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 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.

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.

Egg 0806 TsFile

This dataset is an Apache TsFile conversion of Selinaliu1030/egg_0806, a LeRobot v2.1 SO101 robot-manipulation dataset. It contains numeric trajectories, timing, episode/task tags, and source metadata. Videos remain in the original Hugging Face repository.

Source Dataset and Attribution

  • Original dataset: Selinaliu1030/egg_0806
  • Original author/publisher: Selinaliu1030
  • Authorship note: the source card names no separate authors; this README attributes the dataset to its Hugging Face repository publisher.
  • License: Apache-2.0
  • Task: "Grasp the egg and put it in the red bin."
  • Robot: so101; LeRobot version: v2.1
  • Split: train; sampling rate: 30 fps
  • Scale: 90 episodes, 49,957 frames, 1 task
  • Source shards: 90 Parquet files under data/chunk-000/episode_{episode_index:06d}.parquet
  • Paper/citation: the source card provides neither a paper nor a completed citation.

Schema

TsFile column(s) Type Role Source
Time INT64 TIME round(timestamp * 1000) ms
episode_index STRING TAG/device source episode_index
task_index STRING TAG/device source task_index
frame_index INT64 FIELD source frame_index
sample_index INT64 FIELD source index
action_0 ... action_5 FLOAT FIELD flattened action[6]
observation_state_0 ... observation_state_5 FLOAT FIELD flattened observation.state[6]

The six action/state dimensions are main_shoulder_pan, main_shoulder_lift, main_elbow_flex, main_wrist_flex, main_wrist_roll, and main_gripper. Dots in source vector names are replaced by underscores. Time restarts at zero for every episode.

Conversion Details

  • The 90 train episode shards are merged into one table-model TsFile.
  • Source timestamp is dropped after Time synthesis because it is exactly represented by Time / 1000 seconds.
  • Source index is retained as sample_index; frame_index is unchanged.
  • No numeric row, episode, task, state dimension, or action dimension is dropped.
  • FLOAT/DOUBLE uses GORILLA + LZ4; INT32/INT64 and Time use TS_2DIFF + LZ4; BOOLEAN uses RLE + LZ4; TAG values use the TsFile table/device mechanism.

Videos

Videos are not included in this TsFile repository. The original dataset has 180 frame-aligned AV1 MP4 files (301,079,898 bytes, about 287.1 MiB), 640x480, 30 fps, no audio, in two streams:

Each stream contains 90 files matching videos/chunk-000/{video_key}/episode_{episode_index:06d}.mp4. episode_index, frame_index, and meta/episodes.jsonl preserve alignment.

Usage

from tsfile import TsFileReader

reader = TsFileReader("data/selinaliu1030_egg_0806.tsfile")
with reader.query_table(
    "selinaliu1030_egg_0806",
    ["episode_index", "task_index", "frame_index", "sample_index",
     "action_0", "observation_state_0"],
    batch_size=65536,
) as result:
    print(result.read_arrow_batch().to_pandas().head())
reader.close()
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