Datasets:

THUgewu
/

blue_pick_04

Tasks:

Robotics

Tags:

License:

Dataset card Files Files and versions

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 66, 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.

blue_Pick_04 TsFile

This dataset is a TsFile conversion of the Hugging Face dataset Team-Atom/blue_Pick_04, which was created using LeRobot.

Modalities: Time-series. The original dataset also includes video files; those videos are not included in this repository and remain available in the source dataset.

Source Dataset

Original dataset: Team-Atom/blue_Pick_04
License: Apache-2.0
Task category: robotics
Robot type: koch_follower
Split: single train split (0:100)
Scale: 100 episodes, 51,187 frames, 1 task, 1 data chunk
Sampling rate: 30 fps
Original video features: observation.images.web and observation.images.rs (480 x 640 RGB, AV1, 30 fps)

Converted Files

TsFile: data/blue_pick_04.tsfile
Rows: 51,187
Episodes: 100
Tasks: 1 (task_index = 0)
Table name: blue_pick_04
Time precision: milliseconds
Metadata: meta/ is mirrored from the source dataset, with meta/info.json rewritten to describe the TsFile artifact and conversion mapping.

Schema

Column	Role	Type	Notes
`Time`	TIME	INT64	`round(timestamp * 1000)`, in milliseconds; restarts per episode
`episode_index`	TAG	INT64	Source episode identifier
`task_index`	TAG	INT64	Source task identifier
`frame_index`	FIELD	INT64	Source frame index, preserved
`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]`

The action and observation-state element names are: shoulder_pan.pos, shoulder_lift.pos, elbow_flex.pos, wrist_flex.pos, wrist_roll.pos, and gripper.pos.

episode_index and task_index are TAG columns, so they form the TsFile device dimension. To read one episode, filter by episode_index.

Conversion Notes

The LeRobot frame Parquet files under data/chunk-000/ were merged into one TsFile for the train split.
Vector columns were flattened by preserving the source column name, replacing . with _, and appending the element index.
The source timestamp column is dropped because it is redundant with Time / 1000 seconds.
The source index column is renamed to sample_index.
Camera videos are not uploaded here. Use the original dataset videos: https://huggingface.co/datasets/Team-Atom/blue_Pick_04/tree/main/videos

Read Example

from tsfile import TsFileReader

path = "data/blue_pick_04.tsfile"
table = "blue_pick_04"

with TsFileReader(path) as reader:
    columns = [
        "episode_index",
        "task_index",
        "frame_index",
        "sample_index",
        "action_0",
        "observation_state_0",
    ]
    with reader.query_table(table, columns, batch_size=4096) as result:
        batch = result.read_arrow_batch()
        print(batch)