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 68, 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.

G1 06 11 TsFile

Apache TsFile edition of Jiangeng/G1_06_11, a LeRobot v2.1 Unitree G1 whole-body dataset for the task: Put the teddy bear in the bag. Numeric trajectories are stored in one table-model TsFile.

Source and attribution

  • Original repository owner, publisher, uploader, and sole listed contributor: Jiangeng. No separate personal name is provided.
  • License: the source repository does not declare one.
  • Paper, homepage, and citation: the source repository does not provide them.
  • Split: train; 51 episodes; 79,948 frame rows; one task; 50 Hz; 51 source Parquet episode shards.
  • Task 0: Put the teddy bear in the bag.

Data layout

The table is jiangeng_g1_06_11 and contains 79,948 rows across 51 TAG devices. The source Parquet shards total 67,782,033 bytes; the TsFile is 31,817,362 bytes (46.9% of the source Parquet size).

Column or group TsFile role Type Meaning
Time TIME INT64 milliseconds round(timestamp * 1000), restarting at zero for each episode
episode_index TAG STRING from source INT64 Source episode identity, values 0 through 50
task_index TAG STRING from source INT64 Source task identity, value 0
frame_index FIELD INT64 Frame position within the episode
sample_index FIELD INT64 Source index, renamed for clarity
teleop_delta_heading FIELD DOUBLE Source heading delta
teleop_smpl_frame_index FIELD INT64 Source SMPL frame index
teleop_stream_mode, teleop_planner_mode FIELD INT32 Teleoperation and locomotion planner modes
teleop_planner_speed, teleop_planner_height FIELD FLOAT Planner speed and height
observation_state_0 ... observation_state_42 FIELD FLOAT G1 joint state; 43 elements
observation_eef_state_0 ... observation_eef_state_13 FIELD FLOAT left/right wrist position and quaternion state; 14 elements
action_wbc_0 ... action_wbc_42 FIELD FLOAT whole-body controller action; 43 elements
observation_root_orientation_0 ... observation_root_orientation_3 FIELD FLOAT flattened source observation.root_orientation vector; 4 elements
observation_projected_gravity_0 ... observation_projected_gravity_2 FIELD FLOAT flattened source observation.projected_gravity vector; 3 elements
observation_cpp_rotation_offset_0 ... observation_cpp_rotation_offset_3 FIELD FLOAT flattened source observation.cpp_rotation_offset vector; 4 elements
observation_init_base_quat_0 ... observation_init_base_quat_3 FIELD FLOAT flattened source observation.init_base_quat vector; 4 elements
action_motion_token_0 ... action_motion_token_63 FIELD FLOAT motion token; 64 elements
teleop_smpl_joints_0 ... teleop_smpl_joints_71 FIELD FLOAT SMPL joint values; 72 elements
teleop_smpl_pose_0 ... teleop_smpl_pose_62 FIELD FLOAT SMPL pose values; 63 elements
teleop_body_quat_w_0 ... teleop_body_quat_w_3 FIELD FLOAT flattened source teleop.body_quat_w vector; 4 elements
teleop_target_body_orientation_0 ... teleop_target_body_orientation_5 FIELD FLOAT flattened source teleop.target_body_orientation vector; 6 elements
teleop_left_hand_joints_0 ... teleop_left_hand_joints_6 FIELD FLOAT flattened source teleop.left_hand_joints vector; 7 elements
teleop_right_hand_joints_0 ... teleop_right_hand_joints_6 FIELD FLOAT flattened source teleop.right_hand_joints vector; 7 elements
teleop_left_wrist_joints_0 ... teleop_left_wrist_joints_2 FIELD FLOAT flattened source teleop.left_wrist_joints vector; 3 elements
teleop_right_wrist_joints_0 ... teleop_right_wrist_joints_2 FIELD FLOAT flattened source teleop.right_wrist_joints vector; 3 elements
teleop_planner_movement_0 ... teleop_planner_movement_2 FIELD FLOAT flattened source teleop.planner_movement vector; 3 elements
teleop_planner_facing_0 ... teleop_planner_facing_2 FIELD FLOAT flattened source teleop.planner_facing vector; 3 elements
teleop_vr_3pt_position_0 ... teleop_vr_3pt_position_8 FIELD FLOAT flattened source teleop.vr_3pt_position vector; 9 elements
teleop_vr_3pt_orientation_0 ... teleop_vr_3pt_orientation_17 FIELD FLOAT three-point VR orientation; 18 elements

The source timestamp column is not stored separately because it is represented by Time / 1000 seconds. All 79,948 numeric rows, every episode and task identity, frame_index, and every numeric vector element are retained. Source vector prefixes are preserved with dots replaced by underscores.

Encoding and compression

  • TIME, INT32, and INT64: TS_2DIFF + LZ4
  • FLOAT and DOUBLE: GORILLA + LZ4
  • BOOLEAN, when present: RLE + LZ4
  • TAG values: TsFile table-model device/tag storage

Videos and alignment

The 51 source ego-view MP4 files are not included here. They remain in the original repository at videos/chunk-000/observation.images.ego_view, named episode_000000.mp4 through episode_000050.mp4. The source directory is displayed as 257 MB on Hugging Face.

The source template is videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4. Use episode_index and frame_index to align a numeric row with its 50 FPS video frame.

Read example

from tsfile import TsFileReader

reader = TsFileReader("data/jiangeng_g1_06_11.tsfile")
with reader.query_table(
    "jiangeng_g1_06_11",
    [
        "episode_index",
        "task_index",
        "frame_index",
        "sample_index",
        "observation_state_0",
        "action_wbc_0",
    ],
    batch_size=1024,
) as result:
    batch = result.read_arrow_batch()
    print(batch.to_pandas().head())
reader.close()
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