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twobidoubi
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Robo_trajectory

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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
status: string
asset_id: string
task: string
collection_status: string
target_episodes: int64
accepted_episodes: int64
episodes: int64
dataset: string
format: string
fps: int64
frames: int64
episode_checks: list<item: struct<episode: int64, seed: int64, frames: int64, quality: struct<passed: bool, checks:  (... 529 chars omitted)
  child 0, item: struct<episode: int64, seed: int64, frames: int64, quality: struct<passed: bool, checks: struct<toma (... 517 chars omitted)
      child 0, episode: int64
      child 1, seed: int64
      child 2, frames: int64
      child 3, quality: struct<passed: bool, checks: struct<tomato_center_inside_drawer: bool, tomato_bounds_inside_drawer:  (... 336 chars omitted)
          child 0, passed: bool
          child 1, checks: struct<tomato_center_inside_drawer: bool, tomato_bounds_inside_drawer: bool, tomato_moved: bool, tom (... 282 chars omitted)
              child 0, tomato_center_inside_drawer: bool
              child 1, tomato_bounds_inside_drawer: bool
              child 2, tomato_moved: bool
              child 3, tomato_stable_last_25: bool
              child 4, drawer_opened: bool
              child 5, grippers_released: bool
              child 6, complete_initial_and_scene_evidence: bool
              child 7, drawer_closed_at_mechanical_limit: bool
              child 8, drawer_closed_stable_last_25: bool
              child 9, drawer_joint_calibration_matches_actual: bool
              child 10, tomato_left_gripper_released_la
...

      child 0, decoded_frames: int64
      child 1, files: list<item: string>
          child 0, item: string
      child 2, episode_segments_verified: int64
      child 3, actual_headers: list<item: struct<width: int64, height: int64, frames: int64, rate: string, codec: string>>
          child 0, item: struct<width: int64, height: int64, frames: int64, rate: string, codec: string>
              child 0, width: int64
              child 1, height: int64
              child 2, frames: int64
              child 3, rate: string
              child 4, codec: string
  child 2, observation.images.cam_right_wrist: struct<decoded_frames: int64, files: list<item: string>, episode_segments_verified: int64, actual_he (... 99 chars omitted)
      child 0, decoded_frames: int64
      child 1, files: list<item: string>
          child 0, item: string
      child 2, episode_segments_verified: int64
      child 3, actual_headers: list<item: struct<width: int64, height: int64, frames: int64, rate: string, codec: string>>
          child 0, item: struct<width: int64, height: int64, frames: int64, rate: string, codec: string>
              child 0, width: int64
              child 1, height: int64
              child 2, frames: int64
              child 3, rate: string
              child 4, codec: string
episode_timestamps_hz: int64
original_receipt_files_verified: int64
finite_state_action_shape: list<item: int64>
  child 0, item: int64
frames_per_camera: int64
source_receipt_sha256: string
to
{'status': Value('string'), 'central_accepted_episodes': Value('int64'), 'frames_per_camera': Value('int64'), 'finite_state_action_shape': List(Value('int64')), 'labels_match_actual_physical_trace': Value('bool'), 'episode_timestamps_hz': Value('int64'), 'cameras': {'observation.images.cam_high': {'decoded_frames': Value('int64'), 'files': List(Value('string')), 'episode_segments_verified': Value('int64'), 'actual_headers': List({'width': Value('int64'), 'height': Value('int64'), 'frames': Value('int64'), 'rate': Value('string'), 'codec': Value('string')})}, 'observation.images.cam_left_wrist': {'decoded_frames': Value('int64'), 'files': List(Value('string')), 'episode_segments_verified': Value('int64'), 'actual_headers': List({'width': Value('int64'), 'height': Value('int64'), 'frames': Value('int64'), 'rate': Value('string'), 'codec': Value('string')})}, 'observation.images.cam_right_wrist': {'decoded_frames': Value('int64'), 'files': List(Value('string')), 'episode_segments_verified': Value('int64'), 'actual_headers': List({'width': Value('int64'), 'height': Value('int64'), 'frames': Value('int64'), 'rate': Value('string'), 'codec': Value('string')})}}, 'original_receipt_files_verified': Value('int64'), 'original_receipt_bytes_verified': Value('int64'), 'source_receipt_sha256': Value('string')}
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
              status: string
              asset_id: string
              task: string
              collection_status: string
              target_episodes: int64
              accepted_episodes: int64
              episodes: int64
              dataset: string
              format: string
              fps: int64
              frames: int64
              episode_checks: list<item: struct<episode: int64, seed: int64, frames: int64, quality: struct<passed: bool, checks:  (... 529 chars omitted)
                child 0, item: struct<episode: int64, seed: int64, frames: int64, quality: struct<passed: bool, checks: struct<toma (... 517 chars omitted)
                    child 0, episode: int64
                    child 1, seed: int64
                    child 2, frames: int64
                    child 3, quality: struct<passed: bool, checks: struct<tomato_center_inside_drawer: bool, tomato_bounds_inside_drawer:  (... 336 chars omitted)
                        child 0, passed: bool
                        child 1, checks: struct<tomato_center_inside_drawer: bool, tomato_bounds_inside_drawer: bool, tomato_moved: bool, tom (... 282 chars omitted)
                            child 0, tomato_center_inside_drawer: bool
                            child 1, tomato_bounds_inside_drawer: bool
                            child 2, tomato_moved: bool
                            child 3, tomato_stable_last_25: bool
                            child 4, drawer_opened: bool
                            child 5, grippers_released: bool
                            child 6, complete_initial_and_scene_evidence: bool
                            child 7, drawer_closed_at_mechanical_limit: bool
                            child 8, drawer_closed_stable_last_25: bool
                            child 9, drawer_joint_calibration_matches_actual: bool
                            child 10, tomato_left_gripper_released_la
              ...
              
                    child 0, decoded_frames: int64
                    child 1, files: list<item: string>
                        child 0, item: string
                    child 2, episode_segments_verified: int64
                    child 3, actual_headers: list<item: struct<width: int64, height: int64, frames: int64, rate: string, codec: string>>
                        child 0, item: struct<width: int64, height: int64, frames: int64, rate: string, codec: string>
                            child 0, width: int64
                            child 1, height: int64
                            child 2, frames: int64
                            child 3, rate: string
                            child 4, codec: string
                child 2, observation.images.cam_right_wrist: struct<decoded_frames: int64, files: list<item: string>, episode_segments_verified: int64, actual_he (... 99 chars omitted)
                    child 0, decoded_frames: int64
                    child 1, files: list<item: string>
                        child 0, item: string
                    child 2, episode_segments_verified: int64
                    child 3, actual_headers: list<item: struct<width: int64, height: int64, frames: int64, rate: string, codec: string>>
                        child 0, item: struct<width: int64, height: int64, frames: int64, rate: string, codec: string>
                            child 0, width: int64
                            child 1, height: int64
                            child 2, frames: int64
                            child 3, rate: string
                            child 4, codec: string
              episode_timestamps_hz: int64
              original_receipt_files_verified: int64
              finite_state_action_shape: list<item: int64>
                child 0, item: int64
              frames_per_camera: int64
              source_receipt_sha256: string
              to
              {'status': Value('string'), 'central_accepted_episodes': Value('int64'), 'frames_per_camera': Value('int64'), 'finite_state_action_shape': List(Value('int64')), 'labels_match_actual_physical_trace': Value('bool'), 'episode_timestamps_hz': Value('int64'), 'cameras': {'observation.images.cam_high': {'decoded_frames': Value('int64'), 'files': List(Value('string')), 'episode_segments_verified': Value('int64'), 'actual_headers': List({'width': Value('int64'), 'height': Value('int64'), 'frames': Value('int64'), 'rate': Value('string'), 'codec': Value('string')})}, 'observation.images.cam_left_wrist': {'decoded_frames': Value('int64'), 'files': List(Value('string')), 'episode_segments_verified': Value('int64'), 'actual_headers': List({'width': Value('int64'), 'height': Value('int64'), 'frames': Value('int64'), 'rate': Value('string'), 'codec': Value('string')})}, 'observation.images.cam_right_wrist': {'decoded_frames': Value('int64'), 'files': List(Value('string')), 'episode_segments_verified': Value('int64'), 'actual_headers': List({'width': Value('int64'), 'height': Value('int64'), 'frames': Value('int64'), 'rate': Value('string'), 'codec': Value('string')})}}, 'original_receipt_files_verified': Value('int64'), 'original_receipt_bytes_verified': Value('int64'), 'source_receipt_sha256': Value('string')}
              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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