Datasets:

azithromycin
/

slowturtle99_so100_pick_place_blocks

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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SO100 Pick and Place Blocks TsFile

This Apache TsFile dataset is derived from slowturtle99/so100_pick_place_blocks, a LeRobot v2.1 SO100 robot-manipulation dataset. It contains numeric robot trajectories, frame timing, and episode/task tags. Camera videos remain in the source repository.

Source Dataset and Attribution

Source dataset: slowturtle99/so100_pick_place_blocks
Original author/uploader: slowturtle99
Authorship evidence: the repository shows slowturtle99 as its owner, uploader, and sole contributor. The source card gives no separate author list.
License: Apache-2.0
Task: "Pick blocks and place them in the box."
Robot: so100; LeRobot version: v2.1
Split: train; sampling rate: 30 fps
Scale: 50 episodes, 35,084 frames, 1 task
Source layout: 50 Parquet shards at data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet

Data Layout

Path: data/slowturtle99_so100_pick_place_blocks.tsfile (832,093 bytes)
Table: slowturtle99_so100_pick_place_blocks
Rows: 35,084; episodes/devices: 50; tasks: 1
Time precision: milliseconds
TsFile/source-Parquet size ratio: 0.413
Source metadata remains under meta/; no source Parquet file is moved into that directory.

Schema

Time = round(timestamp * 1000) milliseconds. Time restarts at zero in every episode. Source timestamp is omitted afterward because it equals Time / 1000 seconds.

TAG columns, stored through the TsFile table/device mechanism:

episode_index
task_index

Scalar FIELD columns:

frame_index
sample_index (renamed from source index)

Flattened FLOAT FIELD groups:

action[6] -> action_0 ... action_5
observation.state[6] -> observation_state_0 ... observation_state_5

The six dimensions are main_shoulder_pan, main_shoulder_lift, main_elbow_flex, main_wrist_flex, main_wrist_roll, and main_gripper. Dots in source vector names become underscores. No numeric row, episode, task, or vector dimension is dropped.

Encodings and Compression

FLOAT/DOUBLE: GORILLA + LZ4
INT32/INT64: TS_2DIFF + LZ4
Time: TS_2DIFF + LZ4
BOOLEAN: RLE + LZ4 (the source has no BOOLEAN field)
TAG: TsFile table/device storage

The physical table schema, Time codec, every FIELD codec, and all 35,084 rows were checked with the Apache TsFile Java API.

Videos

Videos are not included in this TsFile repository. The source has 100 frame-aligned AV1 MP4 files (467,621,518 bytes, about 446.0 MiB), 640x480, 30 fps, no audio, in two streams:

Each stream uses videos/chunk-000/{video_key}/episode_{episode_index:06d}.mp4. episode_index, frame_index, and meta/episodes.jsonl preserve alignment.

Usage

from tsfile import TsFileReader

reader = TsFileReader("data/slowturtle99_so100_pick_place_blocks.tsfile")
with reader.query_table(
    "slowturtle99_so100_pick_place_blocks",
    ["episode_index", "task_index", "frame_index", "sample_index",
     "action_0", "observation_state_0"],
    batch_size=65536,
) as result:
    batch = result.read_arrow_batch()
    if batch is not None:
        print(batch.to_pandas().head())
reader.close()

Source Notes

The source card states that the dataset was created with LeRobot. It does not provide a paper or completed BibTeX citation.

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