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README.md

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+---
+license: apache-2.0
+task_categories:
+- robotics
+tags:
+- LeRobot
+- yam
+- bimanual
+- rubiks-cube
+- curated
+configs:
+- config_name: default
+  data_files: data/*/*.parquet
+---
+
+# Magic Cube
+
+YAM bimanual demonstrations: pick up a Rubik's cube with the left arm, present it to the right arm, and turn an outer layer with the right gripper.
+
+This is the stationary-pause-filtered, 2x temporal-subsampling edition of [zekaili/yam-cube-layer-turn](https://huggingface.co/datasets/zekaili/yam-cube-layer-turn), based on source revision `e0afe40d7f2402424c9920e5a49e381de8816dfb`.
+
+| Property | Value |
+| --- | --- |
+| Format | LeRobot v3.0 |
+| Episodes | 46 |
+| Frames | 33,166 |
+| FPS | 15 |
+| Total duration | 2,211.067 seconds (36 min 51 sec) |
+| Views | top, left_wrist, right_wrist |
+| State/action | 12 arm joints plus left/right grippers, original recorded values |
+| Video | H.264, lossless relative to decoded retained source frames |
+
+## Processing
+
+One original episode remains one output episode. Long stationary pause interiors are removed synchronously from all videos and numeric rows, then retained intervals are concatenated in source order. Every other frame of that filtered episode is selected, starting at frame zero, while output FPS remains 15. State/action values are selected, not multiplied or interpolated. Frame indices, global indices, timestamps, and statistics are rebuilt.
+
+The filter requires both arms and grippers to remain stationary for at least 3 seconds, with approximately 0.5 seconds of context retained at each edge. It checks measured state motion and action-target changes, including whole-pause drift limits. Detailed native-unit thresholds are in `meta/curation/plan.json`. Defaults were calibrated on source episode 0; the full dataset was processed automatically, not exhaustively visually reviewed.
+
+The original 165,746 numeric frames became 66,308 after pause removal and common-prefix handling, then 33,166 after 2x subsampling. There were 99,435 pause frames removed, 3 additional alignment-tail rows removed, and 33,142 frames skipped by subsampling. Integer rounding is applied independently to each episode; an extra endpoint frame is not appended.
+
+Temporal compression provides faster demonstrations at the dataset timebase. Actual robot execution speed depends on the controller timing and limits; this export has not been validated by a physical robot execution test.
+
+## Source alignment caveat
+
+Source episode 17/top, 20/right_wrist, and 40/right_wrist (zero-based indices) each contain one fewer video frame than numeric rows. Those episodes use the common prefix across views and numeric rows; the last numeric row and corresponding longer-view frames are omitted before filtering. The original missing capture position is unknown, so these three episodes inherit up to one source frame (about 67 ms) of alignment uncertainty. This is recorded in the per-episode plan and verification files.
+
+## Verification and provenance
+
+All 138 exported videos were checked frame by frame in decoded YUV and RGB against the selected source video frames. Retained state/action/task values and source-frame mappings were independently checked, and episode 0 exactly reproduces the earlier 2x pilot. The original dataset was preserved. These checks verify the transformation, not the original sensor alignment.
+
+- `meta/curation/plan.json`: parameters, pause selections, subsampling, joins, and alignment handling.
+- `meta/curation/source_frame_mapping.parquet`: every output frame's original episode/frame/global index and original timestamp, plus its position on the pause-filtered timeline.
+- `meta/curation/video_verification.json`: decoded pixel hashes and video verification.
+- `meta/curation/verification.json`: export counts and checks.
+- `meta/curation/source_manifest.json`: original dataset file hashes.
+- `meta/curation/code/filter_lerobot_pauses.py`: processing code snapshot; run within the Sana project with its referenced helpers.
+
+Numeric statistics are recomputed from retained rows. Image statistics use stride-4 pixel sampling of retained frames. Audit records retain original storage paths as provenance; LeRobot training paths in `meta/info.json` are relative and portable.
+
+Apache-2.0 is inherited from the source dataset card.

meta/curation/code/filter_lerobot_pauses.py

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+"""Remove prolonged stationary holds from YAM LeRobot v3 episodes into a new tree.
+
+Run from the Sana root with ``python -m dev.rwm.preprocess.filter_lerobot_pauses
+--source SOURCE --output NEW_DIRECTORY --episodes 0``. Use --all-episodes for
+a complete dataset, or --analyze-only to write a plan without exporting videos.
+Add --speedup 2 to sample every other frame AFTER pause removal, keeping the
+original FPS. Run this combined pipeline on the original recording dataset.
+Thresholds use recorded values, not normalized training values. Every original
+episode remains one episode; timestamps compress the removed pauses. The source
+frame mapping records all discontinuities. Original files are never rewritten.
+"""
+from __future__ import annotations
+
+import argparse
+import copy
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from multiprocessing import get_context
+from dataclasses import asdict, dataclass
+from fractions import Fraction
+import hashlib
+import json
+import math
+from pathlib import Path
+import shutil
+
+import av
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+from scipy.ndimage import maximum_filter1d, minimum_filter1d
+
+# These existing helpers are format/video utilities; no hockey selection logic
+# or camera whitelist is used by this filter.
+from .air_hockey.common import (
+    episode_data, load_source, separate_paths, sha256, source_manifest,
+    video_location, write_json,
+)
+from .air_hockey.statistics import image_stats, numeric_stats
+from .air_hockey.video import iter_frames
+
+SCHEMA = 'yam-stationary-pause-filter/v1'
+EXPECTED_NAMES = [f'{arm}_joint_{i}' for arm in ('left', 'right') for i in range(1, 7)] + ['left_gripper', 'right_gripper']
+REINDEXED = {'episode_index', 'frame_index', 'index', 'timestamp'}
+
+
+@dataclass(frozen=True)
+class Config:
+    min_pause_seconds: float = 3.0
+    edge_seconds: float = 0.5
+    window_seconds: float = 1.0
+    joint_window_range: float = 0.005
+    gripper_window_range: float = 0.002
+    joint_run_range: float = 0.01
+    gripper_run_range: float = 0.003
+    action_step_epsilon: float = 1e-6
+    action_run_epsilon: float = 1e-5
+
+    def validate(self):
+        if any(not math.isfinite(v) or v < 0 for v in asdict(self).values()):
+            raise ValueError('Thresholds must be finite and nonnegative')
+        if self.min_pause_seconds <= 0 or self.window_seconds <= 0:
+            raise ValueError('Pause duration and window must be positive')
+        if self.min_pause_seconds <= 2 * self.edge_seconds:
+            raise ValueError('Minimum pause must exceed the two retained edge durations')
+
+
+def runs(mask):
+    diff = np.diff(np.r_[False, mask, False].astype(np.int8))
+    return [(int(a), int(b)) for a, b in zip(np.flatnonzero(diff == 1), np.flatnonzero(diff == -1))]
+
+
+def detect_pauses(states, actions, fps, config=Config()):
+    """Return half-open frame intervals. Require BOTH arms AND grippers still.
+
+    Command changes and measured motion independently veto deletion. Full-run
+    range limits also protect slow drift that could pass a short-window test.
+    Short pauses are retained. No morphological closing bridges a movement.
+    """
+    config.validate()
+    if type(fps) is not int or fps <= 0:
+        raise ValueError('A positive integer FPS is required')
+    states, actions = np.asarray(states), np.asarray(actions)
+    if states.ndim != 2 or states.shape[1] != 14 or states.shape != actions.shape or not len(states):
+        raise ValueError('Expected matching nonempty [frames, 14] state/action arrays')
+    if not np.isfinite(states).all() or not np.isfinite(actions).all():
+        raise ValueError('Nonfinite state/action data')
+    window = max(1, round(config.window_seconds * fps))
+    if window % 2 == 0:
+        window += 1
+    spans = maximum_filter1d(states, size=window, axis=0, mode='nearest') - minimum_filter1d(states, size=window, axis=0, mode='nearest')
+    target_held = np.r_[True, np.max(np.abs(np.diff(actions, axis=0)), axis=1) <= config.action_step_epsilon]
+    still = ((spans[:, :12].max(axis=1) < config.joint_window_range)
+             & (spans[:, 12:].max(axis=1) < config.gripper_window_range) & target_held)
+    margin = math.ceil(config.edge_seconds * fps)
+    minimum = math.ceil(config.min_pause_seconds * fps)
+    removed = np.zeros(len(states), dtype=bool)
+    candidates = []
+    for start, end in runs(still):
+        if end - start < minimum:
+            continue
+        jrange = float(np.ptp(states[start:end, :12], axis=0).max())
+        grange = float(np.ptp(states[start:end, 12:], axis=0).max())
+        arange = float(np.ptp(actions[start:end], axis=0).max())
+        accepted = (jrange < config.joint_run_range and grange < config.gripper_run_range
+                    and arange <= config.action_run_epsilon and end-start > 2*margin)
+        row = {'pause_start_frame': start, 'pause_end_frame_exclusive': end,
+               'pause_start_seconds': start/fps, 'pause_end_seconds': end/fps,
+               'joint_range': jrange, 'gripper_range': grange, 'action_range': arange,
+               'decision': 'remove_interior' if accepted else 'retain_due_to_drift_or_short_interior'}
+        if accepted:
+            a, b = start+margin, end-margin
+            removed[a:b] = True
+            row.update(remove_start_frame=a, remove_end_frame_exclusive=b,
+                       remove_start_seconds=a/fps, remove_end_seconds=b/fps, removed_frames=b-a)
+        candidates.append(row)
+    keep = np.flatnonzero(~removed)
+    if not len(keep):
+        raise ValueError('Filtering would empty an episode')
+    return {'source_frames': len(states), 'retained_frames': len(keep), 'removed_frames': int(removed.sum()),
+            'effective_window_frames': window, 'edge_context_frames': margin, 'candidates': candidates,
+            'keep_intervals': [{'start_frame': a, 'end_frame_exclusive': b} for a, b in runs(~removed)]}
+
+
+def retained_indices(plan):
+    indices = np.concatenate([np.arange(r['start_frame'], r['end_frame_exclusive'], dtype=np.int64)
+                              for r in plan['keep_intervals']])
+    return indices[::plan.get('speedup_factor', 1)]
+
+
+def apply_speedup(plan, factor):
+    """Sample globally across the concatenated retained episode, never per interval.
+
+    No endpoint is appended: output rows are exactly pause_filtered[::factor].
+    This also means an even-length input loses its last frame at factor=2.
+    """
+    if type(factor) is not int or factor < 1:
+        raise ValueError('Speedup must be a positive integer')
+    if 'speedup_factor' in plan:
+        raise ValueError('Speedup has already been applied to this plan')
+    result = copy.deepcopy(plan)
+    count = plan['retained_frames']
+    result.update(speedup_factor=factor, pause_filtered_frames=count,
+                  pause_removed_frames=plan['removed_frames']-plan.get('alignment_trimmed_frames', 0),
+                  resampling_dropped_frames=count - math.ceil(count/factor),
+                  retained_frames=math.ceil(count/factor))
+    result['removed_frames'] = result['source_frames']-result['retained_frames']
+    return result
+
+
+def encode_kept_video(source, destination, first_frame, plan, fps):
+    """Concatenate selected frame intervals with lossless YUV/RGB verification."""
+    destination.parent.mkdir(parents=True, exist_ok=True)
+    histogram = np.zeros((3, 256), dtype=np.int64)
+    expected = [hashlib.sha256(), hashlib.sha256()]
+    with av.open(str(source)) as container:
+        context = container.streams.video[0].codec_context
+        width, height = context.width, context.height
+        colors = {k: getattr(context, k) for k in ('colorspace', 'color_range', 'color_primaries', 'color_trc')}
+    count, filtered_position = 0, 0
+    with av.open(str(destination), 'w') as output:
+        encoder = output.add_stream('libx264', rate=fps)
+        encoder.width, encoder.height, encoder.pix_fmt = width, height, 'yuv420p'
+        encoder.time_base = Fraction(1, fps)
+        encoder.codec_context.thread_count = 1
+        encoder.codec_context.gop_size = fps
+        encoder.codec_context.max_b_frames = 0
+        for key, value in colors.items():
+            setattr(encoder.codec_context, key, value)
+        encoder.options = {'crf': '0', 'preset': 'veryfast'}
+        for interval in plan['keep_intervals']:
+            start, end = first_frame + interval['start_frame'], first_frame + interval['end_frame_exclusive']
+            for frame in iter_frames(source, start, end, fps):
+                take = filtered_position % plan.get('speedup_factor', 1) == 0
+                filtered_position += 1
+                if not take:
+                    continue
+                if frame.format.name != 'yuv420p':
+                    raise ValueError('Lossless export requires yuv420p video')
+                rgb = frame.to_ndarray(format='rgb24')
+                expected[0].update(frame.to_ndarray(format='yuv420p').tobytes())
+                expected[1].update(rgb.tobytes())
+                for channel in range(3):
+                    histogram[channel] += np.bincount(rgb[::4, ::4, channel].ravel(), minlength=256)
+                frame.pts, frame.time_base = count, Fraction(1, fps)
+                frame.pict_type = av.video.frame.PictureType.NONE
+                for packet in encoder.encode(frame):
+                    output.mux(packet)
+                count += 1
+        for packet in encoder.encode():
+            output.mux(packet)
+    if count != plan['retained_frames']:
+        raise ValueError('Exported frame count mismatch')
+    actual = [hashlib.sha256(), hashlib.sha256()]
+    with av.open(str(destination)) as container:
+        stream = container.streams.video[0]
+        stream.thread_count = 1
+        decoded = 0
+        for frame in container.decode(stream):
+            if abs(float(frame.pts * stream.time_base) * fps - decoded) > 1e-3:
+                raise ValueError('Output video timestamp mismatch')
+            actual[0].update(frame.to_ndarray(format='yuv420p').tobytes())
+            actual[1].update(frame.to_ndarray(format='rgb24').tobytes())
+            decoded += 1
+    if decoded != count or any(a.digest() != b.digest() for a, b in zip(actual, expected)):
+        raise ValueError('Output video does not match the retained source pixels')
+    return {'frames': count, 'histogram': histogram, 'width': width, 'height': height,
+            'decoded_yuv_sha256': actual[0].hexdigest(), 'decoded_rgb_sha256': actual[1].hexdigest(),
+            'all_retained_pixels_identical': True, 'sha256': sha256(destination)}
+
+
+def aligned_video_locations(info, episode, views, trim_short_videos=False):
+    locations, lengths = {}, {}
+    for view in views:
+        first = episode[f'videos/{view}/from_timestamp'] * info['fps']
+        end = episode[f'videos/{view}/to_timestamp'] * info['fps']
+        if abs(first-round(first)) > 1e-3 or abs(end-round(end)) > 1e-3 or first < 0:
+            raise ValueError('Video boundaries are not frame aligned')
+        length = round(end-first)
+        difference = episode['length']-length
+        if difference != 0 and not (trim_short_videos and difference == 1):
+            raise ValueError(f"Episode {episode['episode_index']} {view}: video/data length mismatch {difference}")
+        lengths[view] = length
+        locations[view] = (info['video_path'].format(video_key=view,
+            chunk_index=episode[f'videos/{view}/chunk_index'],
+            file_index=episode[f'videos/{view}/file_index']), round(first))
+    common = min(lengths.values())
+    return locations, common, lengths
+
+
+def filter_dataset(source, output, episode_ids, config=Config(), analyze_only=False, speedup=1, trim_short_videos=False, workers=1):
+    source, output = Path(source).resolve(), Path(output).resolve()
+    stage = output.with_name(output.name + '.partial')
+    separate_paths(source, output)
+    separate_paths(source, stage)
+    if output.exists() or stage.exists():
+        raise ValueError('Output or .partial directory exists; choose a new destination')
+    config.validate()
+    if type(workers) is not int or workers < 1:
+        raise ValueError('Workers must be a positive integer')
+    if type(speedup) is not int or speedup < 1:
+        raise ValueError('Speedup must be a positive integer')
+    info, old_meta, episodes = load_source(source)
+    fps = info['fps']
+    for key in ('observation.state', 'action'):
+        if info['features'][key].get('names') != EXPECTED_NAMES:
+            raise ValueError(f'{key}: expected the YAM left/right joint and gripper ordering')
+    ids = sorted(episodes) if episode_ids is None else sorted(episode_ids)
+    if not ids or len(ids) != len(set(ids)) or any(eid not in episodes for eid in ids):
+        raise ValueError('Episode IDs must be nonempty, unique and present in the source')
+    views = [k for k, feature in info['features'].items() if feature['dtype'] == 'video']
+    if not views:
+        raise ValueError('No video features')
+    baseline = source_manifest(source)
+    plans, tables, locations = [], {}, {}
+    for eid in ids:
+        table = episode_data(source, info, episodes[eid])
+        if set(table.column_names) != {k for k in info['features'] if k not in views}:
+            raise ValueError('Unexpected source columns')
+        for key in table.column_names:
+            numeric_stats(table[key])
+        locations[eid], common_length, video_lengths = aligned_video_locations(info, episodes[eid], views, trim_short_videos)
+        aligned = table.slice(0, common_length)
+        plan = detect_pauses(np.array(aligned['observation.state'].to_pylist()), np.array(aligned['action'].to_pylist()), fps, config)
+        trimmed = len(table)-common_length
+        plan.update(source_frames=len(table), alignment_trimmed_frames=trimmed,
+                    video_episode_lengths=video_lengths, removed_frames=plan['removed_frames']+trimmed)
+        if trimmed:
+            plan['alignment_note'] = ('Use the common prefix defined by HF episode boundaries; trim the final numeric row and corresponding longer-view frames. '
+                                      'The original missing capture position is unknown; source alignment may have up to one frame uncertainty.')
+        plan = apply_speedup(plan, speedup)
+        plan['source_episode_index'] = eid
+        tables[eid] = table
+        plans.append(plan)
+    document = {'schema': SCHEMA, 'source': str(source), 'source_fingerprint': baseline['fingerprint'],
+                'fps': fps, 'config': asdict(config), 'speedup_factor': speedup, 'trim_short_videos': trim_short_videos, 'episodes': plans,
+                'semantics': 'One output episode per selected source episode; remove pause interiors, concatenate retained intervals, sample [::speedup_factor], and rebuild timestamps at the original FPS.'}
+    stage.mkdir(parents=True)
+    write_json(stage/'meta/curation/plan.json', document)
+    write_json(stage/'meta/curation/source_manifest.json', baseline)
+    if analyze_only:
+        if source_manifest(source) != baseline:
+            raise ValueError('Source changed during analysis')
+        stage.rename(output)
+        return document
+    encoded = {}
+    if workers > 1:
+        with ProcessPoolExecutor(max_workers=workers, mp_context=get_context('spawn')) as pool:
+            jobs = {}
+            for new_id, plan in enumerate(plans):
+                chunk, file_index = divmod(new_id, info['chunks_size'])
+                for view in views:
+                    relative, first = locations[plan['source_episode_index']][view]
+                    target = info['video_path'].format(video_key=view, chunk_index=chunk, file_index=file_index)
+                    future = pool.submit(encode_kept_video, source/relative, stage/target, first, plan, fps)
+                    jobs[future] = (new_id, view)
+            for future in as_completed(jobs):
+                encoded[jobs[future]] = future.result()
+                print(f'Videos encoded and pixel verified: {len(encoded)}/{len(jobs)}', flush=True)
+    data_parts, episode_rows, mappings, video_checks = [], [], [], []
+    histograms = {view: np.zeros((3, 256), dtype=np.int64) for view in views}
+    offset = 0
+    for new_id, plan in enumerate(plans):
+        eid = plan['source_episode_index']
+        original = tables[eid]
+        kept = retained_indices(plan)
+        selected = original.take(pa.array(kept))
+        part = selected
+        count = len(kept)
+        for key, values in [('episode_index', np.full(count, new_id)), ('frame_index', np.arange(count)),
+                            ('index', np.arange(offset, offset+count)), ('timestamp', np.arange(count)/fps)]:
+            field = part.schema.field(key)
+            part = part.set_column(part.schema.get_field_index(key), field, pa.array(values, type=field.type))
+        for key in set(part.column_names) - REINDEXED:
+            if not part[key].equals(selected[key]):
+                raise ValueError(f'Retained training values changed: {key}')
+        chunk, file_index = divmod(new_id, info['chunks_size'])
+        data_path = stage/info['data_path'].format(chunk_index=chunk, file_index=file_index)
+        data_path.parent.mkdir(parents=True, exist_ok=True)
+        pq.write_table(part, data_path, compression='snappy')
+        if not pq.read_table(data_path).equals(part):
+            raise ValueError('Data parquet round-trip failed')
+        data_parts.append(part)
+        row = {k: copy.deepcopy(v) for k, v in episodes[eid].items() if not k.startswith(('stats/', 'videos/'))}
+        row.update(episode_index=new_id, length=count, dataset_from_index=offset, dataset_to_index=offset+count)
+        row.update({'data/chunk_index': chunk, 'data/file_index': file_index,
+                    'meta/episodes/chunk_index': 0, 'meta/episodes/file_index': 0})
+        for key in part.column_names:
+            row.update({f'stats/{key}/{stat}': value for stat, value in numeric_stats(part[key]).items()})
+        for view in views:
+            source_relative, first = locations[eid][view]
+            relative = info['video_path'].format(video_key=view, chunk_index=chunk, file_index=file_index)
+            result = encoded.pop((new_id, view)) if workers > 1 else encode_kept_video(source/source_relative, stage/relative, first, plan, fps)
+            histograms[view] += result['histogram']
+            for stat, value in image_stats(result['histogram'], count).items():
+                row[f'stats/{view}/{stat}'] = value
+            for key, value in [('chunk_index', chunk), ('file_index', file_index), ('from_timestamp', 0.0), ('to_timestamp', count/fps)]:
+                row[f'videos/{view}/{key}'] = value
+            video_checks.append({k: v for k, v in result.items() if k != 'histogram'} | {
+                'output_episode_index': new_id, 'view': view, 'path': relative,
+                'source_path': source_relative, 'source_video_first_frame': first})
+            print(f'Pixel verified episode {eid} -> {new_id}, {view}, {count} frames', flush=True)
+        episode_rows.append(row)
+        mappings.append(pa.table({'episode_index': np.full(count, new_id, dtype=np.int64),
+                                  'frame_index': np.arange(count, dtype=np.int64),
+                                  'pause_filtered_frame_index': np.arange(count, dtype=np.int64)*speedup,
+                                  'source_episode_index': np.full(count, eid, dtype=np.int64),
+                                  'source_frame_index': kept,
+                                  'source_global_index': original['index'].take(pa.array(kept)),
+                                  'source_timestamp': original['timestamp'].take(pa.array(kept))}))
+        plan['output_episode_index'] = new_id
+        joins = np.flatnonzero(np.diff(kept) != speedup) + 1
+        state = np.array(selected['observation.state'].to_pylist())
+        action = np.array(selected['action'].to_pylist())
+        plan['joins'] = [{'output_frame': int(i), 'source_frame_before': int(kept[i-1]), 'source_frame_after': int(kept[i]),
+                          'joint_step': float(np.max(np.abs(state[i,:12]-state[i-1,:12]))),
+                          'gripper_step': float(np.max(np.abs(state[i,12:]-state[i-1,12:]))),
+                          'action_step': float(np.max(np.abs(action[i]-action[i-1])))} for i in joins]
+        offset += count
+    combined = pa.concat_tables(data_parts)
+    names = set(episode_rows[0])
+    schema = pa.schema([f for f in old_meta.schema if f.name in names])
+    inferred = pa.Table.from_pylist(episode_rows).schema
+    schema = pa.schema(list(schema) + [f for f in inferred if f.name not in schema.names])
+    metadata = pa.Table.from_pylist(episode_rows, schema=schema)
+    metadata_path = stage/'meta/episodes/chunk-000/file-000.parquet'
+    metadata_path.parent.mkdir(parents=True, exist_ok=True)
+    pq.write_table(metadata, metadata_path)
+    if not pq.read_table(metadata_path).equals(metadata):
+        raise ValueError('Episode metadata round-trip failed')
+    for name in ('meta/tasks.parquet', '.gitattributes'):
+        if (source/name).is_file():
+            shutil.copy2(source/name, stage/name)
+    output_info = copy.deepcopy(info)
+    output_info.update(total_episodes=len(plans), total_frames=offset, splits={'train': f'0:{len(plans)}'})
+    if 'total_videos' in output_info:
+        output_info['total_videos'] = len(plans)*len(views)
+    if 'total_chunks' in output_info:
+        output_info['total_chunks'] = math.ceil(len(plans)/info['chunks_size'])
+    for view in views:
+        output_info['features'][view]['info'].update({'video.codec': 'h264', 'video.pix_fmt': 'yuv420p',
+            'video.crf': 0, 'video.preset': 'veryfast', 'video.g': fps, 'video.extra_options': {'bf': '0'}, 'video.fast_decode': 0})
+    stats = {key: numeric_stats(combined[key]) for key in combined.column_names}
+    stats.update({key: image_stats(histogram, offset) for key, histogram in histograms.items()})
+    write_json(stage/'meta/info.json', output_info)
+    write_json(stage/'meta/stats.json', stats)
+    write_json(stage/'meta/curation/plan.json', document)
+    pq.write_table(pa.concat_tables(mappings), stage/'meta/curation/source_frame_mapping.parquet')
+    write_json(stage/'meta/curation/video_verification.json', video_checks)
+    from dev.rwm.diffusion.data.lerobot_source import read_lerobot_episodes
+    loaded = read_lerobot_episodes(stage)
+    if len(loaded) != len(plans) or sum(e['length'] for e in loaded) != offset:
+        raise ValueError('Project LeRobot reader rejected output counts')
+    if source_manifest(source) != baseline:
+        raise ValueError('Source changed during export')
+    summary = {'status': 'complete', 'source': str(source), 'output': str(output), 'episodes': len(plans), 'fps': fps,
+               'source_selected_frames': sum(p['source_frames'] for p in plans), 'retained_frames': offset,
+               'removed_frames': sum(p['removed_frames'] for p in plans), 'views': views,
+               'speedup_factor': speedup,
+               'pause_filtered_frames': sum(p['pause_filtered_frames'] for p in plans),
+               'alignment_trimmed_frames': sum(p['alignment_trimmed_frames'] for p in plans),
+               'source_alignment_uncertain_episodes': [p['source_episode_index'] for p in plans if p['alignment_trimmed_frames']],
+               'pause_removed_frames': sum(p['pause_removed_frames'] for p in plans),
+               'resampling_dropped_frames': sum(p['resampling_dropped_frames'] for p in plans),
+               'all_retained_training_values_exact': True, 'all_retained_video_pixels_exact': True,
+               'source_unchanged': True, 'project_lerobot_reader_passed': True}
+    write_json(stage/'meta/curation/verification.json', summary)
+    (stage/'README.md').write_text('---\nlicense: apache-2.0\ntask_categories:\n- robotics\nconfigs:\n- config_name: default\n  data_files: data/*/*.parquet\n---\n\n'
+        '# YAM dataset with stationary pauses removed and optional temporal subsampling\n\n'
+        f'{len(plans)} episodes, {offset} frames, {fps} FPS. One output episode per selected source episode.\n\n'
+        'Long stationary pause interiors are removed jointly from all camera streams and numeric rows; '
+        'the retained intervals are concatenated in source order. '
+        f'Then every {speedup}th frame is retained globally within each episode, starting at frame 0. '
+        'No extra endpoint frame is appended. The original FPS is retained; timestamps compress time across '
+        'pauses and subsampling. This prepares faster demonstrations; robot execution speed also depends on controller timing and limits. '
+        'State, action and task values are preserved exactly. Video is lossless H.264 relative to decoded source pixels. '
+        'Numeric and image statistics are recomputed from retained frames (image statistics use stride-4 pixel samples).\n\n'
+        'See meta/curation/plan.json for thresholds, removed ranges and join diagnostics, '
+        'source_frame_mapping.parquet for every retained source frame, and verification.json/video_verification.json for checks. '
+        'Thresholds were calibrated on yam-cube-layer-turn episode 0; other episodes need review. '
+        'When trim_short_videos is enabled, one-frame-short views use the common episode prefix; '
+        'the original missing-frame location is unknown, so those episodes inherit up to one source frame of alignment uncertainty. See plan.json.\n')
+    code = stage/'meta/curation/code'
+    code.mkdir()
+    shutil.copy2(__file__, code/'filter_lerobot_pauses.py')
+    stage.rename(output)
+    return summary
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument('--source', type=Path, required=True)
+    parser.add_argument('--output', type=Path, required=True)
+    select = parser.add_mutually_exclusive_group(required=True)
+    select.add_argument('--episodes', nargs='+', type=int)
+    select.add_argument('--all-episodes', action='store_true')
+    parser.add_argument('--analyze-only', action='store_true')
+    parser.add_argument('--trim-short-videos', action='store_true', help='Allow only one-frame video deficits; retain common prefix and record original alignment uncertainty')
+    parser.add_argument('--workers', type=int, default=1)
+    parser.add_argument('--speedup', type=int, default=1,
+                        help='Positive integer sampling stride AFTER pause removal; original output FPS is retained')
+    for name, default in asdict(Config()).items():
+        parser.add_argument('--'+name.replace('_', '-'), type=float, default=default)
+    args = parser.parse_args()
+    config = Config(**{key: getattr(args, key) for key in asdict(Config())})
+    result = filter_dataset(args.source, args.output, args.episodes, config, args.analyze_only, args.speedup, args.trim_short_videos, args.workers)
+    print(json.dumps(result, indent=2, ensure_ascii=False), flush=True)
+
+
+if __name__ == '__main__':
+    main()

meta/curation/source_frame_mapping.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:124e7a2e02e9df6db0dd55feb31f0ed3ef763efb484723bab550a9984448b304
+size 318645

meta/curation/source_manifest.json

@@ -0,0 +1,165 @@
+{
+  "fingerprint": "eddf0d3508384e4563b96965ea2a1e8ce4ef2e8bd153e396bac07e470698f5be",
+  "files": [
+    {
+      "path": "README.md",
+      "size": 5963,
+      "sha256": "0cf5128be31cfb7ae6af9c4348e7d97c6fb288b5ee8fe269b6746fe6c300f8b3"
+    },
+    {
+      "path": "data/chunk-000/file-000.parquet",
+      "size": 6906195,
+      "sha256": "835704c554831745cb0314b37a94faaa685c95c712d10d0a5d75fc162b5e9ca2"
+    },
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+      "path": "meta/episodes/chunk-000/file-000.parquet",
+      "size": 306671,
+      "sha256": "3ee8ed6574bebd62f5171859359ed3cb5040a56f819b3566d23d6e678c8b2326"
+    },
+    {
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+      "size": 5041,
+      "sha256": "ba7a681484dcd8cadc489e0f13cbc633dea6a0effdada90081cf5bcb627334fa"
+    },
+    {
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+      "size": 21010,
+      "sha256": "af4bf8eb1d9c7945f00126b7b83997332ca0035a824f8ee24c25c11acdd4d34b"
+    },
+    {
+      "path": "meta/tasks.parquet",
+      "size": 2680,
+      "sha256": "88bef294cf4feb9e9d0aed6c5031c24cbdaa316d8afacd239d4efa232ea67fdd"
+    },
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+      "size": 178862166,
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+    },
+    {
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+      "size": 192774159,
+      "sha256": "63bb17d3e1e26163799eb1f36cccbd65f7380b0062ef7f0593685dd389313093"
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+      "sha256": "cec250f32a78efbd85be1a06df601e69fa49399936a25259370a1031c35ef8a1"
+    }
+  ]
+}

meta/curation/verification.json

@@ -0,0 +1,29 @@
+{
+  "status": "complete",
+  "source": "/lustre/fs1/portfolios/nvr/projects/nvr_elm_llm/users/lezhao/zekail/lerobot/zekaili/yam-cube-layer-turn",
+  "output": "/lustre/fs1/portfolios/nvr/projects/nvr_elm_llm/users/lezhao/zekail/lerobot/zekaili/yam-cube-layer-turn-pause-filter-2x",
+  "episodes": 46,
+  "fps": 15,
+  "source_selected_frames": 165746,
+  "retained_frames": 33166,
+  "removed_frames": 132580,
+  "views": [
+    "observation.images.top",
+    "observation.images.left_wrist",
+    "observation.images.right_wrist"
+  ],
+  "speedup_factor": 2,
+  "pause_filtered_frames": 66308,
+  "alignment_trimmed_frames": 3,
+  "source_alignment_uncertain_episodes": [
+    17,
+    20,
+    40
+  ],
+  "pause_removed_frames": 99435,
+  "resampling_dropped_frames": 33142,
+  "all_retained_training_values_exact": true,
+  "all_retained_video_pixels_exact": true,
+  "source_unchanged": true,
+  "project_lerobot_reader_passed": true
+}

meta/curation/video_verification.json

@@ -0,0 +1,1934 @@
+[
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+    "height": 480,
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+    "source_path": "videos/observation.images.top/chunk-000/file-000.mp4",
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+    "source_video_first_frame": 0
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+    "source_path": "videos/observation.images.top/chunk-000/file-000.mp4",
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