Datasets:

jzpeng
/

unitreerobotics_G1_Dex3_ObjectPlacement_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.

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G1 Dex3 Object Placement Dataset TsFile

Apache TsFile conversion of the Unitree Robotics G1 Dex3 Object Placement Dataset, a LeRobot v3.0 dataset for picking up toothpaste and a trash bag and placing them in a blue storage container.

Modalities: Time-series, Tabular

Source and attribution

Original dataset: unitreerobotics/G1_Dex3_ObjectPlacement_Dataset
Original uploader/author shown in the repository history: Henry-Ellis
Repository owner: Unitree Robotics
License: Apache-2.0; the source card does not provide a paper or formal citation.
Robot: Unitree_G1, dual-arm 7-DOF robot with three-finger dexterous hands
Sampling: 30 Hz; 210 episodes, 98,266 frames, one task

Converted artifact

The train split is stored in one table-model TsFile:

data/g1_dex3_objectplacement_dataset_train.tsfile

Table name: g1_dex3_objectplacement_dataset_train; 98,266 rows and 210 devices (one device per episode_index/task_index combination). Time is round(timestamp * 1000) in milliseconds and restarts at each episode. The source timestamp is dropped because it is redundant; index is renamed to sample_index.

TAG columns are episode_index and task_index. The vector fields are flattened as follows:

observation.state -> observation_state_0 ... observation_state_27
action -> action_0 ... action_27

Other scalar fields are frame_index and sample_index.

TsFile encoding

Data category	Encoding	Compression
FLOAT / DOUBLE	GORILLA	LZ4
INT32 / INT64	TS_2DIFF	LZ4
Time	TS_2DIFF	LZ4
BOOLEAN	RLE	LZ4
TAG	TsFile table/device TAG mechanism	TsFile schema metadata

The encoded file is 18,234,639 bytes (the previous PLAIN/UNCOMPRESSED file was 25,547,130 bytes). A Java readback check confirmed completeness, row count, TAG roles, and all listed codecs.

Videos

Video files are intentionally not copied to this converted repository. They remain in the original dataset under videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4. The four source streams are:

The source revision contains nine physical MP4 shards and 840 logical episode videos. Numeric rows remain frame-aligned through episode_index and frame_index; source meta/episodes/ metadata is mirrored.

Reading

from tsfile import TsFileReader

reader = TsFileReader("data/g1_dex3_objectplacement_dataset_train.tsfile")
with reader.query_table(
    "g1_dex3_objectplacement_dataset_train",
    ["episode_index", "task_index", "Time", "frame_index", "action_0"],
    batch_size=65536,
) as result:
    print(result.read_arrow_batch().to_pandas().head())

Downloads last month: 66

Total file size:

26.1 MB