The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 291, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
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/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 306, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
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.
Real-world robot teleoperation tasks / 真实机器人遥操作任务
Raw GELLO teleoperation recordings from a Franka FR3 with a Robotiq 2F-85 gripper. Recordings include joint-space actions, measured robot state, and exterior/wrist camera videos at a nominal 15 Hz.
Tasks / 任务
| Task / 任务 | Directory | Episodes | Samples | Duration | Raw size |
|---|---|---|---|---|---|
| 把番茄放进盒子 | place_tomato_in_box | 47 | 19,257 | 21.37 min | 162.56 MB |
| 把绿色方块叠到黄色方块上 | stack_green_cube_on_yellow_cube | 117 | 45,040 | 49.98 min | 349.25 MB |
| 把黄色花朵插入花瓶 | place_yellow_flower_in_vase | 30 | 13,094 | 14.54 min | 118.01 MB |
Total: 194 episodes, 77,391 samples, 85.88 minutes, 629.82 MB of original recordings (1,358 files). Collection dates: 2026-10-27 through 2026-11-02 (UTC).
The directories were renamed from box_and_tomato, green_yellow_cube, and yellow_flower to describe the intended task. The original episode IDs and all recording files are preserved byte-for-byte. Task names were inferred from sampled videos; no per-episode success labels were added.
Layout
<task_id>/
README.md
task.json
episodes.csv
<episode_id>/
metadata.json
samples.jsonl
trajectory.npz
videos/
exterior.mp4
exterior.frames.jsonl
wrist.mp4
wrist.frames.jsonl
episodes.csv indexes recordings by ID, timestamp, sample count, duration, recording status, and video status. manifest.json lists file sizes, SHA-256 digests, and Git blob hashes for upload verification; the manifest itself is excluded from its file list.
Recording format
This repository preserves the project's raw recording format; it is not a converted LeRobot dataset. trajectory.npz can be opened with numpy.load(path, allow_pickle=False).
| Array | Meaning |
|---|---|
action (T × 8) |
Seven absolute joint targets (radians), followed by gripper closedness |
proprioception (T × 8) |
Seven measured joint positions (radians), followed by measured gripper closedness |
joint_velocities (T × 7) |
Measured joint velocities |
eef_pose (T × 7) |
XYZ position in metres and WXYZ quaternion of the configured Franka end effector |
gello_input (T × 8) |
GELLO input before velocity limiting, including gripper closedness |
time_seconds |
Monotonic time relative to the episode start |
phase |
recording or stopping |
action_valid, proprioception_valid, gello_input_valid |
Masks identifying valid values; missing values may use zero placeholders |
command_submitted, command_id, submission_status |
Command submission tracking |
Gripper convention: 0 = open, 1 = closed. Optional missing timestamps may be NaN. samples.jsonl also retains the submitted short trajectories and gripper events. metadata.json contains hardware configuration, recording statistics, and the final observation.
Video frames and numerical samples share a monotonic start time but are not index-synchronized. Align them using time_seconds and the frame JSONL records, which also contain source timestamps and duplicate/placeholder/gap flags. ROS source timestamps must not be directly subtracted from monotonic timestamps. Filter phase == "recording" if stopping-tail samples are unwanted.
All 194 recordings have capture status completed, while success labels are null. Capture completion does not assert manipulation success. Video quality flags from the original metadata and frame logs are retained; consult them when selecting or aligning data.
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