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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.
**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.
## 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/}}
}
``` |