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.

Task 1 Dataset All Trimmed (TsFile)

This is a time-series conversion of ETHrobotlearning/task1dataset_all_trimmed, a LeRobot v3.0 dataset containing banana-placement demonstrations from a so_follower robot.

  • Modalities: Time-series
  • Robot: so_follower
  • Tasks: place the banana in the blue, red, or green colored bowl
  • Source license: not declared

Dataset scale

Split Episodes Frames / TsFile rows Sampling rate Tasks Source Parquets Final TsFiles
train 120 19,095 10 fps 3 20 1

Each task has 40 episodes. The blue-bowl task contains 6,326 frames, the red-bowl task 6,384 frames, and the green-bowl task 6,385 frames.

All numeric frame data is merged into exactly one file, data/task1dataset_all_trimmed.tsfile, containing the table task1dataset_all_trimmed.

TsFile schema

Column TsFile role Type Description
Time TIME INT64 Milliseconds, computed as Time = round(timestamp * 1000); time restarts within each episode.
episode_index TAG INT64 Original episode identifier (0 through 119).
task_index TAG INT64 Original task identifier: 0 blue bowl, 1 red bowl, and 2 green bowl.
frame_index FIELD INT64 Original frame position within the episode.
sample_index FIELD INT64 Original global index, renamed to avoid ambiguity.
observation_state_0 ... observation_state_5 FIELD FLOAT (float32) Complete six-element source observation.state vector: shoulder pan, shoulder lift, elbow flex, wrist flex, wrist roll, and gripper position.
action_0 ... action_5 FIELD FLOAT (float32) Complete six-element source action vector with the same actuator ordering.

The source timestamp column is dropped after generating Time because it is the redundant seconds representation of the same time coordinate (Time / 1000). No rows or other numeric source columns are dropped. Both vectors are flattened without truncation, index is renamed, and the episode, task, and frame identifiers remain available.

Videos and metadata

The source contains one 480 x 640 RGB AV1 stream, observation.images.front, stored in 15 MP4 files. Video files are intentionally omitted from this time-series repository and remain in the original dataset's videos/ tree.

Numeric rows retain episode_index, frame_index, and the 10 fps time coordinate, so they remain aligned with the corresponding source video frames. The source meta/ files are mirrored, except that meta/info.json is rewritten to describe the single TsFile path, TIME/TAG/FIELD schema, flattened and renamed features, omitted video feature, and source-video alignment. No videos/ directory is included here.

Reading the data

Install the Apache TsFile Python package, then query the actual table. query_table returns the time column automatically in addition to the requested TAG and FIELD columns.

from tsfile import TsFileReader

reader = TsFileReader("data/task1dataset_all_trimmed.tsfile")
columns = [
    "episode_index",
    "task_index",
    "frame_index",
    "sample_index",
    "observation_state_0",
    "action_0",
]

with reader.query_table(
    "task1dataset_all_trimmed", columns, batch_size=65536
) as result:
    batch = result.read_arrow_batch()
    if batch is not None:
        print(batch.to_pandas().head())

Source and citation

The source dataset is published under the ETHrobotlearning Hugging Face organization. Its repository does not include a dataset card, license declaration, paper, or BibTeX citation; consult the source repository for future metadata updates.

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