        for frame_index in sorted({int(index) for index in frame_indices}):
+            cache.restore_to_index(initial_snapshot, 0)
+            cache.step_to(frame_index)
+            state = cache.current_state()
+            rows.append(_build_frame_row(episode_dir, demo, templates, task, state))
+        return rows
+    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")
@@ -1334,22 +1667,23 @@ def _annotate_phase_columns(frame_df: pd.DataFrame) -> pd.DataFrame:
     pregrasp_speed = -np.gradient(pregrasp_distance, DEMO_DT)
     frame_df["pregrasp_speed"] = pregrasp_speed
 
-    y_pre_seed = pregrasp_progress >= DEFAULT_PREGRASP_LABEL_PROGRESS_TAU
+    pregrasp_progress_seed = pregrasp_progress >= DEFAULT_PREGRASP_LABEL_PROGRESS_TAU
+    y_pre_seed = (
+        frame_df["y_pre_raw"].to_numpy(dtype=bool)
+        if "y_pre_raw" in frame_df
+        else pregrasp_progress_seed
+    )
     y_pre_binary = _monotone_after_first(
         _persistent_rise_mask(y_pre_seed, DEFAULT_PREGRASP_PERSISTENCE)
     )
     y_ext_binary = _monotone_after_first(
         _persistent_rise_mask(y_ext_raw, DEFAULT_READY_PERSISTENCE)
     )
-    frame_df["y_pre_progress_seed"] = y_pre_seed.astype(float)
+    frame_df["y_pre_progress_seed"] = pregrasp_progress_seed.astype(float)
     frame_df["y_pre"] = y_pre_binary.astype(float)
     frame_df["y_ext"] = y_ext_binary.astype(float)
 
-    frame_df["phase_score"] = np.clip(
-        0.7 * pregrasp_progress + 0.3 * frame_df["p_pre"].to_numpy(dtype=float),
-        0.0,
-        1.0,
-    )
+    frame_df["phase_score"] = np.clip(frame_df["p_pre"].to_numpy(dtype=float), 0.0, 1.0)
     approach_active = (
         (pregrasp_progress >= DEFAULT_APPROACH_PROGRESS_TAU)
         & (pregrasp_speed >= DEFAULT_APPROACH_SPEED_TAU)
@@ -1373,7 +1707,7 @@ def _annotate_phase_columns(frame_df: pd.DataFrame) -> pd.DataFrame:
     frame_df["y_ready"] = _monotone_after_first(ready_seed).astype(float)
 
     phase_seed = _persistent_rise_mask(
-        frame_df["phase_score"].to_numpy(dtype=float) >= DEFAULT_PHASE_SCORE_TAU,
+        frame_df["p_pre"].to_numpy(dtype=float) >= DEFAULT_PHASE_SCORE_TAU,
         DEFAULT_RETRIEVE_PERSISTENCE,
     )
     frame_df["phase_switch"] = _monotone_after_first(phase_seed).astype(float)
@@ -1698,7 +2032,8 @@ def run_study(
     episode_offset: int = 0,
     template_episode_index: int = 0,
     episode_indices: Optional[Sequence[int]] = None,
-    independent_replay: bool = False,
+    independent_replay: bool = True,
+    per_episode_templates: bool = False,
 ) -> Dict[str, object]:
     dataset_path = Path(dataset_root)
     result_path = Path(result_dir)
@@ -1740,27 +2075,58 @@ def run_study(
         )
 
     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,
-                "selected_episode_indices": selected_episode_indices,
-            },
-            handle,
-            indent=2,
-        )
+    if not per_episode_templates:
+        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(
+                {
+                    "template_mode": "shared",
+                    "templates": templates.to_json(),
+                    "template_episode": template_episode_dir.name,
+                    "template_frames": template_frames,
+                    "episode_offset": episode_offset,
+                    "selected_episode_indices": selected_episode_indices,
+                },
+                handle,
+                indent=2,
+            )
+    else:
+        with result_path.joinpath("templates.json").open("w", encoding="utf-8") as handle:
+            json.dump(
+                {
+                    "template_mode": "per_episode",
+                    "episode_offset": episode_offset,
+                    "selected_episode_indices": selected_episode_indices,
+                },
+                handle,
+                indent=2,
+            )
 
     episode_metrics: List[Dict[str, object]] = []
     for episode_dir in episode_dirs:
+        if per_episode_templates:
+            episode_templates, episode_template_frames = _derive_templates(dataset_path, episode_dir)
+            with result_path.joinpath(f"{episode_dir.name}.templates.json").open(
+                "w", encoding="utf-8"
+            ) as handle:
+                json.dump(
+                    {
+                        "template_mode": "per_episode",
+                        "templates": episode_templates.to_json(),
+                        "template_episode": episode_dir.name,
+                        "template_frames": episode_template_frames,
+                    },
+                    handle,
+                    indent=2,
+                )
+        else:
+            episode_templates = templates
+            episode_template_frames = template_frames
         artifacts = _analyze_episode(
             dataset_path,
             episode_dir,
-            templates,
-            template_frames,
+            episode_templates,
+            episode_template_frames,
             checkpoint_stride=checkpoint_stride,
             max_frames=max_frames,
             independent_replay=independent_replay,
@@ -1808,7 +2174,20 @@ def main(argv: Optional[Sequence[str]] = None) -> int:
     parser.add_argument("--episode-offset", type=int, default=0)
     parser.add_argument("--template-episode-index", type=int, default=0)
     parser.add_argument("--episode-indices", type=_parse_episode_indices)
-    parser.add_argument("--independent-replay", action="store_true")
+    parser.add_argument(
+        "--independent-replay",
+        dest="independent_replay",
+        action="store_true",
+        help="Replay each frame from the demo start/checkpoint to avoid simulator drift.",
+    )
+    parser.add_argument(
+        "--sequential-replay",
+        dest="independent_replay",
+        action="store_false",
+        help="Reuse the live replay state across frames for speed at the cost of more drift.",
+    )
+    parser.add_argument("--per-episode-templates", action="store_true")
+    parser.set_defaults(independent_replay=True)
     args = parser.parse_args(argv)
 
     summary = run_study(
@@ -1821,6 +2200,7 @@ def main(argv: Optional[Sequence[str]] = None) -> int:
         template_episode_index=args.template_episode_index,
         episode_indices=args.episode_indices,
         independent_replay=args.independent_replay,
+        per_episode_templates=args.per_episode_templates,
     )
     print(json.dumps(summary, indent=2))
     return 0

code/scripts/launch_parallel_oven_label_study.py

@@ -105,6 +105,7 @@ def _launch_worker(
     template_episode_index: int,
     max_frames: Optional[int],
     independent_replay: bool,
+    per_episode_templates: bool,
     thread_count: int,
 ) -> Tuple[subprocess.Popen, subprocess.Popen]:
     worker_dir.mkdir(parents=True, exist_ok=True)
@@ -130,8 +131,10 @@ def _launch_worker(
     ]
     if max_frames is not None:
         command.extend(["--max-frames", str(max_frames)])
-    if independent_replay:
-        command.append("--independent-replay")
+    if not independent_replay:
+        command.append("--sequential-replay")
+    if per_episode_templates:
+        command.append("--per-episode-templates")
 
     env = os.environ.copy()
     env["DISPLAY"] = f":{display_num}"
@@ -209,7 +212,20 @@ def main(argv: Optional[List[str]] = None) -> int:
     parser.add_argument("--episode-indices")
     parser.add_argument("--thread-count", type=int, default=1)
     parser.add_argument("--stagger-seconds", type=float, default=0.5)
-    parser.add_argument("--independent-replay", action="store_true")
+    parser.add_argument(
+        "--independent-replay",
+        dest="independent_replay",
+        action="store_true",
+        help="Replay each frame independently to avoid simulator drift.",
+    )
+    parser.add_argument(
+        "--sequential-replay",
+        dest="independent_replay",
+        action="store_false",
+        help="Reuse replay state across frames for speed.",
+    )
+    parser.add_argument("--per-episode-templates", action="store_true")
+    parser.set_defaults(independent_replay=True)
     args = parser.parse_args(argv)
 
     dataset_root = Path(args.dataset_root)
@@ -250,6 +266,7 @@ def main(argv: Optional[List[str]] = None) -> int:
                 template_episode_index=args.template_episode_index,
                 max_frames=args.max_frames,
                 independent_replay=args.independent_replay,
+                per_episode_templates=args.per_episode_templates,
                 thread_count=args.thread_count,
             )
             workers.append((xvfb, process))

code/scripts/recompute_oven_episode_parallel.py

@@ -0,0 +1,267 @@
+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, Sequence, Tuple
+
+import numpy as np
+import pandas as pd
+
+
+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,
+    _annotate_phase_columns,
+    _derive_templates,
+    _episode_metrics_from_frames,
+    _interventional_validity,
+    _keyframe_subset,
+    _keypoint_discovery,
+    _load_demo,
+    _load_descriptions,
+)
+
+
+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_batch_job(
+    display_num: int,
+    episode_dir: Path,
+    templates_pkl: Path,
+    frame_indices: Sequence[int],
+    checkpoint_stride: int,
+    output_dir: Path,
+) -> subprocess.Popen:
+    runtime_dir = Path(f"/tmp/rr_label_study_parallel_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)
+    env["PYTHONUNBUFFERED"] = "1"
+    return subprocess.Popen(
+        [
+            sys.executable,
+            str(PROJECT_ROOT.joinpath("scripts", "run_oven_frame_batch.py")),
+            "--episode-dir",
+            str(episode_dir),
+            "--templates-pkl",
+            str(templates_pkl),
+            "--frame-indices",
+            *[str(frame_index) for frame_index in frame_indices],
+            "--checkpoint-stride",
+            str(checkpoint_stride),
+            "--output-dir",
+            str(output_dir),
+            "--independent-replay",
+        ],
+        stdout=subprocess.DEVNULL,
+        stderr=subprocess.DEVNULL,
+        cwd=str(PROJECT_ROOT),
+        env=env,
+        start_new_session=True,
+    )
+
+
+def _chunk_frame_indices(frame_indices: Sequence[int], num_workers: int) -> List[List[int]]:
+    if not frame_indices:
+        return []
+    worker_count = min(max(1, num_workers), len(frame_indices))
+    chunk_size = math.ceil(len(frame_indices) / worker_count)
+    chunks: List[List[int]] = []
+    for worker_index in range(worker_count):
+        start = worker_index * chunk_size
+        chunk = list(frame_indices[start : start + chunk_size])
+        if chunk:
+            chunks.append(chunk)
+    return chunks
+
+
+def _collect_rows(frame_json_dir: Path, num_frames: int) -> pd.DataFrame:
+    rows: List[Dict[str, float]] = []
+    for frame_index in range(num_frames):
+        row_path = frame_json_dir.joinpath(f"frame_{frame_index:04d}.json")
+        if not row_path.exists():
+            raise RuntimeError(f"missing recomputed frame output: {row_path}")
+        with row_path.open("r", encoding="utf-8") as handle:
+            rows.append(json.load(handle))
+    frame_df = pd.DataFrame(rows).sort_values("frame_index").reset_index(drop=True)
+    return frame_df
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset-root", required=True)
+    parser.add_argument("--episode-dir", 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=8)
+    parser.add_argument("--base-display", type=int, default=380)
+    parser.add_argument("--template-episode-dir")
+    parser.add_argument("--stagger-seconds", type=float, default=0.15)
+    parser.add_argument("--keep-frame-json", action="store_true")
+    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)
+
+    demo = _load_demo(episode_dir)
+    descriptions = _load_descriptions(episode_dir)
+    num_frames = len(demo)
+    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(
+            {
+                "template_mode": "per_episode",
+                "template_episode": template_episode_dir.name,
+                "template_frames": template_frames,
+                "templates": templates.to_json(),
+            },
+            handle,
+            indent=2,
+        )
+
+    frame_json_dir = output_dir.joinpath("frame_rows")
+    frame_json_dir.mkdir(parents=True, exist_ok=True)
+    frame_indices = list(range(num_frames))
+    frame_chunks = _chunk_frame_indices(frame_indices, args.num_workers)
+    displays = [args.base_display + index for index in range(len(frame_chunks))]
+    xvfb_procs: List[subprocess.Popen] = []
+    active: Dict[int, Tuple[List[int], subprocess.Popen]] = {}
+
+    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)
+
+        for display_num, frame_chunk in zip(displays, frame_chunks):
+            process = _spawn_frame_batch_job(
+                display_num=display_num,
+                episode_dir=episode_dir,
+                templates_pkl=templates_pkl,
+                frame_indices=frame_chunk,
+                checkpoint_stride=args.checkpoint_stride,
+                output_dir=frame_json_dir,
+            )
+            active[display_num] = (frame_chunk, process)
+            if args.stagger_seconds > 0:
+                time.sleep(args.stagger_seconds)
+
+        while active:
+            time.sleep(1.0)
+            finished: List[int] = []
+            for display_num, (frame_chunk, process) in active.items():
+                return_code = process.poll()
+                if return_code is None:
+                    continue
+                missing = [
+                    frame_index
+                    for frame_index in frame_chunk
+                    if not frame_json_dir.joinpath(f"frame_{frame_index:04d}.json").exists()
+                ]
+                if return_code != 0 or missing:
+                    raise RuntimeError(
+                        f"display :{display_num} failed for frames {frame_chunk[:3]}...; missing={missing[:8]}"
+                    )
+                finished.append(display_num)
+            for display_num in finished:
+                active.pop(display_num)
+    finally:
+        for _, process in list(active.values()):
+            _stop_process(process)
+        for xvfb in xvfb_procs:
+            _stop_process(xvfb)
+
+    frame_df = _collect_rows(frame_json_dir, num_frames)
+    frame_df = _annotate_phase_columns(frame_df)
+    keyframes = [index for index in _keypoint_discovery(demo) if index < len(frame_df)]
+    key_df = _keyframe_subset(frame_df, keyframes)
+    interventions = _interventional_validity(
+        demo=demo,
+        templates=templates,
+        frame_df=frame_df,
+        checkpoint_stride=args.checkpoint_stride,
+    )
+    metrics = _episode_metrics_from_frames(
+        frame_df=frame_df,
+        key_df=key_df,
+        episode_name=episode_dir.name,
+        description=descriptions[0],
+        interventions=interventions,
+    )
+
+    frame_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)
+
+    if not args.keep_frame_json:
+        for row_path in frame_json_dir.glob("frame_*.json"):
+            row_path.unlink()
+        frame_json_dir.rmdir()
+
+    print(json.dumps(summary, indent=2))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

code/scripts/recompute_oven_pregrasp_parallel.py

@@ -0,0 +1,282 @@
+from pathlib import Path
+import argparse
+import json
+import os
+import pickle
+import shutil
+import signal
+import subprocess
+import sys
+import time
+from typing import Dict, List, Optional, Sequence, Tuple
+
+import numpy as np
+import pandas as pd
+
+
+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 (
+    MotionTemplates,
+    _aggregate_summary,
+    _annotate_phase_columns,
+    _episode_metrics_from_frames,
+    _keyframe_subset,
+    _keypoint_discovery,
+    _load_demo,
+    _load_descriptions,
+)
+
+
+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 _wait_for_display(display_num: int, timeout_s: float = 10.0) -> None:
+    deadline = time.time() + timeout_s
+    while time.time() < deadline:
+        result = subprocess.run(
+            ["xdpyinfo", "-display", f":{display_num}"],
+            stdout=subprocess.DEVNULL,
+            stderr=subprocess.DEVNULL,
+            check=False,
+        )
+        if result.returncode == 0:
+            return
+        time.sleep(0.25)
+    raise RuntimeError(f"display :{display_num} did not become ready")
+
+
+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_pregrasp_batch_job(
+    display_num: int,
+    episode_dir: Path,
+    templates_pkl: Path,
+    frame_indices: Sequence[int],
+    checkpoint_stride: int,
+    output_dir: Path,
+    log_path: Path,
+) -> subprocess.Popen:
+    runtime_dir = Path(f"/tmp/rr_label_study_pregrasp_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)
+    env["PYTHONUNBUFFERED"] = "1"
+    env["OMP_NUM_THREADS"] = "1"
+    env["OPENBLAS_NUM_THREADS"] = "1"
+    env["MKL_NUM_THREADS"] = "1"
+    env["NUMEXPR_NUM_THREADS"] = "1"
+    log_handle = log_path.open("w", encoding="utf-8")
+    return subprocess.Popen(
+        [
+            sys.executable,
+            str(PROJECT_ROOT.joinpath("scripts", "run_oven_pregrasp_batch.py")),
+            "--episode-dir",
+            str(episode_dir),
+            "--templates-pkl",
+            str(templates_pkl),
+            "--frame-indices",
+            *[str(frame_index) for frame_index in frame_indices],
+            "--checkpoint-stride",
+            str(checkpoint_stride),
+            "--output-dir",
+            str(output_dir),
+        ],
+        stdout=log_handle,
+        stderr=subprocess.STDOUT,
+        cwd=str(PROJECT_ROOT),
+        env=env,
+        start_new_session=True,
+    )
+
+
+def _chunk_frame_indices(frame_indices: Sequence[int], num_workers: int) -> List[List[int]]:
+    if not frame_indices:
+        return []
+    worker_count = min(max(1, num_workers), len(frame_indices))
+    return [
+        [int(index) for index in chunk.tolist()]
+        for chunk in np.array_split(np.asarray(frame_indices, dtype=int), worker_count)
+        if len(chunk)
+    ]
+
+
+def _load_interventions(metrics_path: Path) -> Dict[str, float]:
+    payload = json.loads(metrics_path.read_text())
+    return {
+        key: float(value)
+        for key, value in payload.items()
+        if key.startswith("pre_ready_") or key.startswith("post_ready_")
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--episode-dir", required=True)
+    parser.add_argument("--input-dense-csv", required=True)
+    parser.add_argument("--input-metrics-json", required=True)
+    parser.add_argument("--templates-json", 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=8)
+    parser.add_argument("--base-display", type=int, default=500)
+    parser.add_argument("--stagger-seconds", type=float, default=0.1)
+    parser.add_argument("--keep-frame-json", action="store_true")
+    args = parser.parse_args()
+
+    episode_dir = Path(args.episode_dir)
+    input_dense_csv = Path(args.input_dense_csv)
+    input_metrics_json = Path(args.input_metrics_json)
+    templates_json = Path(args.templates_json)
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    base_df = pd.read_csv(input_dense_csv)
+    demo = _load_demo(episode_dir)
+    descriptions = _load_descriptions(episode_dir)
+    num_frames = min(len(demo), len(base_df))
+    frame_indices = list(range(num_frames))
+    interventions = _load_interventions(input_metrics_json)
+
+    template_payload = json.loads(templates_json.read_text())
+    templates = MotionTemplates.from_json(template_payload["templates"])
+    with output_dir.joinpath("templates.json").open("w", encoding="utf-8") as handle:
+        json.dump(template_payload, handle, indent=2)
+    templates_pkl = output_dir.joinpath("templates.pkl")
+    with templates_pkl.open("wb") as handle:
+        pickle.dump(templates, handle)
+
+    frame_json_dir = output_dir.joinpath("pregrasp_rows")
+    frame_json_dir.mkdir(parents=True, exist_ok=True)
+    pending_frame_indices = [
+        frame_index
+        for frame_index in frame_indices
+        if not frame_json_dir.joinpath(f"frame_{frame_index:04d}.json").exists()
+    ]
+    frame_chunks = _chunk_frame_indices(pending_frame_indices, args.num_workers)
+    displays = [args.base_display + index for index in range(len(frame_chunks))]
+    xvfb_procs: List[subprocess.Popen] = []
+    active: Dict[int, Tuple[List[int], subprocess.Popen]] = {}
+
+    try:
+        for display_num in displays:
+            xvfb = _launch_xvfb(display_num, output_dir.joinpath(f"xvfb_{display_num}.log"))
+            xvfb_procs.append(xvfb)
+        for display_num in displays:
+            _wait_for_display(display_num)
+
+        for display_num, frame_chunk in zip(displays, frame_chunks):
+            process = _spawn_pregrasp_batch_job(
+                display_num=display_num,
+                episode_dir=episode_dir,
+                templates_pkl=templates_pkl,
+                frame_indices=frame_chunk,
+                checkpoint_stride=args.checkpoint_stride,
+                output_dir=frame_json_dir,
+                log_path=output_dir.joinpath(f"worker_{display_num}.log"),
+            )
+            active[display_num] = (frame_chunk, process)
+            if args.stagger_seconds > 0:
+                time.sleep(args.stagger_seconds)
+
+        while active:
+            time.sleep(1.0)
+            finished: List[int] = []
+            for display_num, (frame_chunk, process) in active.items():
+                return_code = process.poll()
+                if return_code is None:
+                    continue
+                missing = [
+                    frame_index
+                    for frame_index in frame_chunk
+                    if not frame_json_dir.joinpath(f"frame_{frame_index:04d}.json").exists()
+                ]
+                if return_code != 0 or missing:
+                    raise RuntimeError(
+                        "display "
+                        f":{display_num} failed for frames {frame_chunk[:5]} "
+                        f"missing={missing[:8]} log={output_dir.joinpath(f'worker_{display_num}.log')}"
+                    )
+                finished.append(display_num)
+            for display_num in finished:
+                active.pop(display_num)
+    finally:
+        for _, process in list(active.values()):
+            _stop_process(process)
+        for xvfb in xvfb_procs:
+            _stop_process(xvfb)
+
+    corrected_df = base_df.iloc[:num_frames].copy()
+    for frame_index in frame_indices:
+        row_path = frame_json_dir.joinpath(f"frame_{frame_index:04d}.json")
+        if not row_path.exists():
+            raise RuntimeError(f"missing pregrasp row: {row_path}")
+        row = json.loads(row_path.read_text())
+        for key, value in row.items():
+            corrected_df.at[frame_index, key] = value
+
+    corrected_df = _annotate_phase_columns(corrected_df)
+    keyframes = [index for index in _keypoint_discovery(demo) if index < len(corrected_df)]
+    key_df = _keyframe_subset(corrected_df, keyframes)
+    metrics = _episode_metrics_from_frames(
+        frame_df=corrected_df,
+        key_df=key_df,
+        episode_name=episode_dir.name,
+        description=descriptions[0],
+        interventions=interventions,
+    )
+
+    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)
+
+    if not args.keep_frame_json:
+        shutil.rmtree(frame_json_dir, ignore_errors=True)
+
+    print(json.dumps(summary, indent=2))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

code/scripts/recompute_oven_visibility_pregrasp.py

@@ -0,0 +1,147 @@
+from pathlib import Path
+import argparse
+import json
+import sys
+from typing import Dict, Optional
+
+import pandas as pd
+
+
+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,
+    ReplayCache,
+    _aggregate_summary,
+    _annotate_phase_columns,
+    _analyze_episode,
+    _derive_templates,
+    _episode_metrics_from_frames,
+    _keyframe_subset,
+    _keypoint_discovery,
+    _launch_replay_env,
+    _load_demo,
+    _load_descriptions,
+    _pregrasp_progress_and_distance,
+    _pregrasp_score_and_success,
+    _frame_metrics,
+)
+
+
+def _recompute_columns(
+    episode_dir: Path,
+    templates,
+    checkpoint_stride: int,
+    base_df: pd.DataFrame,
+) -> pd.DataFrame:
+    demo = _load_demo(episode_dir)
+    num_frames = min(len(demo), len(base_df))
+    frame_df = base_df.iloc[:num_frames].copy()
+
+    env = _launch_replay_env()
+    try:
+        task = env.get_task(BimanualTakeTrayOutOfOven)
+        cache = ReplayCache(task, demo, checkpoint_stride=checkpoint_stride)
+        cache.reset()
+
+        for frame_index in range(num_frames):
+            cache.step_to(frame_index)
+            state = cache.current_state()
+            visibility = _frame_metrics(episode_dir, demo, state, templates)
+            pregrasp_progress, pregrasp_distance = _pregrasp_progress_and_distance(
+                state.left_gripper_pose,
+                state.tray_pose,
+                templates,
+            )
+            p_pre, y_pre = _pregrasp_score_and_success(task, templates)
+
+            frame_df.at[frame_index, "frame_index"] = frame_index
+            frame_df.at[frame_index, "time_norm"] = frame_index / max(1, num_frames - 1)
+            frame_df.at[frame_index, "door_angle"] = state.door_angle
+            frame_df.at[frame_index, "right_gripper_open"] = state.right_gripper_open
+            frame_df.at[frame_index, "left_gripper_open"] = state.left_gripper_open
+            frame_df.at[frame_index, "pregrasp_progress"] = pregrasp_progress
+            frame_df.at[frame_index, "pregrasp_distance"] = pregrasp_distance
+            frame_df.at[frame_index, "p_pre"] = p_pre
+            frame_df.at[frame_index, "y_pre_raw"] = float(bool(y_pre))
+            frame_df.at[frame_index, "y_pre"] = float(bool(y_pre))
+            for key, value in visibility.items():
+                frame_df.at[frame_index, key] = value
+
+            if (frame_index + 1) % 25 == 0 or (frame_index + 1) == num_frames:
+                print(
+                    f"[{episode_dir.name}] recomputed {frame_index + 1}/{num_frames} dense frames",
+                    flush=True,
+                )
+        return frame_df
+    finally:
+        env.shutdown()
+
+
+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("--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)
+    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,
+        )
+
+    frame_df = _recompute_columns(
+        episode_dir=episode_dir,
+        templates=templates,
+        checkpoint_stride=args.checkpoint_stride,
+        base_df=base_df,
+    )
+    frame_df = _annotate_phase_columns(frame_df)
+
+    keyframes = [index for index in _keypoint_discovery(demo) if index < len(frame_df)]
+    key_df = _keyframe_subset(frame_df, keyframes)
+    metrics = _episode_metrics_from_frames(
+        frame_df=frame_df,
+        key_df=key_df,
+        episode_name=episode_dir.name,
+        description=descriptions[0],
+        interventions={},
+    )
+
+    frame_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(summary, indent=2))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

code/scripts/render_oven_metric_frame.py

@@ -24,16 +24,19 @@ from rr_label_study.oven_study import (
     MotionTemplates,
     ReplayCache,
     Shape,
+    _apply_relative_pose,
+    _camera_point_cloud,
     _camera_file,
     _extract_sequence_poses,
     _frame_metrics,
     _launch_replay_env,
     _load_demo,
     _load_mask,
-    _pregrasp_candidates,
+    _pregrasp_corridor_rel_poses,
     _project_points,
     _sample_full_tray_points,
     _sample_grasp_points,
+    _visibility_projection_details,
 )
 
 
@@ -132,25 +135,13 @@ def _bar(draw: ImageDraw.ImageDraw, x: int, y: int, w: int, h: int, value: float
 
 def _project_coords(
     points_world: np.ndarray,
-    mask: np.ndarray,
-    handle_ids: Sequence[int],
+    point_cloud_world: np.ndarray,
     extrinsics: np.ndarray,
     intrinsics: np.ndarray,
 ) -> Tuple[List[Tuple[int, int]], List[Tuple[int, int]]]:
-    uv, camera_xyz = _project_points(points_world, extrinsics, intrinsics)
-    handle_set = {int(handle) for handle in handle_ids}
-    height, width = mask.shape
-    projected: List[Tuple[int, int]] = []
-    visible: List[Tuple[int, int]] = []
-    for (u, v), (_, _, camera_depth) in zip(uv, camera_xyz):
-        if camera_depth <= 0 or not (0 <= u < width and 0 <= v < height):
-            continue
-        px = min(max(int(round(float(u))), 0), width - 1)
-        py = min(max(int(round(float(v))), 0), height - 1)
-        projected.append((px, py))
-        if int(mask[py, px]) in handle_set:
-            visible.append((px, py))
-    return projected, visible
+    return _visibility_projection_details(
+        points_world, point_cloud_world, extrinsics, intrinsics
+    )
 
 
 def _infer_mask_handle(mask: np.ndarray, coords: Sequence[Tuple[int, int]]) -> Optional[int]:
@@ -202,15 +193,18 @@ def _compose_visibility_panel(
     per_camera_vis: float,
 ) -> Image.Image:
     rgb = _load_rgb(episode_dir, camera_name, frame_index)
-    mask = _load_mask(episode_dir, frame_index, camera_name)
-    extrinsics = demo[frame_index].misc[f"{camera_name}_camera_extrinsics"]
-    intrinsics = demo[frame_index].misc[f"{camera_name}_camera_intrinsics"]
-    handle_ids = templates.mask_handle_ids
+    point_cloud, extrinsics, intrinsics = _camera_point_cloud(
+        episode_dir, demo, frame_index, camera_name
+    )
 
     tray_points = _sample_full_tray_points(tray_pose)
     grasp_points = _sample_grasp_points(templates, tray_pose)
-    tray_proj, tray_visible = _project_coords(tray_points, mask, handle_ids, extrinsics, intrinsics)
-    grasp_proj, grasp_visible = _project_coords(grasp_points, mask, handle_ids, extrinsics, intrinsics)
+    tray_proj, tray_visible = _project_coords(
+        tray_points, point_cloud, extrinsics, intrinsics
+    )
+    grasp_proj, grasp_visible = _project_coords(
+        grasp_points, point_cloud, extrinsics, intrinsics
+    )
 
     scene_panel = _scale_image(rgb)
     _draw_panel_title(
@@ -243,16 +237,14 @@ def _compose_visibility_panel(
     if right_xy is not None:
         _draw_point(draw, _scaled_coords([right_xy], PANEL_SIZE / RGB_SIZE)[0], (140, 110, 255), radius=4)
 
-    if not handle_ids:
-        masked_panel = _new_panel()
-        draw = ImageDraw.Draw(masked_panel)
-        draw.text((10, PANEL_SIZE // 2 - 10), "no tray mask pixels", fill=(180, 180, 180), font=FONT_MD)
-    else:
-        masked_panel = _masked_rgb(rgb, np.isin(mask, np.asarray(handle_ids, dtype=np.int64)))
-        masked_panel = _scale_image(masked_panel)
+    visible_pixels = np.zeros((RGB_SIZE, RGB_SIZE), dtype=bool)
+    for x, y in tray_visible + grasp_visible:
+        visible_pixels[y, x] = True
+    masked_panel = _masked_rgb(rgb, visible_pixels)
+    masked_panel = _scale_image(masked_panel)
     _draw_panel_title(
         masked_panel,
-        f"{camera_name} visible mask",
+        f"{camera_name} depth-visible",
         f"visible grasp pts={len(grasp_visible)}/{len(grasp_proj) or 1}",
     )
     draw = ImageDraw.Draw(masked_panel)
@@ -313,11 +305,11 @@ def _camera_grasp_visibility(
     tray_pose: np.ndarray,
     templates: MotionTemplates,
 ) -> float:
-    mask = _load_mask(episode_dir, frame_index, camera_name)
-    extrinsics = demo[frame_index].misc[f"{camera_name}_camera_extrinsics"]
-    intrinsics = demo[frame_index].misc[f"{camera_name}_camera_intrinsics"]
+    point_cloud, extrinsics, intrinsics = _camera_point_cloud(
+        episode_dir, demo, frame_index, camera_name
+    )
     grasp_points = _sample_grasp_points(templates, tray_pose)
-    proj, vis = _project_coords(grasp_points, mask, templates.mask_handle_ids, extrinsics, intrinsics)
+    proj, vis = _project_coords(grasp_points, point_cloud, extrinsics, intrinsics)
     return float(len(vis) / len(proj)) if proj else 0.0
 
 
@@ -344,10 +336,18 @@ def _compose_path_quality(
         font=FONT_SM,
     )
 
-    current_pregrasp = _pregrasp_candidates(tray_pose, templates)[0]
+    pregrasp_corridor = [
+        _apply_relative_pose(tray_pose, rel_pose)
+        for rel_pose in _pregrasp_corridor_rel_poses(templates)
+    ]
     extract_poses = _extract_sequence_poses(tray_pose, task_base_pose, templates)
-    plan_poses = [current_pregrasp, extract_poses[1], *extract_poses[2:5]]
-    colors = [(255, 220, 0), (255, 140, 0), (80, 255, 120), (80, 255, 120), (80, 255, 120)]
+    plan_poses = [*pregrasp_corridor, extract_poses[1], *extract_poses[2:5]]
+    colors = ([(255, 220, 0)] * len(pregrasp_corridor)) + [
+        (255, 140, 0),
+        (80, 255, 120),
+        (80, 255, 120),
+        (80, 255, 120),
+    ]
 
     demo_left_trail = np.asarray(
         [_demo_gripper_pose(demo[i], "left")[:3] for i in range(max(0, frame_index - 12), frame_index + 1)],
@@ -415,6 +415,7 @@ def _project_world_polyline(points_world: np.ndarray, extrinsics: np.ndarray, in
 
 
 def _compose_all_metrics(
+    episode_name: str,
     episode_dir: Path,
     demo,
     frame_index: int,
@@ -426,7 +427,7 @@ def _compose_all_metrics(
     draw.rectangle((0, 0, canvas.size[0], 36), fill=banner_color)
     draw.text(
         (12, 7),
-        f"Episode 0 | frame {frame_index:03d} | {'REVEAL' if int(frame_row['phase_switch']) == 0 else 'RETRIEVE'}",
+        f"{episode_name} | frame {frame_index:03d} | {'REVEAL' if int(frame_row['phase_switch']) == 0 else 'RETRIEVE'}",
         fill=(255, 255, 255),
         font=FONT_LG,
     )
@@ -534,6 +535,7 @@ def main() -> int:
 
         if all_out is not None:
             all_image = _compose_all_metrics(
+                episode_name=episode_dir.name,
                 episode_dir=episode_dir,
                 demo=demo,
                 frame_index=int(args.frame_index),

code/scripts/render_oven_metric_gifs.py

@@ -8,6 +8,8 @@ import time
 from typing import Dict, List, Optional, Tuple
 
 from PIL import Image
+import numpy as np
+import pandas as pd
 
 
 PROJECT_ROOT = Path(__file__).resolve().parents[1]
@@ -113,6 +115,12 @@ def _assemble_gif(frame_paths: List[Path], output_path: Path, duration_ms: int)
     )
 
 
+def _range_inclusive(start: int, end: int, step: int) -> List[int]:
+    if end < start:
+        return []
+    return list(range(start, end + 1, step))
+
+
 def main() -> int:
     parser = argparse.ArgumentParser()
     parser.add_argument(
@@ -140,6 +148,21 @@ def main() -> int:
     dense_csv = Path(args.dense_csv)
     templates_pkl = Path(args.templates_pkl)
     output_dir = Path(args.output_dir)
+    frame_df = pd.read_csv(dense_csv)
+    episode_name = episode_dir.name
+    frame_indices = (
+        frame_df["frame_index"].to_numpy(dtype=int)
+        if "frame_index" in frame_df
+        else np.arange(len(frame_df), dtype=int)
+    )
+    max_frame = int(frame_indices.max()) if len(frame_indices) else 0
+    phase_candidates = frame_df.loc[frame_df["phase_switch"].to_numpy(dtype=float) >= 0.5]
+    if len(phase_candidates):
+        phase_cross = int(phase_candidates.iloc[0]["frame_index"])
+    else:
+        ppre_candidates = frame_df.loc[frame_df["p_pre"].to_numpy(dtype=float) >= 0.45]
+        phase_cross = int(ppre_candidates.iloc[0]["frame_index"]) if len(ppre_candidates) else max_frame // 2
+
     frames_dir = output_dir.joinpath("frames")
     visibility_dir = frames_dir.joinpath("visibility_focus")
     path_dir = frames_dir.joinpath("path_quality_focus")
@@ -147,9 +170,9 @@ def main() -> int:
     for directory in [visibility_dir, path_dir, all_dir]:
         directory.mkdir(parents=True, exist_ok=True)
 
-    visibility_frames = list(range(220, 261, 1))
-    path_frames = list(range(228, 316, 2))
-    all_frames = list(range(0, 329, 2))
+    visibility_frames = _range_inclusive(max(0, phase_cross - 12), min(max_frame, phase_cross + 28), 1)
+    path_frames = _range_inclusive(max(0, phase_cross - 8), min(max_frame, phase_cross + 80), 2)
+    all_frames = _range_inclusive(0, max_frame, 2)
     unique_frames = sorted(set(visibility_frames) | set(path_frames) | set(all_frames))
 
     pending = unique_frames[:]
@@ -208,9 +231,21 @@ def main() -> int:
         if not path.exists():
             raise RuntimeError(f"missing rendered frame: {path}")
 
-    _assemble_gif(visibility_pngs, output_dir.joinpath("episode0_visibility_focus.gif"), duration_ms=120)
-    _assemble_gif(path_pngs, output_dir.joinpath("episode0_path_quality_focus.gif"), duration_ms=120)
-    _assemble_gif(all_pngs, output_dir.joinpath("episode0_all_metrics.gif"), duration_ms=100)
+    _assemble_gif(
+        visibility_pngs,
+        output_dir.joinpath(f"{episode_name}_visibility_focus.gif"),
+        duration_ms=120,
+    )
+    _assemble_gif(
+        path_pngs,
+        output_dir.joinpath(f"{episode_name}_path_quality_focus.gif"),
+        duration_ms=120,
+    )
+    _assemble_gif(
+        all_pngs,
+        output_dir.joinpath(f"{episode_name}_all_metrics.gif"),
+        duration_ms=100,
+    )
 
     readme = output_dir.joinpath("README.md")
     readme.write_text(
@@ -218,9 +253,9 @@ def main() -> int:
             [
                 "# Visualizations",
                 "",
-                "- `episode0_visibility_focus.gif`: three-view visibility debugging montage over dense frames 220-260.",
-                "- `episode0_path_quality_focus.gif`: path-quality / grasp-quality montage over dense frames 228-314.",
-                "- `episode0_all_metrics.gif`: full episode overlay GIF over dense frames 0-328, sampled every 2 frames.",
+                f"- `{episode_name}_visibility_focus.gif`: three-view visibility montage over dense frames {visibility_frames[0]}-{visibility_frames[-1]}.",
+                f"- `{episode_name}_path_quality_focus.gif`: path-quality montage over dense frames {path_frames[0]}-{path_frames[-1]}.",
+                f"- `{episode_name}_all_metrics.gif`: full episode overlay GIF over dense frames 0-{max_frame}, sampled every 2 frames.",
                 "- `frames/visibility_focus/`: per-frame PNGs with scene overlays, x-ray projections, and tray-mask views.",
                 "- `frames/path_quality_focus/`: per-frame PNGs with demo wrist trails plus planned pregrasp/grasp/retreat path overlays.",
                 "- `frames/all_metrics/`: per-frame PNGs with the episode-wide metric bars and phase banner.",

code/scripts/run_oven_frame_batch.py

@@ -9,7 +9,11 @@ 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_rows_sequential, _load_demo
+from rr_label_study.oven_study import (
+    _compute_frame_rows_independent,
+    _compute_frame_rows_sequential,
+    _load_demo,
+)
 
 
 def main() -> int:
@@ -19,13 +23,19 @@ def main() -> int:
     parser.add_argument("--frame-indices", nargs="+", type=int, required=True)
     parser.add_argument("--checkpoint-stride", type=int, default=16)
     parser.add_argument("--output-dir", required=True)
+    parser.add_argument("--independent-replay", action="store_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)
-    rows = _compute_frame_rows_sequential(
+    compute_rows = (
+        _compute_frame_rows_independent
+        if args.independent_replay
+        else _compute_frame_rows_sequential
+    )
+    rows = compute_rows(
         episode_dir=episode_dir,
         demo=demo,
         templates=templates,

code/scripts/run_oven_pregrasp_batch.py

@@ -0,0 +1,94 @@
+from pathlib import Path
+import argparse
+import json
+import pickle
+import sys
+
+import numpy as np
+
+
+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,
+    ReplayCache,
+    _launch_replay_env,
+    _load_demo,
+    _pregrasp_progress_and_distance,
+    _pregrasp_score_and_success,
+)
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--episode-dir", required=True)
+    parser.add_argument("--templates-pkl", required=True)
+    parser.add_argument("--frame-indices", nargs="+", type=int, required=True)
+    parser.add_argument("--checkpoint-stride", type=int, default=16)
+    parser.add_argument("--output-dir", required=True)
+    args = parser.parse_args()
+
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    frame_indices = sorted(set(args.frame_indices))
+    pending_frame_indices = [
+        frame_index
+        for frame_index in frame_indices
+        if not output_dir.joinpath(f"frame_{frame_index:04d}.json").exists()
+    ]
+    if not pending_frame_indices:
+        return 0
+
+    episode_dir = Path(args.episode_dir)
+    with Path(args.templates_pkl).open("rb") as handle:
+        templates = pickle.load(handle)
+    demo = _load_demo(episode_dir)
+
+    env = _launch_replay_env()
+    try:
+        task = env.get_task(BimanualTakeTrayOutOfOven)
+        cache = ReplayCache(task, demo, checkpoint_stride=args.checkpoint_stride)
+        cache.reset()
+        total = len(pending_frame_indices)
+        for completed, frame_index in enumerate(pending_frame_indices, start=1):
+            cache.step_to(frame_index)
+            state = cache.current_state()
+            pregrasp_progress, pregrasp_distance = _pregrasp_progress_and_distance(
+                np.asarray(state.left_gripper_pose, dtype=np.float64),
+                np.asarray(state.tray_pose, dtype=np.float64),
+                templates,
+            )
+            p_pre, y_pre = _pregrasp_score_and_success(task, templates)
+            row = {
+                "frame_index": int(frame_index),
+                "pregrasp_progress": float(pregrasp_progress),
+                "pregrasp_distance": float(pregrasp_distance),
+                "p_pre": float(p_pre),
+                "y_pre_raw": float(bool(y_pre)),
+                "y_pre": float(bool(y_pre)),
+            }
+            row_path = output_dir.joinpath(f"frame_{frame_index:04d}.json")
+            tmp_path = row_path.with_suffix(".json.tmp")
+            with tmp_path.open("w", encoding="utf-8") as handle:
+                json.dump(row, handle)
+            tmp_path.replace(row_path)
+            if completed == total or completed % 8 == 0:
+                print(
+                    json.dumps(
+                        {
+                            "done": completed,
+                            "total": total,
+                            "frame_index": int(frame_index),
+                        }
+                    ),
+                    flush=True,
+                )
+    finally:
+        env.shutdown()
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())