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 PnP Block TsFile

This Apache TsFile dataset is derived from samsitol/so100_PnPblock, a LeRobot v2.1 SO100 robot-manipulation dataset.

Source Dataset

  • Original dataset: samsitol/so100_PnPblock
  • Author, repository owner, and sole contributor: Sam (samsitol)
  • License: Apache-2.0
  • Robot type: so100
  • LeRobot codebase version: v2.1
  • Task: Grasp the yellow block and put it in the fixed green bin.
  • Split: train
  • Scale: 50 episodes, 32,709 frames, 1 task, 30 fps
  • Source frame files: 50 Parquet files
  • Source frame layout: data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet
  • The source card does not provide a paper or completed citation.

TsFile Data

  • Path: data/samsitol_so100_pnpblock.tsfile
  • Table: samsitol_so100_pnpblock
  • Rows: 32,709
  • Time precision: milliseconds
  • Episodes are represented by TsFile TAG values rather than separate files.

Schema

Time is round(timestamp * 1000) milliseconds and restarts within each episode.

Column TsFile role Type Source mapping
Time TIME INT64 round(timestamp * 1000)
episode_index TAG STRING device/tag value Source episode_index
task_index TAG STRING device/tag value Source task_index
frame_index FIELD INT64 Preserved
sample_index FIELD INT64 Renamed from source index
action_0 ... action_5 FIELD FLOAT Flattened from action[6]
observation_state_0 ... observation_state_5 FIELD FLOAT Flattened from observation.state[6]

The six action and state dimensions are main_shoulder_pan, main_shoulder_lift, main_elbow_flex, main_wrist_flex, main_wrist_roll, and main_gripper.

Conversion Notes

  • All 50 episode Parquet files are merged into one table-model TsFile. Filter by episode_index and task_index to select a device/episode.
  • Vector columns are flattened to scalar fields. Full source prefixes are retained, with . replaced by _.
  • The source timestamp column is omitted after creating Time because the same value is recoverable as Time / 1000 seconds.
  • No numeric rows, episodes, action dimensions, or state dimensions are omitted.
  • Numeric columns use the requested compact TsFile codecs with LZ4, while TAG values use the TsFile table/device mechanism. This source has no BOOLEAN field.

Videos

Videos are not included here. They remain in the original repository under videos/chunk-000:

Each stream has one MP4 per episode, for 150 source videos total. Numeric rows remain frame-aligned through episode_index and frame_index.

Usage

from tsfile import TsFileReader

reader = TsFileReader("data/samsitol_so100_pnpblock.tsfile")
table_name = "samsitol_so100_pnpblock"
columns = [
    "episode_index",
    "task_index",
    "frame_index",
    "sample_index",
    "action_0",
    "observation_state_0",
]

with reader.query_table(table_name, columns, batch_size=65536) as result:
    batch = result.read_arrow_batch()
    print(batch.to_pandas().head())
Downloads last month
45