Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
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.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

SO100 Teleop Record 3 TsFile

Apache TsFile edition of NeilKim/so100-teleop-record-3, a LeRobot v2.1 SO100 teleoperation dataset for the task "Grab the box." The numeric trajectories are stored in one table-model TsFile.

Source and attribution

  • Original author, repository owner, and uploader: Neil Kim (NeilKim).
  • License: Apache-2.0.
  • The source card does not provide a homepage, paper, or completed BibTeX citation.
  • Split: train; 51 episodes; 26,704 frame rows; one task; 30 FPS; 51 source Parquet shards.
  • The source meta/info.json declares 27,314 frames and 104 videos, but the current 51 Parquet files and meta/episodes.jsonl contain 26,704 rows, while the repository has 102 MP4 files. Current files are authoritative here.
Task index Task Episodes Rows
0 Grab the box 51 26,704

Data layout

The table is neilkim_so100_teleop_record_3 and contains 26,704 rows across 51 TAG devices. The source Parquet shards total 1,301,122 bytes; the TsFile is 516,823 bytes (39.7% 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. Source index values are preserved as sample_index, including their existing gaps. No trajectory row, episode, task, action dimension, or state dimension is removed.

The data/ directory contains only neilkim_so100_teleop_record_3.tsfile. Source metadata remains under meta/; source trajectory Parquet files are not placed in meta/.

Videos and alignment

The 102 source AV1 videos are not included here. They remain under videos/chunk-000 in the original repository:

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 both 30 FPS video streams.

Read example

from tsfile import TsFileReader

reader = TsFileReader("data/neilkim_so100_teleop_record_3.tsfile")
with reader.query_table(
    "neilkim_so100_teleop_record_3",
    ["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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