Datasets:

XNX25
/

jonhpark_so101_pick_place

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "tsfile/tsfile_py_cpp.pyx", line 567, in tsfile.tsfile_py_cpp.tsfile_reader_new_c
              tsfile.exceptions.FileOpenError: 28: 
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 271, in _split_generators
                  scan = self._scan_metadata(all_files)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 318, in _scan_metadata
                  with self._open_reader(file) as reader:
                       ~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 742, in _open_reader
                  return TsFileReader(file)
                File "tsfile/tsfile_reader.pyx", line 323, in tsfile.tsfile_reader.TsFileReaderPy.__init__
              SystemError: <class '_weakrefset.WeakSet'> returned a result with an exception set
              
              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/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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SO101 Pick Place TsFile

Apache TsFile edition of jonhpark/so101_pick_place, a LeRobot v2.1 SO101 manipulation dataset. The numeric trajectory table contains robot actions, states, timing, frame positions, and source episode/task identifiers.

Source and attribution

Original repository owner: jonhpark
License: Apache-2.0
Robot type: so101; LeRobot codebase version: v2.1
Split: train; 50 episodes; 29,792 frame rows; one task; 30 FPS; one data chunk
Task 0: Grasp a skyblue doll and put it in the white bowl.
The source card does not provide a homepage, paper, or completed BibTeX citation.

Data layout

The table is jonhpark_so101_pick_place with 29,792 rows and 50 episode/task devices. Source frame Parquet follows data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet; the TsFile path is data/jonhpark_so101_pick_place.tsfile.

Column	Role	Type	Meaning
`Time`	TIME	INT64 milliseconds	`round(timestamp * 1000)`, restarting at zero for each episode
`episode_index`	TAG	STRING	Source episode identifier
`task_index`	TAG	STRING	Source task identifier
`frame_index`	FIELD	INT64	Frame position within the episode
`sample_index`	FIELD	INT64	Source `index`, renamed for the scalar table
`action_0` ... `action_5`	FIELD	FLOAT	Flattened source `action[6]`
`observation_state_0` ... `observation_state_5`	FIELD	FLOAT	Flattened source `observation.state[6]`

The six action dimensions are main_shoulder_pan, main_shoulder_lift, main_elbow_flex, main_wrist_flex, main_wrist_roll, and main_gripper; the state vector uses the same order. The source timestamp is omitted because it is represented by Time / 1000 seconds. No trajectory rows, episodes, tasks, action dimensions, or state dimensions are removed.

Videos and alignment

Camera data is not included in this TsFile repository. The original Hugging Face dataset keeps 100 MP4 streams under:

The source path template is videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4. Use episode_index and frame_index to align each numeric row with its front or top 30 FPS video.

Read example

from tsfile import TsFileReader

reader = TsFileReader("data/jonhpark_so101_pick_place.tsfile")
with reader.query_table(
    "jonhpark_so101_pick_place",
    ["episode_index", "task_index", "frame_index", "sample_index", "action_0", "observation_state_0"],
    batch_size=1024,
) as result:
    batch = result.read_arrow_batch()
    print(batch.to_pandas().head())
reader.close()

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Total file size:

518 kB