---
license: cc-by-4.0
pretty_name: RoboMoCap
tags:
  - robotics
  - humanoid
  - robot-learning
  - egocentric
  - whole-body-motion
  - 3d
---

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<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>
  &nbsp;&nbsp;
  <a href="https://bitrobot-foundation.github.io/robomocap/RoboMoCap.pdf"><strong>[Paper]</strong></a>
  &nbsp;&nbsp;
  <strong>[Code]</strong> (coming soon)
</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{deshmukh2026robomocap,
  title         = {{RoboMoCap}: A Large-Scale Whole-Body Motion Capture Dataset with Egocentric Vision for Robot Learning},
  author        = {Deshmukh, Mayur and Tung, Aaron and Li, Junyi and Bhardwaj, Nikash and Adeniji, Ademi and Liu, Vincent and Cho, Michael and Duan, Jiafei},
  year          = {2026},
  eprint        = {2611.10994},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  doi           = {10.48550/arXiv.2611.10994},
  url           = {https://arxiv.org/abs/2611.10994}
}
```