Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
actionSchema: struct<activitySegmentation: struct<algorithm: string, commandEpsilon: double, jointGroups: struct<h (... 616 chars omitted)
child 0, activitySegmentation: struct<algorithm: string, commandEpsilon: double, jointGroups: struct<head: list<item: string>, left (... 210 chars omitted)
child 0, algorithm: string
child 1, commandEpsilon: double
child 2, jointGroups: struct<head: list<item: string>, leftArm: list<item: string>, rightArm: list<item: string>>
child 0, head: list<item: string>
child 0, item: string
child 1, leftArm: list<item: string>
child 0, item: string
child 2, rightArm: list<item: string>
child 0, item: string
child 3, locomotionModalities: list<item: string>
child 0, item: string
child 4, manipulationModalities: list<item: string>
child 0, item: string
child 5, maximumInactiveGapMs: int64
child 6, motionEpsilon: double
child 7, windowMs: int64
child 1, modalities: struct<base: struct<end: int64, fields: list<item: string>, start: int64>, head: struct<end: int64, (... 262 chars omitted)
child 0, base: struct<end: int64, fields: list<item: string>, start: int64>
child 0, end: int64
child 1, fields: list<item: string>
child 0, item: string
child 2, start: int64
child 1, head: struct<end: int64, fields: list<item: string>, start: int64>
child 0, e
...
item: struct<durationNs: int64, episodeId: string, policySelectedRangesNs: list<item: list<item: int64>>, (... 16 chars omitted)
child 0, durationNs: int64
child 1, episodeId: string
child 2, policySelectedRangesNs: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
child 3, robotId: string
sourceIntervals: list<item: struct<episodeId: string, rangesNs: list<item: list<item: int64>>>>
child 0, item: struct<episodeId: string, rangesNs: list<item: list<item: int64>>>
child 0, episodeId: string
child 1, rangesNs: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
taskTypeVersionId: string
createdAt: string
sourceEpisodes: list<item: struct<acquisitionMode: string, embodimentProfileVersionId: string, episodeId: string, ep (... 143 chars omitted)
child 0, item: struct<acquisitionMode: string, embodimentProfileVersionId: string, episodeId: string, episodeIndex: (... 131 chars omitted)
child 0, acquisitionMode: string
child 1, embodimentProfileVersionId: string
child 2, episodeId: string
child 3, episodeIndex: int64
child 4, segmentPolicy: string
child 5, selectedRangesNs: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
child 6, sourceDurationNs: int64
child 7, taskTypeVersionId: string
schemaVersion: string
profile: string
to
{'collectionClass': Value('string'), 'createdAt': Value('string'), 'datasetId': Value('string'), 'exportId': Value('string'), 'profile': Value('string'), 'schemaVersion': Value('string'), 'sourceEpisodes': List({'acquisitionMode': Value('string'), 'embodimentProfileVersionId': Value('string'), 'episodeId': Value('string'), 'episodeIndex': Value('int64'), 'segmentPolicy': Value('string'), 'selectedRangesNs': List(List(Value('int64'))), 'sourceDurationNs': Value('int64'), 'taskTypeVersionId': 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
actionSchema: struct<activitySegmentation: struct<algorithm: string, commandEpsilon: double, jointGroups: struct<h (... 616 chars omitted)
child 0, activitySegmentation: struct<algorithm: string, commandEpsilon: double, jointGroups: struct<head: list<item: string>, left (... 210 chars omitted)
child 0, algorithm: string
child 1, commandEpsilon: double
child 2, jointGroups: struct<head: list<item: string>, leftArm: list<item: string>, rightArm: list<item: string>>
child 0, head: list<item: string>
child 0, item: string
child 1, leftArm: list<item: string>
child 0, item: string
child 2, rightArm: list<item: string>
child 0, item: string
child 3, locomotionModalities: list<item: string>
child 0, item: string
child 4, manipulationModalities: list<item: string>
child 0, item: string
child 5, maximumInactiveGapMs: int64
child 6, motionEpsilon: double
child 7, windowMs: int64
child 1, modalities: struct<base: struct<end: int64, fields: list<item: string>, start: int64>, head: struct<end: int64, (... 262 chars omitted)
child 0, base: struct<end: int64, fields: list<item: string>, start: int64>
child 0, end: int64
child 1, fields: list<item: string>
child 0, item: string
child 2, start: int64
child 1, head: struct<end: int64, fields: list<item: string>, start: int64>
child 0, e
...
item: struct<durationNs: int64, episodeId: string, policySelectedRangesNs: list<item: list<item: int64>>, (... 16 chars omitted)
child 0, durationNs: int64
child 1, episodeId: string
child 2, policySelectedRangesNs: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
child 3, robotId: string
sourceIntervals: list<item: struct<episodeId: string, rangesNs: list<item: list<item: int64>>>>
child 0, item: struct<episodeId: string, rangesNs: list<item: list<item: int64>>>
child 0, episodeId: string
child 1, rangesNs: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
taskTypeVersionId: string
createdAt: string
sourceEpisodes: list<item: struct<acquisitionMode: string, embodimentProfileVersionId: string, episodeId: string, ep (... 143 chars omitted)
child 0, item: struct<acquisitionMode: string, embodimentProfileVersionId: string, episodeId: string, episodeIndex: (... 131 chars omitted)
child 0, acquisitionMode: string
child 1, embodimentProfileVersionId: string
child 2, episodeId: string
child 3, episodeIndex: int64
child 4, segmentPolicy: string
child 5, selectedRangesNs: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
child 6, sourceDurationNs: int64
child 7, taskTypeVersionId: string
schemaVersion: string
profile: string
to
{'collectionClass': Value('string'), 'createdAt': Value('string'), 'datasetId': Value('string'), 'exportId': Value('string'), 'profile': Value('string'), 'schemaVersion': Value('string'), 'sourceEpisodes': List({'acquisitionMode': Value('string'), 'embodimentProfileVersionId': Value('string'), 'episodeId': Value('string'), 'episodeIndex': Value('int64'), 'segmentPolicy': Value('string'), 'selectedRangesNs': List(List(Value('int64'))), 'sourceDurationNs': Value('int64'), 'taskTypeVersionId': 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.
LASER_CELL
Export facts
- Format: GR00T-compatible LeRobot v2
- Collection class:
production - Output episodes: 8
- Frames / samples: 12567
- Output rate: 8.000 Hz
- Original source time: 1824.872 s
- Selected source time: 1570.875 s
- Excluded source time: 253.997 s
- Embodiment profile:
b1111111-1111-4111-8111-111111111117 - Dataset:
2636e7a9-dd4c-4365-bf95-5aa91f726595 - Export:
53875f17-c1e2-5d14-b9bb-fcaae52e996b
Named modalities, units where declared, source inputs (including wholly excluded episodes), actual intervals and selection policy are recorded in dataset-card.json. Excluded time includes policy filtering and unavailable continuous coverage. Missing units are not inferred.
Limitations
Training episodes are selected continuous source intervals, not independent physical recording sessions. Missing data are not reconstructed. Optional modality validity must be respected. Source Capture and HBR retain the complete recording. Quality passage is not proof of task success or suitability for a particular training objective.
Open locally
Use the official loader from https://github.com/NVIDIA/Isaac-GR00T at commit 23ace64f17aa5015259b8609d371eb61a357c776, as pinned by the DATA_NODE compatibility environment:
import json
from pathlib import Path
from gr00t.data.dataset.lerobot_episode_loader import LeRobotEpisodeLoader
from gr00t.data.types import ModalityConfig
root = Path("/path/to/export")
modalities = json.loads((root / "meta/modality.json").read_text())
config = {
group: ModalityConfig(delta_indices=[0], modality_keys=list(modalities[group]))
for group in ("state", "action", "video") if modalities.get(group)
}
config["language"] = ModalityConfig(
delta_indices=[0], modality_keys=["annotation.human.action.task_description"]
)
dataset = LeRobotEpisodeLoader(dataset_path=root, modality_configs=config)
print(len(dataset), dataset[0])
Owner notes
Add the task description, intended uses and limitations here. No dataset license is inferred or granted by this export.
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