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
episode_0: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_1: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_2: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_3: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_4: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_5: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_6: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_7: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_8: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_9: struct<info: struct<{A}: string
...
ing, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_49: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
schema_version: int64
collection_method: string
name: string
entries: list<item: struct<id: string, task: string, scene: string, embodiment: string, episodes: int64, spee (... 279 chars omitted)
child 0, item: struct<id: string, task: string, scene: string, embodiment: string, episodes: int64, speed_mps: stru (... 267 chars omitted)
child 0, id: string
child 1, task: string
child 2, scene: string
child 3, embodiment: string
child 4, episodes: int64
child 5, speed_mps: struct<min: double, q10: double, q25: double, median: double, q75: double, q90: double, max: double, (... 14 chars omitted)
child 0, min: double
child 1, q10: double
child 2, q25: double
child 3, median: double
child 4, q75: double
child 5, q90: double
child 6, max: double
child 7, mean: double
child 6, artifact: struct<path: string, size_bytes: int64, sha256: string, file_count: int64, internal_root: string>
child 0, path: string
child 1, size_bytes: int64
child 2, sha256: string
child 3, file_count: int64
child 4, internal_root: string
child 7, metadata_path: string
child 8, quality_status: string
to
{'schema_version': Value('int64'), 'name': Value('string'), 'collection_method': Value('string'), 'entries': List({'id': Value('string'), 'task': Value('string'), 'scene': Value('string'), 'embodiment': Value('string'), 'episodes': Value('int64'), 'speed_mps': {'min': Value('float64'), 'q10': Value('float64'), 'q25': Value('float64'), 'median': Value('float64'), 'q75': Value('float64'), 'q90': Value('float64'), 'max': Value('float64'), 'mean': Value('float64')}, 'artifact': {'path': Value('string'), 'size_bytes': Value('int64'), 'sha256': Value('string'), 'file_count': Value('int64'), 'internal_root': Value('string')}, 'metadata_path': Value('string'), 'quality_status': Value('string')})}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
episode_0: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_1: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_2: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_3: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_4: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_5: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_6: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_7: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_8: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_9: struct<info: struct<{A}: string
...
ing, {a}: string>
child 0, {A}: string
child 1, {a}: string
episode_49: struct<info: struct<{A}: string, {a}: string>>
child 0, info: struct<{A}: string, {a}: string>
child 0, {A}: string
child 1, {a}: string
schema_version: int64
collection_method: string
name: string
entries: list<item: struct<id: string, task: string, scene: string, embodiment: string, episodes: int64, spee (... 279 chars omitted)
child 0, item: struct<id: string, task: string, scene: string, embodiment: string, episodes: int64, speed_mps: stru (... 267 chars omitted)
child 0, id: string
child 1, task: string
child 2, scene: string
child 3, embodiment: string
child 4, episodes: int64
child 5, speed_mps: struct<min: double, q10: double, q25: double, median: double, q75: double, q90: double, max: double, (... 14 chars omitted)
child 0, min: double
child 1, q10: double
child 2, q25: double
child 3, median: double
child 4, q75: double
child 5, q90: double
child 6, max: double
child 7, mean: double
child 6, artifact: struct<path: string, size_bytes: int64, sha256: string, file_count: int64, internal_root: string>
child 0, path: string
child 1, size_bytes: int64
child 2, sha256: string
child 3, file_count: int64
child 4, internal_root: string
child 7, metadata_path: string
child 8, quality_status: string
to
{'schema_version': Value('int64'), 'name': Value('string'), 'collection_method': Value('string'), 'entries': List({'id': Value('string'), 'task': Value('string'), 'scene': Value('string'), 'embodiment': Value('string'), 'episodes': Value('int64'), 'speed_mps': {'min': Value('float64'), 'q10': Value('float64'), 'q25': Value('float64'), 'median': Value('float64'), 'q75': Value('float64'), 'q90': Value('float64'), 'max': Value('float64'), 'mean': Value('float64')}, 'artifact': {'path': Value('string'), 'size_bytes': Value('int64'), 'sha256': Value('string'), 'file_count': Value('int64'), 'internal_root': Value('string')}, 'metadata_path': Value('string'), 'quality_status': 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.
DOMINO Extended: Online-Follow
This repository contains online-follow demonstrations for multiple robot tasks. Because every entry uses the same follow-style collector, the data hierarchy only distinguishes task and scene.
Current data: 1 task/scene entries, 50 episodes.
| Task | Scene | Episodes | Speed (m/s) | Details |
|---|---|---|---|---|
adjust_bottle |
dynamic-clean |
50 | 0.040--0.117 | metadata |
Layout
dataset/<task>/<embodiment>_<scene>_<episodes>.zip
metadata/<task>_<scene>.json
provenance/<task>_<scene>/
catalog.json
Download the current entry
hf download HanningHanning/DOMINO-Extended dataset/adjust_bottle/aloha-agilex_dynamic-clean_50.zip --repo-type dataset --local-dir DOMINO-Extended
The bottle starts outside the camera view and moves independently. The expert observes it online, approaches, follows, grasps, lifts, and completes the original upright placement task without access to a future object trajectory or a precomputed interception point. Raw HDF5 archives are canonical; converted LeRobot copies and runtime caches are excluded.
License and attribution
Released under Apache-2.0, following the upstream DOMINO repository and dataset. See each metadata file for its integrity checks and provenance snapshot.
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