Datasets:

wkal
/

omy_insert_manual_qactual_state7_future1

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
observation.image: struct<bytes: binary, path: string>
  child 0, bytes: binary
  child 1, path: string
observation.wrist_image: struct<bytes: binary, path: string>
  child 0, bytes: binary
  child 1, path: string
observation.state: fixed_size_list<element: float>[7]
  child 0, element: float
action: fixed_size_list<element: float>[7]
  child 0, element: float
obj_init: fixed_size_list<element: float>[6]
  child 0, element: float
timestamp: float
frame_index: int64
episode_index: int64
index: int64
task_index: int64
-- schema metadata --
huggingface: '{"info": {"features": {"observation.image": {"_type": "Imag' + 617
to
{'observation.image': Image(mode=None, decode=True), 'observation.wrist_image': Image(mode=None, decode=True), 'observation.state': List(Value('float32')), 'action': List(Value('float32')), 'obj_init': List(Value('float32'))}
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
              observation.image: struct<bytes: binary, path: string>
                child 0, bytes: binary
                child 1, path: string
              observation.wrist_image: struct<bytes: binary, path: string>
                child 0, bytes: binary
                child 1, path: string
              observation.state: fixed_size_list<element: float>[7]
                child 0, element: float
              action: fixed_size_list<element: float>[7]
                child 0, element: float
              obj_init: fixed_size_list<element: float>[6]
                child 0, element: float
              timestamp: float
              frame_index: int64
              episode_index: int64
              index: int64
              task_index: int64
              -- schema metadata --
              huggingface: '{"info": {"features": {"observation.image": {"_type": "Imag' + 617
              to
              {'observation.image': Image(mode=None, decode=True), 'observation.wrist_image': Image(mode=None, decode=True), 'observation.state': List(Value('float32')), 'action': List(Value('float32')), 'obj_init': List(Value('float32'))}
              because column names don't match

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OMY Insert Manual Dual View q_actual State7 Future1

This is the converted dual-view LeRobot dataset for the MuJoCo plug insertion task.

Semantics

fps: 20 Hz
observation.image: agent-view RGB image, 256 x 256
observation.wrist_image: wrist/egocentric RGB image, 256 x 256
observation.state[t]: measured joint state q_actual[t], 7D [joint1..joint6, gripper_actual]
action[t]: measured next-frame joint state q_actual[t+1], 7D [joint1..joint6, gripper_actual]
With chunk_size=15, a training sample at frame t uses: action_chunk_t = [q_actual[t+1], ..., q_actual[t+15]]

The source manual collection stored same-frame measured joint states as action. This dataset shifts those labels by one frame and uses the same measured joint state as the policy state input.

Recommended Training Config

Use smolvla_insert_manual_y_env4_qactual_state7_chunk15.yaml from the companion GitHub repository:

chunk_size: 15
n_action_steps: 10
batch_size: 1
observation.state.shape: [7]
action.shape: [7]

At inference, the policy predicts 15 actions and executes the first 10 before replanning from a fresh observation.

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