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Datasets:
azithromycin
/
YOLO2431_put_pen_into_bag_V4.1

Tasks:
Robotics
Modalities:
Time-series
Tabular
Size:
100K<n<1M
Tags:
tsfile
timeseries
tabular
robotics
lerobot
manipulation
License:
Dataset card Files
xet
 Community
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.

Put Pen Into Bag V4.1 TsFile

This dataset is an Apache TsFile conversion of YOLO2431/put_pen_into_bag_V4.1, a LeRobot v2.1 yam_bimanual robot-manipulation dataset.

Source and provenance

  • Original dataset: YOLO2431/put_pen_into_bag_V4.1
  • Inspected source revision: 74df55d31492ff524fab30f0e2812d052dc4afcc
  • Original author/uploader: Sam F (YOLO2431)
  • License: Apache-2.0
  • Task (task_index = 0): Unzip the bag, pick up the pens from the table one at a time and place them inside, then zip the bag closed.
  • Split: train (0:201)
  • Sampling rate: 30 fps
  • Scale: 201 episodes, 190,948 frame rows, 1 task, 201 source Parquet shards
  • 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 (RGB) or .mkv (depth)

The source README contains placeholder paper/citation text and no paper URL; no paper is asserted for this converted artifact.

Converted files

  • TsFile: data/yolo2431_put_pen_into_bag_v4_1.tsfile (36,171,003 bytes; 34.50 MiB)
  • Table: yolo2431_put_pen_into_bag_v4_1
  • Rows: 190,948
  • Episodes/devices: 201
  • Time precision: milliseconds
  • Source metadata: meta/ is mirrored, and meta/info.json documents the TsFile mapping.

TsFile schema

Time is round(timestamp * 1000) as INT64 milliseconds. It restarts at zero for each episode. The source timestamp is dropped because it is exactly Time / 1000 seconds.

TAG/device columns:

  • episode_index
  • task_index

FIELD columns:

  • frame_index
  • sample_index
  • observation_state_0
  • observation_state_1
  • observation_state_2
  • observation_state_3
  • observation_state_4
  • observation_state_5
  • observation_state_6
  • observation_state_7
  • observation_state_8
  • observation_state_9
  • observation_state_10
  • observation_state_11
  • observation_state_12
  • observation_state_13
  • observation_state_14
  • observation_state_15
  • observation_state_16
  • observation_state_17
  • observation_state_18
  • observation_state_19
  • observation_state_20
  • observation_state_21
  • observation_state_22
  • observation_state_23
  • observation_state_24
  • observation_state_25
  • observation_state_26
  • observation_state_27
  • observation_state_28
  • observation_state_29
  • observation_state_30
  • observation_state_31
  • action_0
  • action_1
  • action_2
  • action_3
  • action_4
  • action_5
  • action_6
  • action_7
  • action_8
  • action_9
  • action_10
  • action_11
  • action_12
  • action_13
  • action_14
  • action_15
  • action_16
  • action_17
  • action_18
  • action_19
  • action_20
  • action_21
  • action_22
  • action_23
  • action_24
  • action_25
  • action_26
  • action_27
  • action_28
  • action_29
  • action_30
  • action_31

Vector flattening:

  • observation.state[32] -> observation_state_0 ... observation_state_31 (FLOAT)
  • action[32] -> action_0 ... action_31 (FLOAT)
  • index -> sample_index; frame_index is retained unchanged

Storage profile:

  • Time: TS_2DIFF + LZ4
  • FLOAT/DOUBLE: GORILLA + LZ4
  • INT32/INT64: TS_2DIFF + LZ4
  • BOOLEAN: RLE + LZ4 (the source has no BOOLEAN columns)
  • TAG: TsFile table-model device/tag mechanism

No source rows or numeric state/action dimensions are dropped. The three source RGB video features and three depth-video features are omitted from TsFile because they are media assets, not scalar time-series fields.

Videos (not included here)

Videos remain only in the original Hugging Face repository; they are not copied to or uploaded with this TsFile dataset. The source has 1,206 files total: 603 RGB MP4 files and 603 FFV1 depth MKV files (201 per stream), about 22,370,690,982 bytes in total.

Numeric rows remain frame-aligned with the original per-episode videos through episode_index and frame_index; depth streams use the same episode/frame identity and are stored remotely as .mkv.

Validation

Local validation status: PASS. A Java metadata scan confirmed the physical TS_2DIFF/GORILLA + LZ4 codecs, and every transformed value in all 190,948 rows was compared exactly against the staged Parquet. Source/staged/TsFile rows are 190,948 / 190,948 / 190,948; query readback returned 1,288 sample rows. The converted TsFile is 59.55% of the source Parquet byte total.

Minimal read example 

from tsfile import TsFileReader

reader = TsFileReader("data/yolo2431_put_pen_into_bag_v4_1.tsfile")
table_name = "yolo2431_put_pen_into_bag_v4_1"
columns = ["episode_index", "task_index", "Time", "frame_index", "action_0", "observation_state_0"]
with reader.query_table(table_name, columns, batch_size=4096) as result:
    batch = result.read_arrow_batch()
    print(batch.to_pandas().head())
reader.close()

Citation

Please cite the original Hugging Face dataset and its creator Sam F (YOLO2431). The source card does not provide a paper or BibTeX citation.
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