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2000_delta_ee_oxe_gr00t

Tags:
tsfile
time-series
robotics
LeRobot
GR00T
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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
total_episodes: int64
total_frames: int64
fps: int64
robot_type: string
embodiment_tag: string
data_path: string
features: struct<Time: struct<dtype: string, shape: list<item: int64>, tsfile_role: string, unit: string>, epi (... 1908 chars omitted)
  child 0, Time: struct<dtype: string, shape: list<item: int64>, tsfile_role: string, unit: string>
      child 0, dtype: string
      child 1, shape: list<item: int64>
          child 0, item: int64
      child 2, tsfile_role: string
      child 3, unit: string
  child 1, episode_index: struct<dtype: string, shape: list<item: int64>, tsfile_role: string>
      child 0, dtype: string
      child 1, shape: list<item: int64>
          child 0, item: int64
      child 2, tsfile_role: string
  child 2, task_index: struct<dtype: string, shape: list<item: int64>, tsfile_role: string>
      child 0, dtype: string
      child 1, shape: list<item: int64>
          child 0, item: int64
      child 2, tsfile_role: string
  child 3, sample_index: struct<dtype: string, shape: list<item: int64>, tsfile_role: string>
      child 0, dtype: string
      child 1, shape: list<item: int64>
          child 0, item: int64
      child 2, tsfile_role: string
  child 4, annotation_human_action_task_description: struct<dtype: string, shape: list<item: int64>, tsfile_role: string>
      child 0, dtype: string
      child 1, shape: list<item: int64>
          child 0, item: int64
      child 2, tsfile_role: string
  child 5, annotation_human_validity: struct<
...
hild 0, dtype: string
      child 1, shape: list<item: int64>
          child 0, item: int64
      child 2, tsfile_role: string
tsfile_conversion: struct<source_dataset: string, source_data_path: null, converted_data_path: string, table_name: stri (... 510 chars omitted)
  child 0, source_dataset: string
  child 1, source_data_path: null
  child 2, converted_data_path: string
  child 3, table_name: string
  child 4, granularity: string
  child 5, time_precision: string
  child 6, time_mapping: struct<source: string, fps: int64, unit: string>
      child 0, source: string
      child 1, fps: int64
      child 2, unit: string
  child 7, tag_columns: list<item: string>
      child 0, item: string
  child 8, row_count: int64
  child 9, feature_source: string
  child 10, flattened_features: struct<observation.state: list<item: string>, action: list<item: string>>
      child 0, observation.state: list<item: string>
          child 0, item: string
      child 1, action: list<item: string>
          child 0, item: string
  child 11, renamed_features: struct<index: string>
      child 0, index: string
  child 12, dropped_features: list<item: string>
      child 0, item: string
  child 13, omitted_features: list<item: null>
      child 0, item: null
  child 14, original_video_path: null
  child 15, original_video_features: struct<>
  child 16, original_video_source: null
  child 17, video_policy: string
length: int64
tasks: list<item: int64>
  child 0, item: int64
episode_index: int64
to
{'episode_index': Value('int64'), 'tasks': List(Value('int64')), 'length': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                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 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
              total_episodes: int64
              total_frames: int64
              fps: int64
              robot_type: string
              embodiment_tag: string
              data_path: string
              features: struct<Time: struct<dtype: string, shape: list<item: int64>, tsfile_role: string, unit: string>, epi (... 1908 chars omitted)
                child 0, Time: struct<dtype: string, shape: list<item: int64>, tsfile_role: string, unit: string>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, tsfile_role: string
                    child 3, unit: string
                child 1, episode_index: struct<dtype: string, shape: list<item: int64>, tsfile_role: string>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, tsfile_role: string
                child 2, task_index: struct<dtype: string, shape: list<item: int64>, tsfile_role: string>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, tsfile_role: string
                child 3, sample_index: struct<dtype: string, shape: list<item: int64>, tsfile_role: string>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, tsfile_role: string
                child 4, annotation_human_action_task_description: struct<dtype: string, shape: list<item: int64>, tsfile_role: string>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, tsfile_role: string
                child 5, annotation_human_validity: struct<
              ...
              hild 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, tsfile_role: string
              tsfile_conversion: struct<source_dataset: string, source_data_path: null, converted_data_path: string, table_name: stri (... 510 chars omitted)
                child 0, source_dataset: string
                child 1, source_data_path: null
                child 2, converted_data_path: string
                child 3, table_name: string
                child 4, granularity: string
                child 5, time_precision: string
                child 6, time_mapping: struct<source: string, fps: int64, unit: string>
                    child 0, source: string
                    child 1, fps: int64
                    child 2, unit: string
                child 7, tag_columns: list<item: string>
                    child 0, item: string
                child 8, row_count: int64
                child 9, feature_source: string
                child 10, flattened_features: struct<observation.state: list<item: string>, action: list<item: string>>
                    child 0, observation.state: list<item: string>
                        child 0, item: string
                    child 1, action: list<item: string>
                        child 0, item: string
                child 11, renamed_features: struct<index: string>
                    child 0, index: string
                child 12, dropped_features: list<item: string>
                    child 0, item: string
                child 13, omitted_features: list<item: null>
                    child 0, item: null
                child 14, original_video_path: null
                child 15, original_video_features: struct<>
                child 16, original_video_source: null
                child 17, video_policy: string
              length: int64
              tasks: list<item: int64>
                child 0, item: int64
              episode_index: int64
              to
              {'episode_index': Value('int64'), 'tasks': List(Value('int64')), 'length': Value('int64')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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End of preview.

2000_delta_ee_oxe_gr00t TsFile Conversion

This dataset is a TsFile conversion of kaveh-kamali/2000_delta_ee_oxe_gr00t, a LeRobot/GR00T-style Franka robot dataset with OXE_DROID embodiment metadata.

Modalities: Time-series. Camera videos, if present in the original dataset, are not included in this converted repository.

Source Dataset Facts

From the downloaded source metadata:

  • Source dataset: kaveh-kamali/2000_delta_ee_oxe_gr00t
  • Robot type: franka
  • Embodiment tag: OXE_DROID
  • Episodes: 2,007
  • Frames / converted rows: 423,477
  • Sampling rate: 20 fps
  • Tasks metadata:
    • task_index=0: lift the red cube
    • task_index=1: valid

Converted Files

  • data/delta_ee_oxe_gr00t_2000.tsfile — one merged TsFile containing all 2,007 episodes.
  • meta/ — mirrored source metadata with meta/info.json updated to describe the TsFile artifact.

The generated TsFile size is 16,323,311 bytes.

TsFile Schema

  • Table name: delta_ee_oxe_gr00t_2000
  • Time precision: milliseconds (ms)
  • Time: synthesized as round(timestamp * 1000). Time restarts per episode.
  • TAG columns: episode_index, task_index
  • FIELD columns:
    • sample_index — source index renamed for clarity.
    • annotation_human_action_task_description
    • annotation_human_validity
    • next_reward
    • next_done
    • observation_state_0 … observation_state_7 — flattened from observation.state as FLOAT fields.
    • action_0 … action_6 — flattened from action as FLOAT fields.

Per the source meta/modality.json, the action vector represents 3 end-effector position deltas, 3 end-effector RPY rotation deltas, and 1 absolute gripper-position value. The modality metadata declares state segments for joint positions and gripper position; the converted schema reflects the actual Parquet vector width observed during conversion (observation_state_0 … observation_state_7).

Conversion Notes

  • Converter: generic scripts/converters/lerobot.py.
  • Source timestamp is dropped after creating Time, because it equals Time / 1000 seconds.
  • Source index is renamed to sample_index.
  • Vector columns preserve source names by replacing . with _ and appending the element index.
  • Videos are not uploaded; use the original Hugging Face dataset for source videos if needed.
  • Aside from the redundant timestamp column, no numeric time-series rows are intentionally dropped.

Minimal Read Example 

# Use Apache TsFile tooling to read:
# data/delta_ee_oxe_gr00t_2000.tsfile
# Query one episode with a predicate such as WHERE episode_index = 0.
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