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.

Pick Pink 1 TsFile

This Apache TsFile edition comes from DanqingZ/pick_pink_1, a LeRobot v2.1 SO100 robot-manipulation dataset. It contains numeric robot trajectories, frame timing, and episode/task tags. Camera videos remain in the original repository.

Source Dataset and Attribution

  • Original dataset: DanqingZ/pick_pink_1
  • Original author/uploader: DanqingZ
  • Authorship evidence: the repository is owned by DanqingZ. The source card gives no separate author list.
  • License: Apache-2.0
  • Task: "Grasp the pink cuboid and put it in the bin."
  • Robot: so100; LeRobot version: v2.1
  • Split: train; sampling rate: 30 fps
  • Scale: 50 episodes, 29,725 frames, 1 task
  • Source layout: 50 Parquet shards at data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet

TsFile Data

  • Path: data/danqingz_pick_pink_1.tsfile (333,671 bytes)
  • Table: danqingz_pick_pink_1
  • Rows: 29,725; episodes/devices: 50; tasks: 1
  • Time precision: milliseconds
  • TsFile/source-Parquet size ratio: 0.284
  • Source metadata remains under meta/, with meta/info.json describing the TsFile layout.

Schema

Time = round(timestamp * 1000) milliseconds. Time restarts at zero in every episode. Source timestamp is omitted afterward because it equals Time / 1000 seconds.

TAG columns, stored through the TsFile table/device mechanism:

  • episode_index
  • task_index

Scalar FIELD columns:

  • frame_index
  • sample_index (renamed from source index)

Flattened FLOAT FIELD groups:

  • action[6] -> action_0 ... action_5
  • observation.state[6] -> observation_state_0 ... observation_state_5

The six 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 become underscores. No numeric row, episode, task, or vector dimension is dropped.

Encodings and Compression

  • FLOAT/DOUBLE: GORILLA + LZ4
  • INT32/INT64: TS_2DIFF + LZ4
  • Time: TS_2DIFF + LZ4
  • BOOLEAN: RLE + LZ4 (the source has no BOOLEAN field)
  • TAG: TsFile table/device storage

The physical table schema, Time codec, every FIELD codec, and all 29,725 rows were read back with the Apache TsFile Java API.

Videos

Videos are not included in this TsFile repository. The source has 150 frame-aligned AV1 MP4 files (731,810,599 bytes, about 697.9 MiB), 640x480, 30 fps, no audio, in three streams:

Each stream uses 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/danqingz_pick_pink_1.tsfile")
with reader.query_table(
    "danqingz_pick_pink_1",
    ["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()

Source Notes

The source card states that the dataset was created with LeRobot. It does not provide a paper or a formal citation.

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