| --- |
| license: cc-by-4.0 |
| pretty_name: RoboMoCap |
| tags: |
| - robotics |
| - humanoid |
| - robot-learning |
| - egocentric |
| - whole-body-motion |
| - 3d |
| --- |
| <h1 align="center">RoboMoCap</h1> |
|
|
| <p align="center">A Large-Scale Whole-Body Motion Capture Dataset with Egocentric Vision for Robot Learning</p> |
|
|
| <p align="center"> |
| <a href="https://bitrobot-foundation.github.io/robomocap/"><strong>[Website]</strong></a> |
| |
| <strong>[Paper]</strong> |
| |
| <strong>[Code]</strong> |
| </p> |
|
|
| **Egocentric vision. High-fidelity whole-body motion.** |
|
|
| RoboMoCap pairs multimodal first-person observations with optical whole-body motion capture for humanoid robot learning. With six head-mounted and two wrist-mounted cameras, the dataset captures diverse locomotion and loco-manipulation behaviours, from everyday walking to dynamic movements and complex object interactions. |
|
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| **Beyond what egocentric cameras can see.** While many egocentric datasets focus primarily on hand-object interactions, RoboMoCap emphasises locomotion and loco-manipulation. By pairing first-person observations with high-fidelity optical motion capture, it provides accurate supervision for lower-body movement, foot placement, and whole-body coordination during navigation and object interactions. |
|
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| ## Dataset Overview |
|
|
| - **200+ hours** of recording |
| - **14** participants |
| - **5TB+** of data |
| - **290+** captured actions |
|
|
| ## Data Modalities |
|
|
| | Modality | Description | |
| | --- | --- | |
| | **Egocentric sensing ([RoboCap](https://robocap.io/))** | Six head-mounted cameras provide forward, downward, and side views, alongside IMU, magnetometer measurements, and WAV audio. | |
| | **Wrist sensing ([RoboWrist](https://robocap.io/))** | Left and right wrist cameras record close-range hand–object interactions, alongside IMU and magnetometer measurements. | |
| | **Whole-body motion** | A 53-marker [NOKOV](https://en.nokov.com/) optical mocap setup records whole-body motion at 120 Hz, including leg and foot movement outside the wearable camera views. | |
| | **External video** | [NOKOV](https://en.nokov.com/)'s third-person video records the participant, manipulated objects, and the surrounding space. | |
| | **Object motion** | Reflective markers track manipulated objects alongside the participant's body motion. | |
| | **Object geometry** | Textured 3D scans preserve the shape and appearance of objects used in the recordings. | |
| | **Hand pose** | Hand pose complements wrist video to describe hand movements during interaction. | |
|
|
| ## License |
|
|
| This dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @misc{robomocap, |
| title = {RoboMoCap: A Large-Scale Whole-Body Motion Capture Dataset with Egocentric Vision for Robot Learning}, |
| howpublished = {\url{https://bitrobot-foundation.github.io/robomocap/}} |
| } |
| ``` |
