Datasets:

zjt24
/

new_gtor

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.

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new_GtoR TsFile

This dataset is a TsFile conversion of FeiYjf/new_GtoR, a LeRobot SO-100 robotics dataset. The source dataset card states that it was created using LeRobot.

Modalities: Time-series. The original camera videos are not uploaded here; they remain available in the source dataset under videos/.

Source Dataset

Source: FeiYjf/new_GtoR
License: apache-2.0
Source tags: LeRobot, tutorial
Codebase version: v2.0
Robot type: so100
Split: train 0:378
Scale: 378 episodes, 126,369 frames, 1 task, 1 data chunk, 756 videos
Sampling rate: 30 fps
Task: new_GtoR

Converted Files

TsFile path: data/new_gtor.tsfile
Table name: new_gtor
Row count: 126,369
Time precision: milliseconds
Metadata: meta/ is mirrored from the source dataset, with meta/info.json updated to describe the converted TsFile artifact and video policy.

Schema

Time is generated as round(timestamp * 1000) in milliseconds. Time restarts within each episode, and episode_index plus task_index identify the series.

TAG columns:

episode_index
task_index

FIELD columns:

frame_index
sample_index converted from the source index
action_0 to action_5
observation_state_0 to observation_state_5

The action and observation vectors use these source element names in order:

main_shoulder_pan
main_shoulder_lift
main_elbow_flex
main_wrist_flex
main_wrist_roll
main_gripper

Conversion Notes

All 378 train episodes are stored in a single TsFile using the TsFile table model, with episode_index and task_index as TAG columns.
The source timestamp column is not retained as a FIELD because it is the source for Time and equals Time / 1000 seconds after conversion.
The source index column is renamed to sample_index.
action[6] is flattened to scalar FLOAT fields action_0 to action_5.
observation.state[6] is flattened to scalar FLOAT fields observation_state_0 to observation_state_5.
Video features are omitted from this repository: observation.images.left and observation.images.right. Use the original dataset videos linked above for frame-aligned visual data.

Minimal Read Example

from tsfile import TsFileReader

path = "data/new_gtor.tsfile"
reader = TsFileReader(path)

columns = [
    "episode_index",
    "task_index",
    "frame_index",
    "sample_index",
    "action_0",
    "observation_state_0",
]

with reader.query_table("new_gtor", columns, batch_size=1024) as result:
    batch = result.read_arrow_batch()
    if batch is not None:
        df = batch.to_pandas()
        print(df.head())

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