Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
episode_id: string
camera_id: string
capture_source: string
coordinate_frame: string
pose_convention: string
camera_axes: string
to_opencv_camera_axes: string
timestamp_source: string
pose_is: string
align_by: string
num_frames: int64
frames: list<item: struct<frame_index: int64, tick_index: int64, trajectory_timestamp_seconds: double, times (... 97 chars omitted)
child 0, item: struct<frame_index: int64, tick_index: int64, trajectory_timestamp_seconds: double, timestamp_unix_s (... 85 chars omitted)
child 0, frame_index: int64
child 1, tick_index: int64
child 2, trajectory_timestamp_seconds: double
child 3, timestamp_unix_seconds: double
child 4, position: list<item: double>
child 0, item: double
child 5, rotation_quat_xyzw: list<item: double>
child 0, item: double
blit_rect_uv: list<item: double>
child 0, item: double
delivered_width: int64
sensor_width: int64
height: int64
sensor_crop_rect_px: list<item: double>
child 0, item: double
dist: list<item: double>
child 0, item: double
fx: double
sensor_height: int64
fy: double
distortion_model: string
delivered_height: int64
width: int64
cx: double
intrinsics_convention: string
cy: double
to
{'fx': Value('float64'), 'fy': Value('float64'), 'cx': Value('float64'), 'cy': Value('float64'), 'width': Value('int64'), 'height': Value('int64'), 'dist': List(Value('float64')), 'distortion_model': Value('string'), 'camera_id': Value('string'), 'capture_source': Value('string'), 'intrinsics_convention': Value('string'), 'sensor_width': Value('int64'), 'sensor_height': Value('int64'), 'sensor_crop_rect_px': List(Value('float64')), 'delivered_width': Value('int64'), 'delivered_height': Value('int64'), 'blit_rect_uv': List(Value('float64'))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
episode_id: string
camera_id: string
capture_source: string
coordinate_frame: string
pose_convention: string
camera_axes: string
to_opencv_camera_axes: string
timestamp_source: string
pose_is: string
align_by: string
num_frames: int64
frames: list<item: struct<frame_index: int64, tick_index: int64, trajectory_timestamp_seconds: double, times (... 97 chars omitted)
child 0, item: struct<frame_index: int64, tick_index: int64, trajectory_timestamp_seconds: double, timestamp_unix_s (... 85 chars omitted)
child 0, frame_index: int64
child 1, tick_index: int64
child 2, trajectory_timestamp_seconds: double
child 3, timestamp_unix_seconds: double
child 4, position: list<item: double>
child 0, item: double
child 5, rotation_quat_xyzw: list<item: double>
child 0, item: double
blit_rect_uv: list<item: double>
child 0, item: double
delivered_width: int64
sensor_width: int64
height: int64
sensor_crop_rect_px: list<item: double>
child 0, item: double
dist: list<item: double>
child 0, item: double
fx: double
sensor_height: int64
fy: double
distortion_model: string
delivered_height: int64
width: int64
cx: double
intrinsics_convention: string
cy: double
to
{'fx': Value('float64'), 'fy': Value('float64'), 'cx': Value('float64'), 'cy': Value('float64'), 'width': Value('int64'), 'height': Value('int64'), 'dist': List(Value('float64')), 'distortion_model': Value('string'), 'camera_id': Value('string'), 'capture_source': Value('string'), 'intrinsics_convention': Value('string'), 'sensor_width': Value('int64'), 'sensor_height': Value('int64'), 'sensor_crop_rect_px': List(Value('float64')), 'delivered_width': Value('int64'), 'delivered_height': Value('int64'), 'blit_rect_uv': List(Value('float64'))}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
OOJU Galaxea R1 Pro Pick Cube
177 bimanual cube-grasping demonstrations for the Galaxea R1 Pro, captured with Meta Quest 3 hand tracking and retargeted to the robot. Each episode carries full 26-joint hand poses for both hands, first-person passthrough camera frames, and 22-DoF robot joint positions, velocities and drive targets sampled at a nominal 50 Hz. The set totals 38,027 XR/camera frames and 80,014 robot samples across two grasp strategies, side grasp and top grasp.
No teleoperation rig was used. The demonstrations were recorded by a person wearing a headset and moving their hands, then converted to robot joint commands.
OOJU dataset series
| Dataset | Robot | Episodes | Robot actions | Format |
|---|---|---|---|---|
| wild-mani-kitchen | retarget targets: Unitree G1, UR5e | 102 | no | LeRobot v3.0 parquet |
| ooju-galaxea-pick-cube (this card) | Galaxea R1 Pro | 177 | yes, 80,014 samples | raw JSON + TAR |
| unitree-g1-pick-place-cup | Unitree G1 | 47 | yes | LeRobot parquet + policy baselines |
If you want published success rates for policies trained on OOJU data, the cup dataset is the one that ships evaluation results.
Tasks
| Folder | Description | Episodes |
|---|---|---|
side-grasp/ |
Pick up a cube using a side grasp | 120 |
top-grasp/ |
Pick up a cube using a top grasp | 57 |
Snapshot
| Property | Value |
|---|---|
| Episodes released | 177 |
| Recorded task time | 1,600.3 s (26.7 min), mean 9.0 s per episode |
| XR/camera frames | 38,027 |
| Robot samples | 80,014 |
| Robot degrees of freedom | 22 |
| Robot nominal sample rate | 50 Hz |
| JPEG frames (inside archives) | 38,027 |
| Frame archives | 177 |
| JSON files | 1,062 |
| Extracted size | ~5.4 GB |
| Collection dates | 2026-08-13 to 2026-08-16 |
The 177 episodes are the set retained after validation. Recordings that failed the workspace, joint-limit or teleoperation quality checks were not carried into this release, so this snapshot contains no failed demonstrations and the recorded task time above is the time in the released episodes, not the total time spent collecting.
Recording details
| Property | Value |
|---|---|
| Robot embodiment | Galaxea R1 Pro with grippers |
| Recording mode | Bimanual, hand tracking (no teleoperation rig) |
| XR device | Meta Quest 3 |
| XR tracking | Full hand-joint poses, 26 joints per hand |
| Camera | Left passthrough camera (passthrough_left) |
| Camera resolution | 1280 x 1280 RGB |
| Observed camera rate | 23.08 to 25.47 FPS |
Loading
This repository keeps the native recorder output as JSON and JPEG. It is not
converted to LeRobot or another training format, so load_dataset will not work.
Pull the files directly:
from huggingface_hub import snapshot_download
path = snapshot_download("OOJU/ooju-galaxea-pick-cube", repo_type="dataset")
# one episode, trajectory + robot stream + camera frames
tar -xf side-grasp/side-grasp_20260815_210713/camera_real/frames.tar -C ./frames
What is in each episode
Each recording has a task name and timestamp identifier, for example
side-grasp_20260815_210713 or top-grasp_20260813_213502:
<task>_<timestamp>.json— Time-series XR frames, including hand and eye poses and validation signals.<task>_<timestamp>_metadata.json— Episode and environment metadata, including task environment, device, timing, success, and quality checks.<task>_<timestamp>_robot.json— Robot observations and actions, including joint positions, velocities, drive targets, root pose, and engine state.camera_real/frames.tar— Uncompressed archive of the original JPEG passthrough frames (real_frame_*.jpg).camera_real/also contains camera poses, calibration, and video sidecar metadata.
Episode layout
side-grasp/
├── side-grasp_20260815_210713/
│ ├── side-grasp_20260815_210713.json
│ ├── side-grasp_20260815_210713_metadata.json
│ ├── side-grasp_20260815_210713_robot.json
│ └── camera_real/
│ ├── side-grasp_20260815_210713_camera_poses.json
│ ├── side-grasp_20260815_210713_video.json
│ ├── calib.json
│ └── frames.tar
└── ...
Robot joint ordering
The 22-element q, qd, and action_q arrays use this order:
- Four torso joints
- Seven left-arm joints
- Seven right-arm joints
- Two left-gripper finger joints
- Two right-gripper finger joints
Exact joint names are recorded in each *_robot.json file.
Hand-joint ordering
Each hand stores 26 poses in this order: palm and wrist; thumb metacarpal,
proximal, distal, tip; then metacarpal, proximal, intermediate, distal and tip
for index, middle, ring and little fingers. The names are repeated in each frame
under left_hand.joint_names and right_hand.joint_names. Every hand pose is
translation followed by rotation, [x, y, z, qx, qy, qz, qw].
Units and coordinate conventions
- Joint angles in radians, translations in metres, time in seconds.
- Quaternions are XYZW, scalar last.
- Robot root poses use the Unity left-handed, Y-up world frame.
- Camera poses are camera-to-world transforms in the Unity world frame.
- Camera intrinsics follow the OpenCV pinhole convention, top-left origin, downward-positive image Y axis.
Synchronization
The robot and XR/camera streams run at different rates. Robot samples are
nominally 50 Hz; each JPEG maps one-to-one to an XR frame and a camera-pose
entry at roughly 24 FPS. Align with timestamp_seconds in the trajectory file
and trajectory_timestamp_seconds in the camera-pose sidecar.
Do not substitute center_eye_pose, left_eye_pose or right_eye_pose for the
physical passthrough-camera pose. The eye fields are display-view poses; the
camera-pose sidecar describes the image sensor at capture time.
Data quality and limitations
- The first JPEG of every episode is blank. It is written before the passthrough stream is live, so start from the second frame.
- Camera sidecars flag 678 frames as
IMG_STATIC. - The camera distortion model is unmeasured; distortion coefficients are zero.
- Video sidecars contain recorder-generated MP4 path fields, but this snapshot
ships the original JPEG frames inside
frames.tar, not MP4 files. - The snapshot covers two grasp tasks, one robot embodiment, one XR device, one camera viewpoint and a limited physical setting.
- It contains no failed demonstrations, no audio, no depth and no force/torque.
- Capture device identifiers in the JSON have been replaced with a neutral label. All episodes come from a single headset.
What is visible in the frames
The passthrough frames are first person and were recorded indoors on a high floor. They show the demonstrator's bare hands and forearms, the desk surface and props used for the task, and a window with a view of the surrounding area. If you plan to redistribute derived imagery, note that the window view is identifiable.
FAQ
Can I collect demonstration data without building a teleoperation rig? Yes. Every episode here was recorded by a person wearing a Meta Quest 3 and moving their hands; the robot joint commands were produced afterwards by retargeting. There is no leader arm, no exoskeleton and no motion-capture stage.
Can data recorded from human hands be used on a robot? It has to be retargeted first. This repository ships the result of that step for the Galaxea R1 Pro: 80,014 robot samples with 22-DoF joint positions, velocities and drive targets, alongside the original hand poses. The retargeting is what turns a hand trajectory into something a robot controller can execute.
How many demonstrations do I need? This dataset does not answer that on its own. Our cup dataset ships trained checkpoints and evaluation results for one task at 47 demonstrations, and reports both the learned-policy and the retrieval-and-replay numbers so the gap is visible.
What is the difference between this and a teleoperation dataset? Most teleoperation datasets record the leader device or the robot's own commanded state. This records the human hand at 26 joints per hand plus the first-person view, and derives the robot action from it. The hand pose is kept, not discarded, so grasp shape survives into the released data.
Collect and validate your own data
If your task, objects or environment are not covered here, the same pipeline is available: Quest 3 capture, automated validation against a target embodiment, and export.
License
Released under the Apache 2.0 License.
Citation
@misc{ooju_galaxea_pick_cube,
title={OOJU Galaxea R1 Pro Pick Cube},
author={OOJU},
year={2026},
publisher={Hugging Face},
howpublished={\url{https://huggingface.co/datasets/OOJU/ooju-galaxea-pick-cube}}
}
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