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.

SO100 PengripD TsFile

Apache TsFile edition of Ahi-Yu/so100_pengripD, a LeRobot v2.1 SO100 robot dataset for "Grasp a pengrip and put it in the cup." Numeric trajectories are stored in one table-model TsFile.

Source And Attribution

  • Original repository and author: shylee/so100_pengripD, owned by shylee
  • Source mirror used for the local snapshot: Ahi-Yu/so100_pengripD, published by Ahi-Yu
  • Attribution evidence: the mirror commit is titled Duplicate from shylee/so100_pengripD and names both Ahi-Yu and shylee as contributors.
  • License: Apache-2.0
  • Task: Grasp a pengrip and put it in the cup.
  • Split: train; 275 episodes, 58,050 frames, 1 task, sampled at 30 FPS, from 275 source Parquet shards.
  • Source episode indexes are non-contiguous: 0 through 174 and 200 through 299. This source-defined gap is preserved in the TAG values.
  • The source card does not provide a homepage, paper, or completed BibTeX citation.

Dataset Contents

The file data/ahi_yu_so100_pengripd.tsfile contains 58,050 rows across 275 episode/task devices. Time is round(timestamp * 1000) in milliseconds and restarts at zero for each episode. The source timestamp field is omitted because it is equivalent to Time / 1000 seconds.

Column Role Type Source mapping
Time TIME INT64 milliseconds round(timestamp * 1000)
episode_index, task_index TAG STRING Original INT64 identifiers
frame_index FIELD INT64 Preserved frame position
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]

Vector prefixes are preserved and dots are replaced with underscores. The six dimensions retain the source order: main_shoulder_pan, main_shoulder_lift, main_elbow_flex, main_wrist_flex, main_wrist_roll, main_gripper. No trajectory rows, episodes, tasks, action dimensions, or state dimensions are removed.

Source Videos

Videos are not included in this repository. They remain in the source mirror under videos/chunk-000. The three streams are observation.images.FrontCam, observation.images.TopCam, and observation.images.WristCam, with 275 MP4 files per stream (825 files total). The source template is videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4; use episode_index and frame_index to align each numeric row with the corresponding 30 FPS frames.

Read Example

from tsfile import TsFileReader

reader = TsFileReader("data/ahi_yu_so100_pengripd.tsfile")
with reader.query_table(
    "ahi_yu_so100_pengripd",
    ["episode_index", "task_index", "frame_index", "sample_index", "action_0", "observation_state_0"],
    batch_size=1024,
) as result:
    print(result.read_arrow_batch().to_pandas().head())
reader.close()
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