Datasets:

jzpeng
/

Jeevesh2009_so101_gray_block_lowvar_test

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.

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SO101 Gray Block Lowvar Test TsFile

Apache TsFile edition of Jeevesh2009/so101_gray_block_lowvar_test, a LeRobot v2.1 SO101 dataset for picking up a gray block and placing it in a box. The numeric trajectories are stored in one table-model TsFile.

Source and attribution

Original author and repository owner: Madala Venkata Renu Jeevesh (Jeevesh2009).
License: Apache-2.0.
The source card does not provide a homepage, paper, or completed BibTeX citation.
Split: train; 100 episodes; 39,229 frame rows; one task; 30 FPS; 100 source Parquet shards.
Task 0: Pick the gray block and keep it in the box.

Data layout

The table is jeevesh2009_so101_gray_block_lowvar_test and contains 39,229 rows across 100 TAG devices. The source Parquet shards total 2,206,758 bytes; the TsFile is 759,135 bytes (34.4% of the source Parquet size).

Column	TsFile role	Type	Meaning
`Time`	TIME	INT64 milliseconds	`round(timestamp * 1000)`, restarting at zero per episode
`episode_index`	TAG	STRING from source INT64	Source episode identity
`task_index`	TAG	STRING from source INT64	Source task identity
`frame_index`	FIELD	INT64	Frame position within the episode
`sample_index`	FIELD	INT64	Source `index`, renamed for clarity
`action_0` ... `action_5`	FIELD	FLOAT	Flattened `action[6]`
`observation_state_0` ... `observation_state_5`	FIELD	FLOAT	Flattened `observation.state[6]`

timestamp is not retained as a separate FIELD because it is represented by Time / 1000 seconds. The vector column names preserve their source prefixes, with dots changed to underscores. No trajectory row, episode, task, action dimension, or state dimension is removed.

Videos and alignment

The 200 source AV1 videos are not included here. They remain under videos/chunk-000 in the original repository:

observation.images.side: 100 files
observation.images.top: 100 files

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 both 30 FPS video streams.

Read example

from tsfile import TsFileReader

reader = TsFileReader("data/jeevesh2009_so101_gray_block_lowvar_test.tsfile")
with reader.query_table(
    "jeevesh2009_so101_gray_block_lowvar_test",
    ["episode_index", "task_index", "frame_index", "sample_index", "action_0", "observation_state_0"],
    batch_size=1024,
) as result:
    batch = result.read_arrow_batch()
    print(batch.to_pandas().head())
reader.close()

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Total file size:

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