Datasets:

tmeynier
/

hepha_act_100_new

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
timestamp: float
frame_index: int64
episode_index: int64
index: int64
task_index: int64
episode.drawer_index: int64
episode.cube_position: list<element: float>
  child 0, element: float
episode.cube_quaternion: list<element: float>
  child 0, element: float
observation.state: list<element: float>
  child 0, element: float
action: list<element: float>
  child 0, element: float
observation.state_raw: list<element: float>
  child 0, element: float
action_raw: list<element: float>
  child 0, element: float
to
{'observation.state': Value('float32'), 'action': Value('float32'), 'observation.images.head_camera': Video(decode=True, stream_index=None, dimension_order='NCHW', num_ffmpeg_threads=1, device='cpu', seek_mode='exact')}
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 478, 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/parquet/parquet.py", line 220, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 156, 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
              timestamp: float
              frame_index: int64
              episode_index: int64
              index: int64
              task_index: int64
              episode.drawer_index: int64
              episode.cube_position: list<element: float>
                child 0, element: float
              episode.cube_quaternion: list<element: float>
                child 0, element: float
              observation.state: list<element: float>
                child 0, element: float
              action: list<element: float>
                child 0, element: float
              observation.state_raw: list<element: float>
                child 0, element: float
              action_raw: list<element: float>
                child 0, element: float
              to
              {'observation.state': Value('float32'), 'action': Value('float32'), 'observation.images.head_camera': Video(decode=True, stream_index=None, dimension_order='NCHW', num_ffmpeg_threads=1, device='cpu', seek_mode='exact')}
              because column names don't match

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tmeynier/hepha_act_100_new

LeRobot-style behavior-cloning dataset generated from the Hepha MuJoCo simulation.

Summary

Robot type: hepha_mujoco
Codebase version: v3.0
Episodes: 100
Frames: 188546
FPS: 30
Joint normalization: min_max_0_1

Features

timestamp: float32 [1]
frame_index: int64 [1]
episode_index: int64 [1]
index: int64 [1]
task_index: int64 [1]
episode.drawer_index: int64 [1]
episode.cube_position: float32 [3]
episode.cube_quaternion: float32 [4]
observation.state: float32 [15]
action: float32 [15]
observation.state_raw: float32 [15]
action_raw: float32 [15]
observation.images.head_camera: video [3, 480, 640]

Policy-Facing Columns

observation.state: normalized robot joints in [0, 1]
action: normalized next-step robot joint targets in [0, 1]
observation.images.head_camera: RGB video frames from the robot camera

Extra Columns

observation.state_raw: raw MuJoCo joint positions
action_raw: raw next-step MuJoCo joint positions
episode.drawer_index: selected drawer index for each frame
episode.cube_position: initial cube position for each frame
episode.cube_quaternion: initial cube orientation for each frame

Notes

The dataset was produced by first computing the full IK episode, then resampling the robot joint trajectory at a constant normalized joint speed before saving frames/actions.

Joint limits and the normalization formula are stored in meta/info.json.

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