Add iter24 geometry-aware door-contact reruns

.gitattributes

@@ -1191,3 +1191,9 @@ artifacts/debug/iter22_scratch/episode0/render/frames/path_quality_focus/frame_0
 artifacts/debug/iter22_scratch/episode0/render/frames/path_quality_focus/frame_0213.png filter=lfs diff=lfs merge=lfs -text
 artifacts/debug/iter22_scratch/episode0/render/frames/path_quality_focus/frame_0211.png filter=lfs diff=lfs merge=lfs -text
 artifacts/debug/iter22_scratch/episode0/render/frames/path_quality_focus/frame_0209.png filter=lfs diff=lfs merge=lfs -text
+artifacts/results/metric_iter24_ep0_door_contact_geom/visualizations/episode0_all_metrics.gif filter=lfs diff=lfs merge=lfs -text
+artifacts/results/metric_iter24_ep0_door_contact_geom/visualizations/episode0_path_quality_focus.gif filter=lfs diff=lfs merge=lfs -text
+artifacts/results/metric_iter24_ep0_door_contact_geom/visualizations/episode0_visibility_focus.gif filter=lfs diff=lfs merge=lfs -text
+artifacts/results/metric_iter24_ep1_door_contact_geom/visualizations/episode1_all_metrics.gif filter=lfs diff=lfs merge=lfs -text
+artifacts/results/metric_iter24_ep1_door_contact_geom/visualizations/episode1_path_quality_focus.gif filter=lfs diff=lfs merge=lfs -text
+artifacts/results/metric_iter24_ep1_door_contact_geom/visualizations/episode1_visibility_focus.gif filter=lfs diff=lfs merge=lfs -text

README.md

@@ -14,7 +14,7 @@ This is still a label-validation repository, not a policy repository. No `pi0.5`
 
 ## Current Status
 
-The latest work in this upload is the `metric_iter22_*_pregrasp_repair` rerun for episodes 0 and 1.
+The latest work in this upload is the `metric_iter24_*_door_contact_geom` rerun for episodes 0 and 1.
 
 That rerun fixes the main simulator-state bugs that were still contaminating the oven metrics:
 
@@ -40,17 +40,18 @@ 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_*`, `iter6_*`, and `iter19_*` outputs are still useful historical checkpoints, but the current authoritative outputs are:
+The old `iter4_*`, `iter6_*`, `iter19_*`, and `iter22_*` 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/`
+- `artifacts/results/metric_iter24_ep0_door_contact_geom/`
+- `artifacts/results/metric_iter24_ep1_door_contact_geom/`
 
-The main improvement from the `iter22` rerun is that the reveal-to-retrieve signal now flips well before grasp:
+The main new fix in `iter24` is the assisted-door contact scoring inside `p_pre`:
 
-- 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 old `ignore_collisions=True` branch treated oven-door contact as name-whitelisted and only checked the final door angle change
+- the new scorer traces door contacts step-by-step, estimates the local door-surface normal from simulator geometry, scores whether the retriever is sliding along the door or pushing it open, and penalizes direct head-on contact or door-closing motion
+- this specifically removes the false closed-door `p_pre` spike in episode 0 around frames `43-56` without collapsing the later pregrasp rise once the door is actually opening
 
-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.
+The current repo state should therefore be treated as the repaired benchmark snapshot with geometry-aware door assistance, not the final metric design.
 
 ## What Is In This Upload
 
@@ -107,6 +108,12 @@ This fixes the old failure where visibility dropped to zero even when the tray l
   - `177, 187, 197, 208, 218, 229, 232`
 - `p_pre` now uses the last few approach templates plus explicit geometric progress toward the pregrasp pose instead of only brittle planner success.
 - `y_pre` now treats "already inside the pregrasp corridor" as success, which is appropriate for this oracle study.
+- The assisted pregrasp branch no longer treats oven-door collisions as a binary whitelist:
+  - it traces per-step door contacts under `ignore_collisions=True`
+  - estimates a local door-surface normal from the contacted simulator shape
+  - rewards tangential or door-opening contact
+  - penalizes head-on or door-closing contact
+  - requires a minimum geometry-aware door-contact quality before assisted `p_pre` credit is given
 
 ### 3. Replay/repair correctness
 
@@ -157,20 +164,20 @@ 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/metric_iter24_ep0_door_contact_geom/episode0.dense.csv`
+- `artifacts/results/metric_iter24_ep0_door_contact_geom/episode0.keyframes.csv`
+- `artifacts/results/metric_iter24_ep0_door_contact_geom/episode0.metrics.json`
+- `artifacts/results/metric_iter24_ep0_door_contact_geom/summary.json`
+- `artifacts/results/metric_iter24_ep0_door_contact_geom/visualizations/episode0_all_metrics.gif`
+- `artifacts/results/metric_iter24_ep0_door_contact_geom/visualizations/episode0_visibility_focus.gif`
+- `artifacts/results/metric_iter24_ep0_door_contact_geom/visualizations/episode0_path_quality_focus.gif`
+- `artifacts/results/metric_iter24_ep1_door_contact_geom/episode1.dense.csv`
+- `artifacts/results/metric_iter24_ep1_door_contact_geom/episode1.keyframes.csv`
+- `artifacts/results/metric_iter24_ep1_door_contact_geom/episode1.metrics.json`
+- `artifacts/results/metric_iter24_ep1_door_contact_geom/summary.json`
+- `artifacts/results/metric_iter24_ep1_door_contact_geom/visualizations/episode1_all_metrics.gif`
+- `artifacts/results/metric_iter24_ep1_door_contact_geom/visualizations/episode1_visibility_focus.gif`
+- `artifacts/results/metric_iter24_ep1_door_contact_geom/visualizations/episode1_path_quality_focus.gif`
 
 - `artifacts/results/oven_episode0_iter4_templates/templates.json`
 - `artifacts/results/oven_episode0_iter4_templates/templates.pkl`

artifacts/results/metric_iter24_ep0_door_contact_geom/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_iter24_ep0_door_contact_geom/episode0.metrics.json

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artifacts/results/metric_iter24_ep0_door_contact_geom/summary.json

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artifacts/results/metric_iter24_ep0_door_contact_geom/templates.json

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artifacts/results/metric_iter24_ep0_door_contact_geom/templates.pkl

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artifacts/results/metric_iter24_ep0_door_contact_geom/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_iter24_ep1_door_contact_geom/episode1.dense.csv

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artifacts/results/metric_iter24_ep1_door_contact_geom/episode1.metrics.json

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+}

artifacts/results/metric_iter24_ep1_door_contact_geom/summary.json

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+}

artifacts/results/metric_iter24_ep1_door_contact_geom/templates.json

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+}

artifacts/results/metric_iter24_ep1_door_contact_geom/templates.pkl

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

artifacts/results/metric_iter24_ep1_door_contact_geom/visualizations/README.md

@@ -0,0 +1,13 @@
+# Visualizations
+
+- `episode1_visibility_focus.gif`: three-view visibility montage over dense frames 142-182.
+- `episode1_path_quality_focus.gif`: path-quality montage over dense frames 146-234.
+- `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

@@ -65,6 +65,9 @@ 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_DOOR_ASSIST_STEP_OPEN_SCALE = 0.02
+DEFAULT_DOOR_ASSIST_DIRECT_ALIGN_FREE = 0.55
+DEFAULT_DOOR_ASSIST_MIN_QUALITY = 0.3
 DEFAULT_READY_PERSISTENCE = 3
 DEFAULT_RETRIEVE_PERSISTENCE = 3
 DEFAULT_PREGRASP_PERSISTENCE = 3
@@ -1269,22 +1272,164 @@ def _allowed_door_collision_names(scene) -> set:
     return allowed
 
 
-def _execute_path_with_collision_trace(task, path, arm_name: str) -> List[str]:
+def _door_collision_shapes(scene) -> List[Shape]:
+    return [
+        Shape(shape.get_handle())
+        for shape in scene.pyrep.get_objects_in_tree(object_type=ObjectType.SHAPE)
+        if shape.get_name().startswith("oven_door") or shape.get_name().startswith("oven_knob")
+    ]
+
+
+def _robot_collision_shape_handles(scene, arm_name: str) -> set:
+    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 = arm.get_objects_in_tree(object_type=ObjectType.SHAPE)
+    robot_shapes.extend(gripper.get_objects_in_tree(object_type=ObjectType.SHAPE))
+    return {shape.get_handle() for shape in robot_shapes}
+
+
+def _shape_robot_contacts(shape: Shape, robot_shape_handles: set) -> List[np.ndarray]:
+    contacts: List[np.ndarray] = []
+    try:
+        raw_contacts = shape.get_contact(None, True)
+    except Exception:
+        return contacts
+    shape_handle = shape.get_handle()
+    for contact in raw_contacts:
+        handles = set(contact.get("contact_handles", []))
+        if shape_handle not in handles:
+            continue
+        other_handles = handles - {shape_handle}
+        if not other_handles.intersection(robot_shape_handles):
+            continue
+        contact_array = np.asarray(contact.get("contact", []), dtype=np.float64)
+        if contact_array.shape != (9,) or not np.all(np.isfinite(contact_array)):
+            continue
+        contacts.append(contact_array)
+    return contacts
+
+
+def _door_surface_normal(shape: Shape, contact_point: np.ndarray) -> Optional[np.ndarray]:
+    bbox = np.asarray(shape.get_bounding_box(), dtype=np.float64)
+    extents = np.array(
+        [bbox[1] - bbox[0], bbox[3] - bbox[2], bbox[5] - bbox[4]], dtype=np.float64
+    )
+    if not np.all(np.isfinite(extents)):
+        return None
+    thin_axis = int(np.argmin(extents))
+    axes = np.asarray(shape.get_matrix()[:3, :3], dtype=np.float64)
+    axis = axes[:, thin_axis]
+    axis_norm = float(np.linalg.norm(axis))
+    if axis_norm <= 1e-9:
+        return None
+    axis = axis / axis_norm
+    relative = np.asarray(contact_point, dtype=np.float64) - shape.get_position()
+    sign = 1.0 if float(relative @ axis) >= 0.0 else -1.0
+    return sign * axis
+
+
+def _door_contact_alignments(
+    shape: Shape, contacts: Sequence[np.ndarray], tip_delta: np.ndarray
+) -> List[float]:
+    tip_delta = np.asarray(tip_delta, dtype=np.float64)
+    tip_speed = float(np.linalg.norm(tip_delta))
+    if tip_speed <= 1e-9:
+        return []
+    move = tip_delta / tip_speed
+    alignments: List[float] = []
+    for contact in contacts:
+        normal = _door_surface_normal(shape, contact[:3])
+        if normal is None:
+            continue
+        alignments.append(float(np.clip(move @ normal, -1.0, 1.0)))
+    return alignments
+
+
+def _door_contact_step_quality(alignments: Sequence[float], step_open_delta: float) -> float:
+    if not alignments:
+        return 0.0
+    values = np.asarray(alignments, dtype=np.float64)
+    parallel_bonus = float(np.mean(1.0 - np.abs(values)))
+    opening_motion_bonus = float(np.mean(np.clip(-values, 0.0, 1.0)))
+    direct_penalty = float(
+        np.mean(
+            np.clip(
+                (np.abs(values) - DEFAULT_DOOR_ASSIST_DIRECT_ALIGN_FREE)
+                / max(1.0 - DEFAULT_DOOR_ASSIST_DIRECT_ALIGN_FREE, 1e-6),
+                0.0,
+                1.0,
+            )
+        )
+    )
+    open_bonus = float(
+        np.clip(step_open_delta / max(DEFAULT_DOOR_ASSIST_STEP_OPEN_SCALE, 1e-6), 0.0, 1.0)
+    )
+    close_penalty = float(
+        np.clip(-step_open_delta / max(DEFAULT_DOOR_ASSIST_STEP_OPEN_SCALE, 1e-6), 0.0, 1.0)
+    )
+    return float(
+        0.6 * parallel_bonus
+        + 0.4 * opening_motion_bonus
+        + 0.4 * open_bonus
+        - 0.8 * direct_penalty
+        - 1.0 * close_penalty
+    )
+
+
+def _door_contact_path_quality(step_qualities: Sequence[float], door_open_delta: float) -> float:
+    if not step_qualities:
+        return 0.0
+    mean_quality = float(np.mean(np.asarray(step_qualities, dtype=np.float64)))
+    open_bonus = float(
+        np.clip(
+            door_open_delta / (3.0 * max(DEFAULT_DOOR_ASSIST_STEP_OPEN_SCALE, 1e-6)),
+            0.0,
+            1.0,
+        )
+    )
+    return float(np.clip(0.7 * mean_quality + 0.3 * open_bonus, 0.0, 1.0))
+
+
+def _execute_path_with_collision_trace(task, path, arm_name: str) -> Tuple[List[str], List[float]]:
     collisions = set()
+    step_qualities: List[float] = []
+    door_shapes = _door_collision_shapes(task._scene)
+    robot_shape_handles = _robot_collision_shape_handles(task._scene, arm_name)
+    arm = task._scene.robot.left_arm if arm_name == "left" else task._scene.robot.right_arm
+    tip = arm.get_tip()
     path.set_to_start(disable_dynamics=True)
     task._pyrep.step()
+    previous_tip = tip.get_position()
+    previous_door_angle = float(Joint("oven_door_joint").get_joint_position())
     steps = 0
     while True:
         done = path.step()
         task._pyrep.step()
-        collisions.update(_arm_external_collision_names(task._scene, arm_name))
+        current_tip = tip.get_position()
+        tip_delta = np.asarray(current_tip - previous_tip, dtype=np.float64)
+        previous_tip = current_tip
+        current_door_angle = float(Joint("oven_door_joint").get_joint_position())
+        step_open_delta = previous_door_angle - current_door_angle
+        previous_door_angle = current_door_angle
+        current_collisions = set(_arm_external_collision_names(task._scene, arm_name))
+        for shape in door_shapes:
+            contacts = _shape_robot_contacts(shape, robot_shape_handles)
+            if not contacts:
+                continue
+            current_collisions.add(shape.get_name())
+            alignments = _door_contact_alignments(shape, contacts, tip_delta)
+            if alignments:
+                step_qualities.append(
+                    _door_contact_step_quality(alignments, step_open_delta)
+                )
+        collisions.update(current_collisions)
         steps += 1
         if done or steps > 400:
             break
-    return sorted(collisions)
+    return sorted(collisions), step_qualities
 
 
-def _door_assisted_pregrasp_score(
+def _single_door_assisted_pregrasp_score(
     task,
     target_pose: np.ndarray,
     base_snapshot: Optional[SimulatorSnapshot] = None,
@@ -1305,7 +1450,9 @@ def _door_assisted_pregrasp_score(
         if path is None or not np.isfinite(length):
             return 0.0, False
 
-        collision_names = _execute_path_with_collision_trace(task, path, "left")
+        collision_names, step_qualities = _execute_path_with_collision_trace(
+            task, path, "left"
+        )
         allowed = (
             _allowed_door_collision_names(task._scene)
             if allowed_collisions is None
@@ -1313,12 +1460,18 @@ def _door_assisted_pregrasp_score(
         )
         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:
+        door_quality = _door_contact_path_quality(step_qualities, door_open_delta)
+        if (
+            bad_collisions
+            or door_open_delta <= DEFAULT_DOOR_ASSIST_MIN_OPEN_DELTA
+            or door_quality < DEFAULT_DOOR_ASSIST_MIN_QUALITY
+        ):
             return 0.0, False
 
         base_score = float(
             math.exp(-float(length) / DEFAULT_PREGRASP_ASSIST_PATH_SCALE)
         )
+        base_score *= door_quality
         strict_path = _plan_path(
             task._scene,
             "left",
@@ -1334,6 +1487,39 @@ def _door_assisted_pregrasp_score(
         _restore_snapshot(task, snapshot)
 
 
+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]:
+    attempts = max(1, DEFAULT_PLAN_ATTEMPTS)
+    successful_scores: List[float] = []
+    assisted_success = False
+    for _ in range(attempts):
+        score, success = _single_door_assisted_pregrasp_score(
+            task,
+            target_pose,
+            base_snapshot=base_snapshot,
+            before_angle=before_angle,
+            allowed_collisions=allowed_collisions,
+        )
+        if score > 0.0:
+            successful_scores.append(float(score))
+        assisted_success = assisted_success or success
+
+    if not successful_scores:
+        return 0.0, assisted_success
+
+    reliability = float(len(successful_scores) / attempts)
+    if not _plan_is_reliable(reliability):
+        return 0.0, assisted_success
+
+    stable_score = float(np.median(np.asarray(successful_scores, dtype=np.float64)))
+    return stable_score * reliability, assisted_success
+
+
 def _pregrasp_score_and_success(task, templates: MotionTemplates) -> Tuple[float, bool]:
     tray = Shape("tray")
     if any(