Datasets:

jzpeng
/

anvilbot-patrickhhh_SO101_record_test

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 71, 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 Record Test TsFile

Apache TsFile edition of anvilbot-patrickhhh/SO101_record_test, a LeRobot v2.1 SO101 dataset for putting a green cube in the area marked with black tape. The numeric trajectories are stored in one table-model TsFile.

Source and attribution

Original repository owner and uploader: anvilbot-patrickhhh. The source does not provide a personal author name.
License: Apache-2.0.
The source card does not provide a homepage, paper, or completed BibTeX citation.
Split: train; 100 episodes; 30,169 frame rows; one task; 30 FPS; 100 source Parquet shards.
Task 0: Put the green cube in the area with the black tape.

Data layout

The table is anvilbot_patrickhhh_so101_record_test and contains 30,169 rows across 100 TAG devices. The source Parquet shards total 1,663,535 bytes; the TsFile is 770,267 bytes (46.3% of the source Parquet size).

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

timestamp is not retained as a separate FIELD because it is represented by Time / 1000 seconds. The vector column names preserve their source prefixes, with dots changed to underscores. No trajectory row, episode, task, action dimension, or state dimension is removed.

Videos and alignment

The 100 source AV1 videos are not included here. They remain under videos/chunk-000 in the original repository:

observation.images.front: 100 per-episode MP4 files, 1280x720 at 30 FPS. Hugging Face displays the directory size as about 587 MB.

The source 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 the 30 FPS video stream.

Read example

from tsfile import TsFileReader

reader = TsFileReader("data/anvilbot_patrickhhh_so101_record_test.tsfile")
with reader.query_table(
    "anvilbot_patrickhhh_so101_record_test",
    ["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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