juhyun-ooju Sharan Nayak hugsharan commited on
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Co-authored-by: Sharan Nayak <Sharan Nayak@users.noreply.huggingface.co>
Co-authored-by: hugsharan <hugsharan@users.noreply.huggingface.co>

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  3. README.md +256 -0
  4. side-grasp/side-grasp_20260815_210713/camera_real/calib.json +35 -0
  5. side-grasp/side-grasp_20260815_210713/camera_real/frames.tar +3 -0
  6. side-grasp/side-grasp_20260815_210713/camera_real/side-grasp_20260815_210713_camera_poses.json +0 -0
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  11. side-grasp/side-grasp_20260815_210732/camera_real/calib.json +35 -0
  12. side-grasp/side-grasp_20260815_210732/camera_real/frames.tar +3 -0
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  18. side-grasp/side-grasp_20260815_210753/camera_real/calib.json +35 -0
  19. side-grasp/side-grasp_20260815_210753/camera_real/frames.tar +3 -0
  20. side-grasp/side-grasp_20260815_210753/camera_real/side-grasp_20260815_210753_camera_poses.json +0 -0
  21. side-grasp/side-grasp_20260815_210753/camera_real/side-grasp_20260815_210753_video.json +27 -0
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  26. side-grasp/side-grasp_20260815_210809/camera_real/frames.tar +3 -0
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  40. side-grasp/side-grasp_20260815_210841/camera_real/frames.tar +3 -0
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+ *.lz4 filter=lfs diff=lfs merge=lfs -text
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+ *.mlmodel filter=lfs diff=lfs merge=lfs -text
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+ *.pickle filter=lfs diff=lfs merge=lfs -text
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+ *.pkl filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tar filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.zst filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - uncompressed
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+ *.pcm filter=lfs diff=lfs merge=lfs -text
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+ *.sam filter=lfs diff=lfs merge=lfs -text
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+ *.raw filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - compressed
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+ *.aac filter=lfs diff=lfs merge=lfs -text
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+ *.flac filter=lfs diff=lfs merge=lfs -text
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+ *.mp3 filter=lfs diff=lfs merge=lfs -text
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+ *.ogg filter=lfs diff=lfs merge=lfs -text
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+ *.wav filter=lfs diff=lfs merge=lfs -text
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+ # Image files - uncompressed
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+ *.bmp filter=lfs diff=lfs merge=lfs -text
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+ *.gif filter=lfs diff=lfs merge=lfs -text
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+ *.png filter=lfs diff=lfs merge=lfs -text
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+ *.tiff filter=lfs diff=lfs merge=lfs -text
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+ # Image files - compressed
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+ *.jpg filter=lfs diff=lfs merge=lfs -text
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+ *.jpeg filter=lfs diff=lfs merge=lfs -text
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+ *.webp filter=lfs diff=lfs merge=lfs -text
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+ # Video files - compressed
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+ *.mp4 filter=lfs diff=lfs merge=lfs -text
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+ *.webm filter=lfs diff=lfs merge=lfs -text
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+ .DS_Store
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+ *.zip
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+ ---
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+ license: apache-2.0
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+ pretty_name: OOJU Galaxea R1 Pro Pick Cube
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+ task_categories:
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+ - robotics
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+ size_categories:
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+ - 10K<n<100K
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+ tags:
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+ - robotics
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+ - real-world
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+ - teleoperation
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+ - imitation-learning
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+ - manipulation
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+ - bimanual
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+ - galaxea
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+ - galaxea-r1-pro
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+ - quest-3
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+ - meta-quest-3
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+ - hand-tracking
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+ - retargeting
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+ - embodied-ai
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+ ---
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+
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+ # OOJU Galaxea R1 Pro Pick Cube
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+
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+ **177 bimanual cube-grasping demonstrations for the Galaxea R1 Pro, captured with
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+ Meta Quest 3 hand tracking and retargeted to the robot.** Each episode carries
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+ full 26-joint hand poses for both hands, first-person passthrough camera frames,
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+ and 22-DoF robot joint positions, velocities and drive targets sampled at a
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+ nominal 50 Hz. The set totals 38,027 XR/camera frames and 80,014 robot samples
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+ across two grasp strategies, side grasp and top grasp.
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+
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+ No teleoperation rig was used. The demonstrations were recorded by a person
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+ wearing a headset and moving their hands, then converted to robot joint commands.
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+
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+ ## OOJU dataset series
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+
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+ | Dataset | Robot | Episodes | Robot actions | Format |
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+ | --- | --- | ---: | --- | --- |
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+ | [wild-mani-kitchen](https://huggingface.co/datasets/OOJU/wild-mani-kitchen) | retarget targets: Unitree G1, UR5e | 102 | no | LeRobot v3.0 parquet |
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+ | **ooju-galaxea-pick-cube** (this card) | Galaxea R1 Pro | 177 | **yes, 80,014 samples** | raw JSON + TAR |
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+ | [unitree-g1-pick-place-cup](https://huggingface.co/datasets/OOJU/pick-place-cup-20260404) | Unitree G1 | 47 | yes | LeRobot parquet + policy baselines |
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+
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+ If you want published success rates for policies trained on OOJU data, the cup
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+ dataset is the one that ships evaluation results.
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+
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+ ## Tasks
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+
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+ | Folder | Description | Episodes |
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+ | --- | --- | ---: |
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+ | `side-grasp/` | Pick up a cube using a side grasp | 120 |
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+ | `top-grasp/` | Pick up a cube using a top grasp | 57 |
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+
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+ ## Snapshot
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+
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+ | Property | Value |
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+ | --- | ---: |
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+ | Episodes released | 177 |
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+ | Recorded task time | 1,600.3 s (26.7 min), mean 9.0 s per episode |
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+ | XR/camera frames | 38,027 |
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+ | Robot samples | 80,014 |
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+ | Robot degrees of freedom | 22 |
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+ | Robot nominal sample rate | 50 Hz |
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+ | JPEG frames (inside archives) | 38,027 |
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+ | Frame archives | 177 |
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+ | JSON files | 1,062 |
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+ | Extracted size | ~5.4 GB |
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+ | Collection dates | 2026-08-13 to 2026-08-16 |
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+
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+ The 177 episodes are the set retained after validation. Recordings that failed
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+ the workspace, joint-limit or teleoperation quality checks were not carried into
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+ this release, so this snapshot contains no failed demonstrations and the recorded
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+ task time above is the time in the released episodes, not the total time spent
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+ collecting.
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+
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+ ## Recording details
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+
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+ | Property | Value |
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+ | --- | --- |
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+ | Robot embodiment | Galaxea R1 Pro with grippers |
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+ | Recording mode | Bimanual, hand tracking (no teleoperation rig) |
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+ | XR device | Meta Quest 3 |
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+ | XR tracking | Full hand-joint poses, 26 joints per hand |
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+ | Camera | Left passthrough camera (`passthrough_left`) |
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+ | Camera resolution | 1280 x 1280 RGB |
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+ | Observed camera rate | 23.08 to 25.47 FPS |
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+
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+ ## Loading
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+
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+ This repository keeps the native recorder output as JSON and JPEG. It is not
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+ converted to LeRobot or another training format, so `load_dataset` will not work.
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+ Pull the files directly:
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+
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+ ```python
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+ from huggingface_hub import snapshot_download
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+
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+ path = snapshot_download("OOJU/ooju-galaxea-pick-cube", repo_type="dataset")
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+ ```
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+
100
+ ```bash
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+ # one episode, trajectory + robot stream + camera frames
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+ tar -xf side-grasp/side-grasp_20260815_210713/camera_real/frames.tar -C ./frames
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+ ```
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+
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+ <!-- IF WE CONVERT TO PARQUET, REPLACE THE BLOCK ABOVE WITH:
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+ ```python
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+ from datasets import load_dataset
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+ ds = load_dataset("OOJU/ooju-galaxea-pick-cube", split="train")
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+ ```
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+ and add `lerobot` + `format:parquet` to the tags, and a `configs:` block to the
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+ frontmatter. Without that the Hub viewer stays off and no Croissant record is
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+ generated, so the dataset will not appear in Google Dataset Search.
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+ -->
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+
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+ ## What is in each episode
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+
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+ Each recording has a task name and timestamp identifier, for example
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+ `side-grasp_20260815_210713` or `top-grasp_20260813_213502`:
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+
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+ - `<task>_<timestamp>.json` — Time-series XR frames, including hand and eye
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+ poses and validation signals.
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+ - `<task>_<timestamp>_metadata.json` — Episode and environment metadata,
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+ including task environment, device, timing, success, and quality checks.
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+ - `<task>_<timestamp>_robot.json` — Robot observations and actions, including
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+ joint positions, velocities, drive targets, root pose, and engine state.
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+ - `camera_real/frames.tar` — Uncompressed archive of the original JPEG
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+ passthrough frames (`real_frame_*.jpg`).
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+ - `camera_real/` also contains camera poses, calibration, and video sidecar
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+ metadata.
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+
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+ ### Episode layout
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+
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+ ```text
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+ side-grasp/
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+ ├── side-grasp_20260815_210713/
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+ │ ├── side-grasp_20260815_210713.json
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+ │ ├── side-grasp_20260815_210713_metadata.json
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+ │ ├── side-grasp_20260815_210713_robot.json
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+ │ └── camera_real/
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+ │ ├── side-grasp_20260815_210713_camera_poses.json
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+ │ ├── side-grasp_20260815_210713_video.json
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+ │ ├── calib.json
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+ │ └── frames.tar
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+ └── ...
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+ ```
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+
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+ ### Robot joint ordering
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+
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+ The 22-element `q`, `qd`, and `action_q` arrays use this order:
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+
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+ 1. Four torso joints
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+ 2. Seven left-arm joints
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+ 3. Seven right-arm joints
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+ 4. Two left-gripper finger joints
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+ 5. Two right-gripper finger joints
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+
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+ Exact joint names are recorded in each `*_robot.json` file.
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+
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+ ### Hand-joint ordering
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+
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+ Each hand stores 26 poses in this order: palm and wrist; thumb metacarpal,
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+ proximal, distal, tip; then metacarpal, proximal, intermediate, distal and tip
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+ for index, middle, ring and little fingers. The names are repeated in each frame
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+ under `left_hand.joint_names` and `right_hand.joint_names`. Every hand pose is
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+ translation followed by rotation, `[x, y, z, qx, qy, qz, qw]`.
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+
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+ ### Units and coordinate conventions
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+
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+ - Joint angles in radians, translations in metres, time in seconds.
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+ - Quaternions are XYZW, scalar last.
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+ - Robot root poses use the Unity left-handed, Y-up world frame.
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+ - Camera poses are camera-to-world transforms in the Unity world frame.
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+ - Camera intrinsics follow the OpenCV pinhole convention, top-left origin,
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+ downward-positive image Y axis.
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+
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+ ## Synchronization
177
+
178
+ The robot and XR/camera streams run at different rates. Robot samples are
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+ nominally 50 Hz; each JPEG maps one-to-one to an XR frame and a camera-pose
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+ entry at roughly 24 FPS. Align with `timestamp_seconds` in the trajectory file
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+ and `trajectory_timestamp_seconds` in the camera-pose sidecar.
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+
183
+ Do not substitute `center_eye_pose`, `left_eye_pose` or `right_eye_pose` for the
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+ physical passthrough-camera pose. The eye fields are display-view poses; the
185
+ camera-pose sidecar describes the image sensor at capture time.
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+
187
+ ## Data quality and limitations
188
+
189
+ - The first JPEG of every episode is blank. It is written before the passthrough
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+ stream is live, so start from the second frame.
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+ - Camera sidecars flag 678 frames as `IMG_STATIC`.
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+ - The camera distortion model is unmeasured; distortion coefficients are zero.
193
+ - Video sidecars contain recorder-generated MP4 path fields, but this snapshot
194
+ ships the original JPEG frames inside `frames.tar`, not MP4 files.
195
+ - The snapshot covers two grasp tasks, one robot embodiment, one XR device, one
196
+ camera viewpoint and a limited physical setting.
197
+ - It contains no failed demonstrations, no audio, no depth and no force/torque.
198
+ - Capture device identifiers in the JSON have been replaced with a neutral label.
199
+ All episodes come from a single headset.
200
+
201
+ ## What is visible in the frames
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+
203
+ The passthrough frames are first person and were recorded indoors on a high
204
+ floor. They show the demonstrator's bare hands and forearms, the desk surface and
205
+ props used for the task, and a window with a view of the surrounding area. If you
206
+ plan to redistribute derived imagery, note that the window view is identifiable.
207
+
208
+ ## FAQ
209
+
210
+ **Can I collect demonstration data without building a teleoperation rig?**
211
+ Yes. Every episode here was recorded by a person wearing a Meta Quest 3 and
212
+ moving their hands; the robot joint commands were produced afterwards by
213
+ retargeting. There is no leader arm, no exoskeleton and no motion-capture stage.
214
+
215
+ **Can data recorded from human hands be used on a robot?**
216
+ It has to be retargeted first. This repository ships the result of that step for
217
+ the Galaxea R1 Pro: 80,014 robot samples with 22-DoF joint positions, velocities
218
+ and drive targets, alongside the original hand poses. The retargeting is what
219
+ turns a hand trajectory into something a robot controller can execute.
220
+
221
+ **How many demonstrations do I need?**
222
+ This dataset does not answer that on its own. Our
223
+ [cup dataset](https://huggingface.co/datasets/OOJU/pick-place-cup-20260404)
224
+ ships trained checkpoints and evaluation results for one task at 47
225
+ demonstrations, and reports both the learned-policy and the retrieval-and-replay
226
+ numbers so the gap is visible.
227
+
228
+ **What is the difference between this and a teleoperation dataset?**
229
+ Most teleoperation datasets record the leader device or the robot's own
230
+ commanded state. This records the human hand at 26 joints per hand plus the
231
+ first-person view, and derives the robot action from it. The hand pose is kept,
232
+ not discarded, so grasp shape survives into the released data.
233
+
234
+ ## Collect and validate your own data
235
+
236
+ If your task, objects or environment are not covered here, the same pipeline is
237
+ available: Quest 3 capture, automated validation against a target embodiment, and
238
+ export.
239
+
240
+ **[Request access](https://ooju-app.world/request?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=galaxea-pick-cube&utm_content=footer)**
241
+
242
+ ## License
243
+
244
+ Released under the Apache 2.0 License.
245
+
246
+ ## Citation
247
+
248
+ ```bibtex
249
+ @misc{ooju_galaxea_pick_cube,
250
+ title={OOJU Galaxea R1 Pro Pick Cube},
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+ author={OOJU},
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+ year={2026},
253
+ publisher={Hugging Face},
254
+ howpublished={\url{https://huggingface.co/datasets/OOJU/ooju-galaxea-pick-cube}}
255
+ }
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+ ```
side-grasp/side-grasp_20260815_210713/camera_real/calib.json ADDED
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