Datasets:

azithromycin
/

memmelma_MnMs_to_skittles_optimal

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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MnMs to Skittles Optimal TsFile

This dataset is an Apache TsFile conversion of memmelma/MnMs_to_skittles_optimal, a LeRobot v2.1 YAM bimanual manipulation dataset. The task is: Pour the M&Ms in the bowl into the bowl of skittles.

Source Dataset and Attribution

Original dataset: memmelma/MnMs_to_skittles_optimal
Pinned source revision: 68e0cc0799176fcd441d98503470d0ec85c313d5
Original repository creator and uploader: memmelma
License: Apache-2.0
Robot type: yam_bimanual
LeRobot codebase version: v2.1
Split: train
Sampling rate: 30 fps
Scale: 48 episodes, 19,331 frame rows, 1 task, 48 source Parquet files
Source frame layout: data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet
Source video layout: videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4

The source card does not provide a paper, completed citation, homepage, or personal author name. Attribution therefore uses the Hugging Face repository owner memmelma without inventing additional identity information.

Converted Files

TsFile: data/memmelma_mnms_to_skittles_optimal.tsfile
Table: memmelma_mnms_to_skittles_optimal
Rows: 19,331
Episodes/devices: 48
TsFile size: 1.32 MiB
Time precision: milliseconds
Source split/shards: one train split, 48 per-episode Parquet shards
Converted sharding: one merged TsFile

TsFile Schema

Column/group	TsFile role	Type	Description
`Time`	TIME	INT64	`round(timestamp * 1000)` ms; restarts per episode
`episode_index`	TAG	STRING	Source INT64 episode identifier stored as a device/TAG segment
`task_index`	TAG	STRING	Source INT64 task identifier stored as a device/TAG segment
`frame_index`	FIELD	INT64	Frame number within the episode
`sample_index`	FIELD	INT64	Source `index` renamed to avoid ambiguity
`observation_state_0..13`	FIELD	FLOAT	Flattened `observation.state[14]`
`action_0..13`	FIELD	FLOAT	Flattened `action[14]`

The 14 observation and action dimensions follow the source order: left joints 0-5, left gripper, right joints 0-5, right gripper.

Conversion and Encoding

All 19,331 numeric rows are preserved.
timestamp is dropped after Time synthesis because it is redundant with Time / 1000 seconds.
index is retained as sample_index; frame_index is unchanged.
Video features are omitted from TsFile because they are external MP4 streams.
FLOAT/DOUBLE fields use GORILLA + LZ4.
INT32/INT64 fields and Time use TS_2DIFF + LZ4.
BOOLEAN fields use RLE + LZ4 when present; this dataset has none.
TAG values use the TsFile table device/TAG mechanism.
The final TsFile/source-Parquet size ratio is 0.5582.

Videos

Videos are not included in this converted repository. The original dataset has three frame-aligned streams and 144 MP4 files in total:

observation.images.head - 48 per-episode MP4 files, AV1, 320x240, 30 fps
observation.images.left_wrist - 48 per-episode MP4 files, AV1, 320x240, 30 fps
observation.images.right_wrist - 48 per-episode MP4 files, AV1, 320x240, 30 fps

Rows remain aligned to the original videos through episode_index and frame_index.

Minimal Read Example

from tsfile import TsFileReader

path = "data/memmelma_mnms_to_skittles_optimal.tsfile"
reader = TsFileReader(path)
columns = [
    "episode_index",
    "task_index",
    "frame_index",
    "sample_index",
    "observation_state_0",
    "action_0",
]
with reader.query_table("memmelma_mnms_to_skittles_optimal", columns, batch_size=65536) as result:
    batch = result.read_arrow_batch()
    print(batch.to_pandas().head())
reader.close()

Citation

The source dataset card contains no completed citation. Cite the original Hugging Face dataset and repository owner memmelma when using this converted artifact.

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