The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
codebase_version: string
fps: int64
features: struct<action: struct<dtype: string, shape: list<item: int64>, names: list<item: string>>, episode_i (... 2205 chars omitted)
child 0, action: struct<dtype: string, shape: list<item: int64>, names: list<item: string>>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: list<item: string>
child 0, item: string
child 1, episode_index: struct<dtype: string, shape: list<item: int64>, names: null>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 2, frame_index: struct<dtype: string, shape: list<item: int64>, names: null>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 3, index: struct<dtype: string, shape: list<item: int64>, names: null>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 4, observation.images.left_wrist: struct<dtype: string, shape: list<item: int64>, names: list<item: string>, info: struct<video.height (... 293 chars omitted)
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: list<item: string>
child 0, item: string
child 3, info: struct<video.height: int64, video.width: int64, video.codec: string,
...
eed: struct<dtype: string, shape: list<item: int64>, names: null>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 12, retime.source_slot: struct<dtype: string, shape: list<item: int64>, names: null>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 13, retime.u: struct<dtype: string, shape: list<item: int64>, names: null>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 14, task_index: struct<dtype: string, shape: list<item: int64>, names: null>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 15, timestamp: struct<dtype: string, shape: list<item: int64>, names: null>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
total_episodes: int64
total_frames: int64
total_tasks: int64
chunks_size: int64
data_files_size_in_mb: int64
video_files_size_in_mb: int64
data_path: string
video_path: string
robot_type: string
splits: struct<train: string>
child 0, train: string
image_quantiles: string
sample_weight: string
stats_sha256: string
frames: int64
algorithm: string
quantiles: list<item: double>
child 0, item: double
mask_exclusion: bool
method: string
padding: string
to
{'algorithm': Value('string'), 'method': Value('string'), 'quantiles': List(Value('float64')), 'frames': Value('int64'), 'sample_weight': Value('string'), 'padding': Value('string'), 'mask_exclusion': Value('bool'), 'image_quantiles': Value('string'), 'stats_sha256': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
codebase_version: string
fps: int64
features: struct<action: struct<dtype: string, shape: list<item: int64>, names: list<item: string>>, episode_i (... 2205 chars omitted)
child 0, action: struct<dtype: string, shape: list<item: int64>, names: list<item: string>>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: list<item: string>
child 0, item: string
child 1, episode_index: struct<dtype: string, shape: list<item: int64>, names: null>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 2, frame_index: struct<dtype: string, shape: list<item: int64>, names: null>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 3, index: struct<dtype: string, shape: list<item: int64>, names: null>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 4, observation.images.left_wrist: struct<dtype: string, shape: list<item: int64>, names: list<item: string>, info: struct<video.height (... 293 chars omitted)
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: list<item: string>
child 0, item: string
child 3, info: struct<video.height: int64, video.width: int64, video.codec: string,
...
eed: struct<dtype: string, shape: list<item: int64>, names: null>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 12, retime.source_slot: struct<dtype: string, shape: list<item: int64>, names: null>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 13, retime.u: struct<dtype: string, shape: list<item: int64>, names: null>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 14, task_index: struct<dtype: string, shape: list<item: int64>, names: null>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 15, timestamp: struct<dtype: string, shape: list<item: int64>, names: null>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
total_episodes: int64
total_frames: int64
total_tasks: int64
chunks_size: int64
data_files_size_in_mb: int64
video_files_size_in_mb: int64
data_path: string
video_path: string
robot_type: string
splits: struct<train: string>
child 0, train: string
image_quantiles: string
sample_weight: string
stats_sha256: string
frames: int64
algorithm: string
quantiles: list<item: double>
child 0, item: double
mask_exclusion: bool
method: string
padding: string
to
{'algorithm': Value('string'), 'method': Value('string'), 'quantiles': List(Value('float64')), 'frames': Value('int64'), 'sample_weight': Value('string'), 'padding': Value('string'), 'mask_exclusion': Value('bool'), 'image_quantiles': Value('string'), 'stats_sha256': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
ctr-scan-object-100ep-concurrent
100 episodes, 22787 frames, 100 unique scene seeds, LeRobot v3,25FPS.
Episode order / 数据集备注
Original cohort first, then E742 cohort; the two seed sets are disjoint.
Seeds and timing
The complete seed list and per-seed episode/timing mapping are in seed_timing_map.json,
episode_seed_timing.csv and merge_episode_map.json.
retime.source_seed and retime.source_cohort are also stored on every numeric row.
Positive offset_s delays the right arm; negative values delay the left arm.
Nominal offsets are rounded to the nearest250Hz physical step. Workspace waits are separately recorded.
Cameras, labels and provenance
All sources use New FOV centered_fovy90:320×240, vertical90degrees, calibrated centered optical-axis mount;
head plus both wrist cameras,250Hz physics,25FPS recordings. Original raster rendering is retained.
This is a physical-video merge, with no resimulation, cross-time image composition or retiming of controls.
Source videos are copied without re-encoding, and video files remain separate across source boundaries.
The first cohort is the20260911 paired750 collection; the second is E742 paired750. Requested gripper targets are checked against frozen controls. Any stale first-cohort pre-command zero labels are corrected only to the verified initial-open command; all other action values and all observation values are preserved. First-cohort observations retain their original capture/accessor provenance; unavailable historical raw physical qpos is not reconstructed. E742 records measured qpos and normalized measured grippers. Per-source corrections and hashes are in meta/source_provenance.json. Uniform alone includes observation.arm_active_mask:0 only for the initial artificial start delay,1 for all later responsibilities including finished holds, scan barriers and workspace waits. Other methods have no IdleMask. Numeric statistics are exact global linear quantiles over all valid published rows once, without padding or mask exclusion; no mean of per-episode quantiles. Image quantiles are omitted.
The E742 Scan Uniform left-wrist video was restored from original HDF5 after a native exporter dropped one frame; the verified repaired parent is used. Physical success and collision checks follow source receipts (2mm penetration gate). Publication does not imply user audit approval or authorization for model training. No train/test split is added.
Full seed list: 5, 7, 9, 12, 21, 26, 30, 34, 46, 50, 58, 59, 71, 72, 75, 76, 78, 82, 83, 84, 86, 87, 90, 95, 97, 98, 107, 118, 119, 127, 128, 130, 134, 135, 138, 141, 142, 146, 150, 151, 152, 154, 155, 159, 160, 164, 170, 172, 174, 185, 187, 189, 190, 191, 199, 200, 201, 202, 205, 211, 214, 218, 224, 231, 235, 237, 244, 247, 249, 253, 255, 257, 258, 261, 262, 265, 267, 275, 283, 290, 294, 298, 301, 306, 311, 312, 313, 319, 323, 326, 333, 344, 347, 353, 355, 356, 358, 362, 363, 365
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