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
status: string
lerobot_dataset_rows: int64
sh5_array_rows: int64
episodes: int64
video_backend: string
actual_lerobot_samples_checked: list<item: int64>
child 0, item: int64
task_text_verified: bool
state_action_shape: list<item: int64>
child 0, item: int64
per_hand_tactile_shape: list<item: int64>
child 0, item: int64
image_shape: list<item: int64>
child 0, item: int64
all_grid_times_within_phase: bool
paired_tactile_baselines_identical: bool
all_video_header_frame_counts_match: bool
frames: int64
uniform_30hz_grid: bool
schema_joint_camera_order_matches_template: bool
representative_original_mcap_and_video_checks: list<item: struct<episode_index: int64, source_sensor_rows_exact: list<item: int64>, source_video_fr (... 37 chars omitted)
child 0, item: struct<episode_index: int64, source_sensor_rows_exact: list<item: int64>, source_video_frames_pixel_ (... 25 chars omitted)
child 0, episode_index: int64
child 1, source_sensor_rows_exact: list<item: int64>
child 0, item: int64
child 2, source_video_frames_pixel_exact: list<item: int64>
child 0, item: int64
all_numeric_values_finite: bool
episode_indices_offsets_timestamps_consistent: bool
to
{'status': Value('string'), 'episodes': Value('int64'), 'frames': Value('int64'), 'schema_joint_camera_order_matches_template': Value('bool'), 'all_numeric_values_finite': Value('bool'), 'episode_indices_offsets_timestamps_consistent': Value('bool'), 'uniform_30hz_grid': Value('bool'), 'all_grid_times_within_phase': Value('bool'), 'paired_tactile_baselines_identical': Value('bool'), 'all_video_header_frame_counts_match': Value('bool'), 'representative_original_mcap_and_video_checks': List({'episode_index': Value('int64'), 'source_sensor_rows_exact': List(Value('int64')), 'source_video_frames_pixel_exact': List(Value('int64'))})}
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
status: string
lerobot_dataset_rows: int64
sh5_array_rows: int64
episodes: int64
video_backend: string
actual_lerobot_samples_checked: list<item: int64>
child 0, item: int64
task_text_verified: bool
state_action_shape: list<item: int64>
child 0, item: int64
per_hand_tactile_shape: list<item: int64>
child 0, item: int64
image_shape: list<item: int64>
child 0, item: int64
all_grid_times_within_phase: bool
paired_tactile_baselines_identical: bool
all_video_header_frame_counts_match: bool
frames: int64
uniform_30hz_grid: bool
schema_joint_camera_order_matches_template: bool
representative_original_mcap_and_video_checks: list<item: struct<episode_index: int64, source_sensor_rows_exact: list<item: int64>, source_video_fr (... 37 chars omitted)
child 0, item: struct<episode_index: int64, source_sensor_rows_exact: list<item: int64>, source_video_frames_pixel_ (... 25 chars omitted)
child 0, episode_index: int64
child 1, source_sensor_rows_exact: list<item: int64>
child 0, item: int64
child 2, source_video_frames_pixel_exact: list<item: int64>
child 0, item: int64
all_numeric_values_finite: bool
episode_indices_offsets_timestamps_consistent: bool
to
{'status': Value('string'), 'episodes': Value('int64'), 'frames': Value('int64'), 'schema_joint_camera_order_matches_template': Value('bool'), 'all_numeric_values_finite': Value('bool'), 'episode_indices_offsets_timestamps_consistent': Value('bool'), 'uniform_30hz_grid': Value('bool'), 'all_grid_times_within_phase': Value('bool'), 'paired_tactile_baselines_identical': Value('bool'), 'all_video_header_frame_counts_match': Value('bool'), 'representative_original_mcap_and_video_checks': List({'episode_index': Value('int64'), 'source_sensor_rows_exact': List(Value('int64')), 'source_video_frames_pixel_exact': List(Value('int64'))})}
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.
Task_000519_Pour_Water_Into_Cup_Hand_Intern_lerobot_v30
SH5 bimanual water-pouring demonstrations: pick and pour.
- 159 episodes, numbered 0–158.
- Episodes 0–80 originate from Task 000519; episodes 81–158 originate from Task 000608 (original episodes 4–82, excluding 56).
- The phase boundary is the start of final bottle uprighting after pouring. Uprighting and object placement belong to the second phase.
source_mapping.csvrecords the original episode numbers.
LeRobot v3 at 30 Hz, 123,853 frames. State and action are 54-dimensional; raw tactile and baseline are 45-dimensional per hand. The 672×376 head-camera video retains its recorded orientation.
Actions are projected to the recorded follower URDF limits, matching the existing SH5 training contract. Raw leader commands remain in the MCAP datasets. Both phases use a common baseline calculated from the first 20 raw tactile messages in the paired pick-and-pour episode. Video is causally resampled using camera header timestamps and losslessly encoded.
See conversion_receipt.json, validation_receipt.json, and loader_validation_receipt.json for conversion and validation details. Local absolute paths in provenance records describe the producer workspace; dataset loading uses the relative paths in meta/info.json.
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