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.

Whiteboard Inspect 0708 TsFile

Apache TsFile edition of Xense/whiteboard_inspect_0708, a LeRobot v3.0 bimanual Flexiv Rizon 4 robotics dataset.

Source and attribution

  • Original dataset: https://huggingface.co/datasets/Xense/whiteboard_inspect_0708
  • Publishing organization: XenseRobotics (Xense)
  • Repository contributor: fza (fza1796262052)
  • License: Apache-2.0
  • Task: Pick up the inspection probe, press it on each red-marked spot on the whiteboard one by one, put down the probe, pick up the eraser, and wipe off all the red marks.
  • Robot: bi_flexiv_rizon4_rt
  • Train split: 19 episodes, 67,684 frame rows, one task, 30 fps, 19 Parquet files
  • Source frame layout: data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet

The source dataset card does not provide a paper or BibTeX citation. Cite the original Hugging Face dataset, XenseRobotics, and contributor fza when using this data.

TsFile schema

Time = round(timestamp * 1000) as INT64 milliseconds and restarts at zero in each episode. The source timestamp column is removed because it is exactly represented by Time / 1000 seconds. index is renamed to sample_index, and frame_index is retained.

Columns TsFile type Role
Time INT64/TIMESTAMP TIME
episode_index, task_index STRING device segments TAG
frame_index, sample_index INT64 FIELD
action_0 ... action_19 FLOAT FIELD
observation_state_0 ... observation_state_19 FLOAT FIELD

The 20 action and 20 state elements follow the source feature order. Each arm contains TCP x/y/z, six rotation representation values, r1 through r6, in the source-defined order, and a gripper position value. The left-arm values precede the right-arm values. Dots in source vector names are represented by the scalar field prefix and element index. No numeric rows, action dimensions, or state dimensions are omitted.

Storage uses GORILLA + LZ4 for FLOAT/DOUBLE, TS_2DIFF + LZ4 for INT32/INT64, TS_2DIFF + LZ4 for Time, and RLE + LZ4 for BOOLEAN fields. TAG values use the TsFile table/device mechanism.

Original videos

The 133 source MP4 files remain at videos/ and are not included here. The path template is videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4 for these streams:

  • observation.images.head (640 x 480, H.264, 30 fps)
  • observation.images.left_wrist (640 x 480, H.264, 30 fps)
  • observation.images.right_wrist (640 x 480, H.264, 30 fps)
  • observation.images.left_tactile_0 (700 x 400, H.264, 30 fps)
  • observation.images.left_tactile_1 (700 x 400, H.264, 30 fps)
  • observation.images.right_tactile_0 (700 x 400, H.264, 30 fps)
  • observation.images.right_tactile_1 (700 x 400, H.264, 30 fps)

Use episode_index and frame_index to align numeric rows with the matching frame in each original per-episode video.

Read example

from tsfile import TsFileReader

reader = TsFileReader("data/xense_whiteboard_inspect_0708.tsfile")
with reader.query_table(
    "xense_whiteboard_inspect_0708",
    ["episode_index", "task_index", "Time", "frame_index", "action_0"],
    batch_size=4096,
) as result:
    print(result.read_arrow_batch().to_pandas().head())
reader.close()
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