Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
Paper • 2304.13705 • Published • 7
Datasets:
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
task_index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: dou (... 31 chars omitted)
child 0, min: list<item: int64>
child 0, item: int64
child 1, max: list<item: int64>
child 0, item: int64
child 2, mean: list<item: double>
child 0, item: double
child 3, std: list<item: double>
child 0, item: double
child 4, count: list<item: int64>
child 0, item: int64
observation.state: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 33 chars omitted)
child 0, min: list<item: double>
child 0, item: double
child 1, max: list<item: double>
child 0, item: double
child 2, mean: list<item: double>
child 0, item: double
child 3, std: list<item: double>
child 0, item: double
child 4, count: list<item: int64>
child 0, item: int64
timestamp: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 33 chars omitted)
child 0, min: list<item: double>
child 0, item: double
child 1, max: list<item: double>
child 0, item: double
child 2, mean: list<item: double>
child 0, item: double
child 3, std: list<item: double>
child 0, item: double
child 4, count: list<item: int64>
child 0, item: int64
next.done: struct<min: list<item: bool>, max: list<item: bool>, mean: list<item: double>, std: list<item: doubl (... 29 chars omitted)
child
...
uct<dtype: string, shape: list<item: int64>, names: null, fps: double>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 3, fps: double
child 5, timestamp: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 3, fps: double
child 6, next.done: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 3, fps: double
child 7, index: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 3, fps: double
child 8, task_index: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 3, fps: double
robot_type: string
files_size_in_mb: double
chunks_size: int64
total_frames: int64
splits: struct<train: string>
child 0, train: string
codebase_version: string
to
{'codebase_version': Value('string'), 'robot_type': Value('string'), 'total_episodes': Value('int64'), 'total_frames': Value('int64'), 'total_tasks': Value('int64'), 'chunks_size': Value('int64'), 'fps': Value('int64'), 'splits': {'train': Value('string')}, 'data_path': Value('string'), 'video_path': Value('string'), 'features': {'Time': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'episode_index': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'task_index': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'frame_index': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'sample_index': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'next_done': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'observation_state_0': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'observation_state_1': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'observation_state_2': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'observation_state_3': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'observation_state_4': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'observation_state_5': {'dtype': Value('string
...
dex': Value('string'), 'next.done': Value('string')}, 'dropped': {'timestamp': Value('string'), 'observation.images.top': Value('string')}, 'note': Value('string')}, 'original_features': {'observation.images.top': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': List(Value('string')), 'video_info': {'video.fps': Value('float64'), 'video.codec': Value('string'), 'video.pix_fmt': Value('string'), 'video.is_depth_map': Value('bool'), 'has_audio': Value('bool')}}, 'observation.state': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': {'motors': List(Value('string'))}, 'fps': Value('float64')}, 'action': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': {'motors': List(Value('string'))}, 'fps': Value('float64')}, 'episode_index': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'frame_index': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'timestamp': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'next.done': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'index': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'task_index': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}}}}
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
task_index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: dou (... 31 chars omitted)
child 0, min: list<item: int64>
child 0, item: int64
child 1, max: list<item: int64>
child 0, item: int64
child 2, mean: list<item: double>
child 0, item: double
child 3, std: list<item: double>
child 0, item: double
child 4, count: list<item: int64>
child 0, item: int64
observation.state: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 33 chars omitted)
child 0, min: list<item: double>
child 0, item: double
child 1, max: list<item: double>
child 0, item: double
child 2, mean: list<item: double>
child 0, item: double
child 3, std: list<item: double>
child 0, item: double
child 4, count: list<item: int64>
child 0, item: int64
timestamp: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 33 chars omitted)
child 0, min: list<item: double>
child 0, item: double
child 1, max: list<item: double>
child 0, item: double
child 2, mean: list<item: double>
child 0, item: double
child 3, std: list<item: double>
child 0, item: double
child 4, count: list<item: int64>
child 0, item: int64
next.done: struct<min: list<item: bool>, max: list<item: bool>, mean: list<item: double>, std: list<item: doubl (... 29 chars omitted)
child
...
uct<dtype: string, shape: list<item: int64>, names: null, fps: double>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 3, fps: double
child 5, timestamp: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 3, fps: double
child 6, next.done: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 3, fps: double
child 7, index: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 3, fps: double
child 8, task_index: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: null
child 3, fps: double
robot_type: string
files_size_in_mb: double
chunks_size: int64
total_frames: int64
splits: struct<train: string>
child 0, train: string
codebase_version: string
to
{'codebase_version': Value('string'), 'robot_type': Value('string'), 'total_episodes': Value('int64'), 'total_frames': Value('int64'), 'total_tasks': Value('int64'), 'chunks_size': Value('int64'), 'fps': Value('int64'), 'splits': {'train': Value('string')}, 'data_path': Value('string'), 'video_path': Value('string'), 'features': {'Time': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'episode_index': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'task_index': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'frame_index': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'sample_index': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'next_done': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'observation_state_0': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'observation_state_1': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'observation_state_2': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'observation_state_3': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'observation_state_4': {'dtype': Value('string'), 'role': Value('string'), 'shape': List(Value('int64'))}, 'observation_state_5': {'dtype': Value('string
...
dex': Value('string'), 'next.done': Value('string')}, 'dropped': {'timestamp': Value('string'), 'observation.images.top': Value('string')}, 'note': Value('string')}, 'original_features': {'observation.images.top': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': List(Value('string')), 'video_info': {'video.fps': Value('float64'), 'video.codec': Value('string'), 'video.pix_fmt': Value('string'), 'video.is_depth_map': Value('bool'), 'has_audio': Value('bool')}}, 'observation.state': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': {'motors': List(Value('string'))}, 'fps': Value('float64')}, 'action': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': {'motors': List(Value('string'))}, 'fps': Value('float64')}, 'episode_index': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'frame_index': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'timestamp': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'next.done': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'index': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'task_index': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}}}}
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.
aloha_sim_transfer_cube_human (TsFile)
Apache TsFile version of the LeRobot dataset
lerobot/aloha_sim_transfer_cube_human.
Overview
ALOHA simulated bimanual transfer-cube manipulation demonstrations.
- Robot: ALOHA (simulated)
- Episodes: 50
- Frames: 20,000
- Sampling rate: 50 fps
- Tasks: 1
Schema (TsFile structure)
- Time (INT64, milliseconds) —
round(timestamp * 1000), restarting per episode. - episode_index / task_index (TAG) — the device dimension. Query a single episode with
WHERE episode_index=N. - FIELD —
frame_index,sample_index, and the flattened state/action vectors (observation_state_0..13,action_0..13) as single-precision FLOAT.
The robot's camera video streams are time-series-irrelevant and not uploaded to this repository;
get them from the original dataset (its videos/ directory). The source meta/ is mirrored here.
Usage
Read the .tsfile files with the Apache TsFile Java or Python SDK.
Source & license
- Original dataset:
lerobot/aloha_sim_transfer_cube_human - Author / publisher: LeRobot team / ALOHA
- Paper: arXiv:2304.13705
- License: mit
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