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Datasets:
2bidoubi
/
Robo_trajectory

Tags:
robotics
lerobot
cobotmagic
embodichain
License:
Dataset card Files
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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_index: int64
stats: struct<observation.state: struct<min: list<item: double>, max: list<item: double>, mean: list<item:  (... 3885 chars omitted)
  child 0, observation.state: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 158 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
      child 5, q01: list<item: double>
          child 0, item: double
      child 6, q10: list<item: double>
          child 0, item: double
      child 7, q50: list<item: double>
          child 0, item: double
      child 8, q90: list<item: double>
          child 0, item: double
      child 9, q99: list<item: double>
          child 0, item: double
  child 1, observation.qvel: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 158 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>
 
...
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
      child 5, q01: list<item: double>
          child 0, item: double
      child 6, q10: list<item: double>
          child 0, item: double
      child 7, q50: list<item: double>
          child 0, item: double
      child 8, q90: list<item: double>
          child 0, item: double
      child 9, q99: list<item: double>
          child 0, item: double
  child 11, task_index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: dou (... 156 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
      child 5, q01: list<item: double>
          child 0, item: double
      child 6, q10: list<item: double>
          child 0, item: double
      child 7, q50: list<item: double>
          child 0, item: double
      child 8, q90: list<item: double>
          child 0, item: double
      child 9, q99: list<item: double>
          child 0, item: double
tasks: list<item: string>
  child 0, item: string
length: int64
to
{'episode_index': Value('int64'), 'tasks': List(Value('string')), 'length': 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 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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
              episode_index: int64
              stats: struct<observation.state: struct<min: list<item: double>, max: list<item: double>, mean: list<item:  (... 3885 chars omitted)
                child 0, observation.state: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 158 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
                    child 5, q01: list<item: double>
                        child 0, item: double
                    child 6, q10: list<item: double>
                        child 0, item: double
                    child 7, q50: list<item: double>
                        child 0, item: double
                    child 8, q90: list<item: double>
                        child 0, item: double
                    child 9, q99: list<item: double>
                        child 0, item: double
                child 1, observation.qvel: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 158 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>
               
              ...
              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
                    child 5, q01: list<item: double>
                        child 0, item: double
                    child 6, q10: list<item: double>
                        child 0, item: double
                    child 7, q50: list<item: double>
                        child 0, item: double
                    child 8, q90: list<item: double>
                        child 0, item: double
                    child 9, q99: list<item: double>
                        child 0, item: double
                child 11, task_index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: dou (... 156 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
                    child 5, q01: list<item: double>
                        child 0, item: double
                    child 6, q10: list<item: double>
                        child 0, item: double
                    child 7, q50: list<item: double>
                        child 0, item: double
                    child 8, q90: list<item: double>
                        child 0, item: double
                    child 9, q99: list<item: double>
                        child 0, item: double
              tasks: list<item: string>
                child 0, item: string
              length: int64
              to
              {'episode_index': Value('int64'), 'tasks': List(Value('string')), 'length': Value('int64')}
              because column names don't match

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RoboSynChallenge team trajectories

Expert and generated demonstrations collected by our team for the RoboSynChallenge CobotMagic tasks, as opposed to the official RoboSynChallenge/cobotmagic_Sim_* releases.

Each top-level folder is one dataset in the LeRobot layout (meta/, data/, videos/): 14-dimensional observation.state and action (two arms: 6 joints + gripper each), three 640x480 cameras (cam_high, cam_left_wrist, cam_right_wrist) at 25 Hz.

Pull and push

From a checkout of the team repository (arlo-yang/RoboSynChallenge):

python web/scripts/hf_hub.py list datasets
python web/scripts/hf_hub.py download dataset <name>                       # -> lerobot_dataset/<name>, commit in .hf_revision
python web/scripts/hf_hub.py download dataset <name> --revision <sha>      # exact version
python web/scripts/hf_hub.py upload   dataset lerobot_dataset/<task>/<dataset_dir> --name <task>_<setting>_<episodes>

Without the helper: hf download 2bidoubi/Robo_trajectory --repo-type dataset --include "<name>/*" --local-dir .

Conventions

  • Names: <task>_<setting>_<episodes> (expert collection, e.g. click_bell_random_100) or <task>_<source>_<episodes> (generated objects, e.g. drawer_open_place_meshy_tomato_50); _v2 for a re-collection. Do not overwrite a folder a checkpoint was trained on; push a new name.
  • Every push prints a commit sha. Record it next to the training run that uses the data.
  • Only validated episodes belong here (finite 14-D states and actions, 25 Hz timestamps, all three videos decodable, task success checks passed). Keep rejected attempts and raw images/ local.
  • example_click_bell_1ep is a one-episode smoke dataset for testing the round trip.

Team documentation: the Data → Trajectory hub section of the team page in the repository.
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