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README.md

@@ -55,6 +55,7 @@ The old `iter4_*` outputs are still useful historical checkpoints, but the curre
   - `repair_oven_episode_dense.py`: batched repair pass for suspicious dense rows.
   - `render_oven_metric_frame.py`: per-frame visualization renderer.
   - `render_oven_metric_gifs.py`: GIF renderer.
+  - The visualization renderer now accepts either legacy `templates.pkl` files or the newer authoritative `templates.json` bundles.
 - `artifacts/results/`
   - Full debug history, including stale runs and current validation outputs.
 - `environment/`
@@ -152,6 +153,11 @@ The current trustworthy artifacts are:
 - `artifacts/results/oven_episode0_iter6_independent_full/episode0.metrics.json`
 - `artifacts/results/oven_episode0_iter6_independent_full/summary.json`
 - `artifacts/results/oven_episode0_iter6_visual_checks/early_visibility_contact_sheet.png`
+- `artifacts/results/oven_episode0_iter16_gif_suite/episode0.dense.csv`
+- `artifacts/results/oven_episode0_iter16_gif_suite/episode0.metrics.json`
+- `artifacts/results/oven_episode0_iter16_gif_suite/visualizations/episode0_all_metrics.gif`
+- `artifacts/results/oven_episode0_iter16_gif_suite/visualizations/episode0_visibility_focus.gif`
+- `artifacts/results/oven_episode0_iter16_gif_suite/visualizations/episode0_path_quality_focus.gif`
 - `artifacts/results/manual_metric_checks/episode0_frame210_visibility.png`
 - `artifacts/results/manual_metric_checks/episode0_frame232_visibility.png`
 - `artifacts/results/manual_metric_checks/episode0_frame210_path.png`
@@ -284,6 +290,11 @@ Relevant current artifacts:
 - `artifacts/results/oven_episode0_iter6_independent_full/summary.json`
 - `artifacts/results/oven_episode0_iter6_visual_checks/boundary_rgb_contact_sheet.png`
 - `artifacts/results/oven_episode0_iter6_visual_checks/early_visibility_contact_sheet.png`
+- `artifacts/results/oven_episode0_iter16_gif_suite/episode0.dense.csv`
+- `artifacts/results/oven_episode0_iter16_gif_suite/episode0.metrics.json`
+- `artifacts/results/oven_episode0_iter16_gif_suite/visualizations/episode0_all_metrics.gif`
+- `artifacts/results/oven_episode0_iter16_gif_suite/visualizations/episode0_visibility_focus.gif`
+- `artifacts/results/oven_episode0_iter16_gif_suite/visualizations/episode0_path_quality_focus.gif`
 
 ## Environment
 

artifacts/results/oven_episode0_iter16_gif_suite/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,y_ext,three_view_visibility,three_view_whole_tray_visibility,full_view_visibility,full_view_whole_tray_visibility,door_speed_abs,pregrasp_speed,phase_score,approach_active,y_retrieve,y_ready,phase_switch,y_pre_progress_seed
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artifacts/results/oven_episode0_iter16_gif_suite/episode0.metrics.json

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artifacts/results/oven_episode0_iter16_gif_suite/summary.json

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artifacts/results/oven_episode0_iter16_gif_suite/templates.json

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+      0.004
+    ],
+    "hold_open_angle": 0.7285879850387573,
+    "open_more_delta": 0.12,
+    "reference_tray_height": 1.0472617149353027,
+    "mask_handle_ids": [
+      163
+    ]
+  },
+  "template_episode": "episode0",
+  "template_frames": {
+    "pregrasp": 229,
+    "grasp": 234,
+    "right_close": 91,
+    "right_open": 130,
+    "approach": [
+      177,
+      187,
+      197,
+      208,
+      218,
+      229,
+      232
+    ],
+    "retreat": [
+      239,
+      244,
+      249,
+      254,
+      264,
+      274,
+      284,
+      295,
+      305,
+      310,
+      315
+    ]
+  },
+  "episode_offset": 0
+}

code/rr_label_study/oven_study.py

@@ -53,6 +53,7 @@ DEFAULT_DOOR_SPEED_TAU = 0.08
 DEFAULT_PHASE_SCORE_TAU = 0.5
 DEFAULT_APPROACH_SPEED_TAU = 0.01
 DEFAULT_APPROACH_PROGRESS_TAU = 0.02
+DEFAULT_PREGRASP_LABEL_PROGRESS_TAU = 0.60
 DEFAULT_APPROACH_ONSET_WINDOW = 96
 DEFAULT_PREGRASP_CANDIDATE_COUNT = 4
 DEFAULT_PLAN_TRIALS = 48
@@ -63,6 +64,7 @@ DEFAULT_PLAN_ATTEMPTS = 2
 DEFAULT_PLAN_MIN_SUCCESSES = 2
 DEFAULT_READY_PERSISTENCE = 3
 DEFAULT_RETRIEVE_PERSISTENCE = 3
+DEFAULT_PREGRASP_PERSISTENCE = 3
 DEFAULT_MASK_HANDLE_COUNT = 2
 
 
@@ -1013,16 +1015,61 @@ def _pregrasp_score_and_success(task, templates: MotionTemplates) -> Tuple[float
         current_pose, tray_pose, templates
     )
     best = progress
-    success = distance_to_pregrasp <= 0.08 or progress >= 0.50
-    for pose in _pregrasp_candidates(tray_pose, templates):
-        proximity = math.exp(-float(np.linalg.norm(current_pose[:3] - pose[:3])) / 0.09)
-        best = max(best, 0.7 * progress + 0.3 * proximity)
-        path = _plan_path(task._scene, "left", pose, ignore_collisions=False)
-        length = _path_length(path)
-        if np.isfinite(length):
-            success = True
-            planner_score = math.exp(-length / DEFAULT_PATH_SCALE)
-            best = max(best, 0.6 * progress + 0.25 * planner_score + 0.15 * proximity)
+    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
 
 
@@ -1202,6 +1249,8 @@ def _compute_frame_rows_sequential(
                     "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,
@@ -1267,8 +1316,11 @@ def _annotate_phase_columns(frame_df: pd.DataFrame) -> pd.DataFrame:
     door_speed = np.gradient(frame_df["door_angle"].to_numpy(dtype=float), DEMO_DT)
     frame_df["door_speed_abs"] = np.abs(door_speed)
 
-    y_pre_binary = frame_df["y_pre"].to_numpy(dtype=bool)
-    y_ext_binary = frame_df["y_ext"].to_numpy(dtype=bool)
+    y_ext_raw = (
+        frame_df["y_ext_raw"].to_numpy(dtype=bool)
+        if "y_ext_raw" in frame_df
+        else frame_df["y_ext"].to_numpy(dtype=bool)
+    )
     pregrasp_progress = (
         frame_df["pregrasp_progress"].to_numpy(dtype=float)
         if "pregrasp_progress" in frame_df
@@ -1282,6 +1334,17 @@ 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
+    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"] = 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,
@@ -1563,6 +1626,8 @@ def _analyze_episode(
                     "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,
@@ -1632,6 +1697,7 @@ def run_study(
     max_frames: Optional[int] = None,
     episode_offset: int = 0,
     template_episode_index: int = 0,
+    episode_indices: Optional[Sequence[int]] = None,
     independent_replay: bool = False,
 ) -> Dict[str, object]:
     dataset_path = Path(dataset_root)
@@ -1646,12 +1712,31 @@ def run_study(
             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]
+    selected_episode_indices: List[int]
+    if episode_indices is not None:
+        selected_episode_indices = []
+        seen_episode_indices = set()
+        for raw_index in episode_indices:
+            episode_index = int(raw_index)
+            if not (0 <= episode_index < len(all_episode_dirs)):
+                raise ValueError(
+                    f"episode index {episode_index} outside available range 0..{len(all_episode_dirs) - 1}"
+                )
+            if episode_index in seen_episode_indices:
+                continue
+            selected_episode_indices.append(episode_index)
+            seen_episode_indices.add(episode_index)
+        episode_dirs = [all_episode_dirs[index] for index in selected_episode_indices]
+    else:
+        episode_dirs = all_episode_dirs[episode_offset:]
+        if max_episodes is not None:
+            episode_dirs = episode_dirs[:max_episodes]
+        selected_episode_indices = [
+            all_episode_dirs.index(episode_dir) for episode_dir in episode_dirs
+        ]
     if not episode_dirs:
         raise RuntimeError(
-            f"no episodes selected under {dataset_root} with offset={episode_offset} max_episodes={max_episodes}"
+            f"no episodes selected under {dataset_root} with offset={episode_offset} max_episodes={max_episodes} episode_indices={episode_indices}"
         )
 
     template_episode_dir = all_episode_dirs[template_episode_index]
@@ -1663,6 +1748,7 @@ def run_study(
                 "template_episode": template_episode_dir.name,
                 "template_frames": template_frames,
                 "episode_offset": episode_offset,
+                "selected_episode_indices": selected_episode_indices,
             },
             handle,
             indent=2,
@@ -1696,6 +1782,17 @@ def run_study(
 
 
 def main(argv: Optional[Sequence[str]] = None) -> int:
+    def _parse_episode_indices(value: str) -> List[int]:
+        indices: List[int] = []
+        for chunk in value.split(","):
+            chunk = chunk.strip()
+            if not chunk:
+                continue
+            indices.append(int(chunk))
+        if not indices:
+            raise argparse.ArgumentTypeError("episode-indices must contain at least one integer")
+        return indices
+
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "--dataset-root",
@@ -1710,6 +1807,7 @@ def main(argv: Optional[Sequence[str]] = None) -> int:
     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("--episode-indices", type=_parse_episode_indices)
     parser.add_argument("--independent-replay", action="store_true")
     args = parser.parse_args(argv)
 
@@ -1721,6 +1819,7 @@ def main(argv: Optional[Sequence[str]] = None) -> int:
         max_frames=args.max_frames,
         episode_offset=args.episode_offset,
         template_episode_index=args.template_episode_index,
+        episode_indices=args.episode_indices,
         independent_replay=args.independent_replay,
     )
     print(json.dumps(summary, indent=2))

code/scripts/launch_parallel_oven_label_study.py

@@ -7,35 +7,72 @@ import subprocess
 import sys
 import time
 from pathlib import Path
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, Sequence, Tuple
 
 
 PROJECT_ROOT = Path(__file__).resolve().parents[1]
 if str(PROJECT_ROOT) not in sys.path:
     sys.path.insert(0, str(PROJECT_ROOT))
 
+
+def _configure_coppeliasim_env() -> None:
+    coppeliasim_root = os.environ.setdefault("COPPELIASIM_ROOT", "/workspace/coppelia_sim")
+    ld_library_path_parts = [
+        part for part in os.environ.get("LD_LIBRARY_PATH", "").split(":") if part
+    ]
+    if coppeliasim_root not in ld_library_path_parts:
+        ld_library_path_parts.insert(0, coppeliasim_root)
+    os.environ["LD_LIBRARY_PATH"] = ":".join(ld_library_path_parts)
+
+
+_configure_coppeliasim_env()
+
 from rr_label_study.oven_study import _aggregate_summary, _episode_dirs
 
 
-def _chunk_specs(
+def _select_episode_indices(
     total_episodes: int,
     episode_offset: int,
     max_episodes: Optional[int],
-    num_workers: int,
-) -> List[Tuple[int, int]]:
+    episode_indices: Optional[Sequence[int]],
+) -> List[int]:
+    if episode_indices is not None:
+        selected: List[int] = []
+        seen = set()
+        for raw_index in episode_indices:
+            episode_index = int(raw_index)
+            if not (0 <= episode_index < total_episodes):
+                raise ValueError(
+                    f"episode index {episode_index} outside available range 0..{total_episodes - 1}"
+                )
+            if episode_index in seen:
+                continue
+            selected.append(episode_index)
+            seen.add(episode_index)
+        return selected
+
     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]] = []
+    return list(range(episode_offset, episode_offset + remaining))
+
+
+def _chunk_episode_indices(
+    episode_indices: Sequence[int],
+    num_workers: int,
+) -> List[List[int]]:
+    if not episode_indices:
+        return []
+    worker_count = min(num_workers, len(episode_indices))
+    chunk_size = math.ceil(len(episode_indices) / worker_count)
+    specs: List[List[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))
+        start = worker_index * chunk_size
+        chunk = list(episode_indices[start : start + chunk_size])
+        if chunk:
+            specs.append(chunk)
     return specs
 
 
@@ -63,12 +100,12 @@ def _launch_worker(
     worker_dir: Path,
     display_num: int,
     dataset_root: str,
-    episode_offset: int,
-    max_episodes: int,
+    episode_indices: Sequence[int],
     checkpoint_stride: int,
     template_episode_index: int,
     max_frames: Optional[int],
     independent_replay: bool,
+    thread_count: int,
 ) -> Tuple[subprocess.Popen, subprocess.Popen]:
     worker_dir.mkdir(parents=True, exist_ok=True)
     xvfb = _launch_xvfb(display_num, worker_dir.joinpath("xvfb.log"))
@@ -84,14 +121,12 @@ def _launch_worker(
         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),
+        "--episode-indices",
+        ",".join(str(index) for index in episode_indices),
     ]
     if max_frames is not None:
         command.extend(["--max-frames", str(max_frames)])
@@ -101,6 +136,22 @@ def _launch_worker(
     env = os.environ.copy()
     env["DISPLAY"] = f":{display_num}"
     env["XDG_RUNTIME_DIR"] = str(runtime_dir)
+    env["PYTHONUNBUFFERED"] = "1"
+    coppeliasim_root = env.get("COPPELIASIM_ROOT", "/workspace/coppelia_sim")
+    env["COPPELIASIM_ROOT"] = coppeliasim_root
+    ld_library_path_parts = [
+        part for part in env.get("LD_LIBRARY_PATH", "").split(":") if part
+    ]
+    if coppeliasim_root not in ld_library_path_parts:
+        ld_library_path_parts.insert(0, coppeliasim_root)
+    env["LD_LIBRARY_PATH"] = ":".join(ld_library_path_parts)
+    thread_count_str = str(thread_count)
+    env["OMP_NUM_THREADS"] = thread_count_str
+    env["OPENBLAS_NUM_THREADS"] = thread_count_str
+    env["MKL_NUM_THREADS"] = thread_count_str
+    env["NUMEXPR_NUM_THREADS"] = thread_count_str
+    env["VECLIB_MAXIMUM_THREADS"] = thread_count_str
+    env["BLIS_NUM_THREADS"] = thread_count_str
 
     worker_log = worker_dir.joinpath("worker.log").open("w", encoding="utf-8")
     process = subprocess.Popen(
@@ -155,15 +206,27 @@ def main(argv: Optional[List[str]] = None) -> int:
     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)
+    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")
     args = parser.parse_args(argv)
 
     dataset_root = Path(args.dataset_root)
     all_episodes = _episode_dirs(dataset_root)
-    chunk_specs = _chunk_specs(
+    explicit_episode_indices = None
+    if args.episode_indices:
+        explicit_episode_indices = [
+            int(chunk.strip()) for chunk in args.episode_indices.split(",") if chunk.strip()
+        ]
+    selected_episode_indices = _select_episode_indices(
         total_episodes=len(all_episodes),
         episode_offset=args.episode_offset,
         max_episodes=args.max_episodes,
+        episode_indices=explicit_episode_indices,
+    )
+    chunk_specs = _chunk_episode_indices(
+        episode_indices=selected_episode_indices,
         num_workers=args.num_workers,
     )
     if not chunk_specs:
@@ -175,29 +238,30 @@ def main(argv: Optional[List[str]] = None) -> int:
     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):
+        for worker_index, worker_episode_indices 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,
+                episode_indices=worker_episode_indices,
                 checkpoint_stride=args.checkpoint_stride,
                 template_episode_index=args.template_episode_index,
                 max_frames=args.max_frames,
                 independent_replay=args.independent_replay,
+                thread_count=args.thread_count,
             )
             workers.append((xvfb, process))
             worker_meta.append(
                 {
                     "worker_index": worker_index,
                     "display_num": display_num,
-                    "episode_offset": episode_offset,
-                    "episode_count": episode_count,
+                    "episode_indices": list(worker_episode_indices),
                 }
             )
+            if args.stagger_seconds > 0:
+                time.sleep(args.stagger_seconds)
 
         for meta, (_, process) in zip(worker_meta, workers):
             return_code = process.wait()

code/scripts/render_oven_metric_frame.py

@@ -119,7 +119,12 @@ def _draw_segmented_polyline(
 def _bar(draw: ImageDraw.ImageDraw, x: int, y: int, w: int, h: int, value: float, label: str, color: Tuple[int, int, int], threshold: Optional[float] = None) -> None:
     draw.text((x, y - 18), f"{label}: {value:.3f}", fill=(255, 255, 255), font=FONT_SM)
     draw.rounded_rectangle((x, y, x + w, y + h), radius=5, outline=(120, 120, 120), fill=(20, 20, 20))
-    draw.rounded_rectangle((x, y, x + int(max(0.0, min(1.0, value)) * w), y + h), radius=5, fill=color)
+    filled_w = int(max(0.0, min(1.0, value)) * w)
+    if filled_w > 0:
+        if filled_w >= 12:
+            draw.rounded_rectangle((x, y, x + filled_w, y + h), radius=5, fill=color)
+        else:
+            draw.rectangle((x, y, x + filled_w, y + h), fill=color)
     if threshold is not None:
         tx = x + int(max(0.0, min(1.0, threshold)) * w)
         draw.line((tx, y - 2, tx, y + h + 2), fill=(255, 255, 255), width=2)
@@ -450,6 +455,15 @@ def _save_if_requested(image: Image.Image, path: Optional[Path]) -> None:
     image.save(path)
 
 
+def _load_templates(path: Path) -> MotionTemplates:
+    if path.suffix.lower() == ".json":
+        with path.open("r", encoding="utf-8") as handle:
+            payload = json.load(handle)
+        return MotionTemplates.from_json(payload["templates"])
+    with path.open("rb") as handle:
+        return pickle.load(handle)
+
+
 def main() -> int:
     parser = argparse.ArgumentParser()
     parser.add_argument("--episode-dir", required=True)
@@ -463,8 +477,7 @@ def main() -> int:
     args = parser.parse_args()
 
     episode_dir = Path(args.episode_dir)
-    with Path(args.templates_pkl).open("rb") as handle:
-        templates = pickle.load(handle)
+    templates = _load_templates(Path(args.templates_pkl))
     frame_df = pd.read_csv(args.dense_csv)
     if "frame_index" in frame_df.columns:
         matches = frame_df.loc[frame_df["frame_index"] == int(args.frame_index)]

code/scripts/run_oven_label_study.py

@@ -1,3 +1,4 @@
+import os
 from pathlib import Path
 import sys
 
@@ -6,6 +7,34 @@ PROJECT_ROOT = Path(__file__).resolve().parents[1]
 if str(PROJECT_ROOT) not in sys.path:
     sys.path.insert(0, str(PROJECT_ROOT))
 
+
+def _configure_thread_env() -> None:
+    # Keep BLAS/OpenMP libraries from spawning machine-wide thread pools per worker.
+    defaults = {
+        "OMP_NUM_THREADS": "1",
+        "OPENBLAS_NUM_THREADS": "1",
+        "MKL_NUM_THREADS": "1",
+        "NUMEXPR_NUM_THREADS": "1",
+        "VECLIB_MAXIMUM_THREADS": "1",
+        "BLIS_NUM_THREADS": "1",
+    }
+    for key, value in defaults.items():
+        os.environ.setdefault(key, value)
+
+
+def _configure_coppeliasim_env() -> None:
+    coppeliasim_root = os.environ.setdefault("COPPELIASIM_ROOT", "/workspace/coppelia_sim")
+    ld_library_path_parts = [
+        part for part in os.environ.get("LD_LIBRARY_PATH", "").split(":") if part
+    ]
+    if coppeliasim_root not in ld_library_path_parts:
+        ld_library_path_parts.insert(0, coppeliasim_root)
+    os.environ["LD_LIBRARY_PATH"] = ":".join(ld_library_path_parts)
+
+
+_configure_thread_env()
+_configure_coppeliasim_env()
+
 from rr_label_study.oven_study import main
 
 

code/scripts/status_parallel_oven_label_study.py

@@ -0,0 +1,109 @@
+import argparse
+import json
+import re
+import subprocess
+from pathlib import Path
+from typing import Dict, List, Optional
+
+
+def _active_worker_dirs(result_dir: Path) -> Dict[str, int]:
+    command = [
+        "pgrep",
+        "-af",
+        f"{result_dir}/worker_",
+    ]
+    try:
+        output = subprocess.check_output(command, text=True)
+    except subprocess.CalledProcessError:
+        return {}
+
+    active: Dict[str, int] = {}
+    for line in output.splitlines():
+        pid_match = re.match(r"\s*(\d+)\s+", line)
+        worker_match = re.search(r"(worker_\d+)", line)
+        if not pid_match or not worker_match:
+            continue
+        active[worker_match.group(1)] = int(pid_match.group(1))
+    return active
+
+
+def _selected_episode_indices(worker_dir: Path) -> Optional[List[int]]:
+    templates_path = worker_dir.joinpath("templates.json")
+    if not templates_path.exists():
+        worker_match = re.fullmatch(r"worker_(\d+)", worker_dir.name)
+        if worker_match:
+            return [int(worker_match.group(1))]
+        return None
+    with templates_path.open("r", encoding="utf-8") as handle:
+        payload = json.load(handle)
+    selected = payload.get("selected_episode_indices")
+    if isinstance(selected, list):
+        return [int(index) for index in selected]
+    episode_offset = payload.get("episode_offset")
+    if episode_offset is not None:
+        return [int(episode_offset)]
+    return None
+
+
+def _worker_status(worker_dir: Path, active_workers: Dict[str, int]) -> Dict[str, object]:
+    name = worker_dir.name
+    summary_path = worker_dir.joinpath("summary.json")
+    worker_log = worker_dir.joinpath("worker.log")
+    selected_episode_indices = _selected_episode_indices(worker_dir)
+    log_text = worker_log.read_text(encoding="utf-8", errors="ignore") if worker_log.exists() else ""
+
+    if summary_path.exists():
+        status = "completed"
+    elif name in active_workers:
+        status = "running"
+    elif re.search(r"Traceback|RuntimeError|signal 11", log_text):
+        status = "crashed"
+    elif worker_log.exists():
+        status = "stalled"
+    else:
+        status = "empty"
+
+    return {
+        "worker": name,
+        "status": status,
+        "pid": active_workers.get(name),
+        "selected_episode_indices": selected_episode_indices,
+        "summary_path": str(summary_path) if summary_path.exists() else None,
+        "worker_log": str(worker_log) if worker_log.exists() else None,
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--result-dir", required=True)
+    args = parser.parse_args()
+
+    result_dir = Path(args.result_dir)
+    worker_dirs = sorted(path for path in result_dir.glob("worker_*") if path.is_dir())
+    active_workers = _active_worker_dirs(result_dir)
+    worker_rows = [_worker_status(worker_dir, active_workers) for worker_dir in worker_dirs]
+
+    rerun_episode_indices: List[int] = []
+    for row in worker_rows:
+        if row["status"] != "crashed":
+            continue
+        selected = row.get("selected_episode_indices") or []
+        rerun_episode_indices.extend(int(index) for index in selected)
+
+    counts: Dict[str, int] = {}
+    for row in worker_rows:
+        status = str(row["status"])
+        counts[status] = counts.get(status, 0) + 1
+
+    payload = {
+        "result_dir": str(result_dir),
+        "counts": counts,
+        "rerun_episode_indices": sorted(set(rerun_episode_indices)),
+        "workers": worker_rows,
+    }
+    print(json.dumps(payload, indent=2))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())