README.md

---
license: apache-2.0
task_categories:
  - robotics
tags:
  - humanoid
  - vla
  - imitation-learning
  - unitree-g1
  - egocentric
pretty_name: EgoHumanoid Sample Dataset (G1)
size_categories:
  - n<1K
---

# EgoHumanoid · Sample G1 Dataset

A sample dataset released alongside [**EgoHumanoid: Unlocking In-the-Wild Loco-Manipulation with Robot-Free Egocentric Demonstration**](https://arxiv.org/abs/2602.10106), containing both **robot teleoperation** (Unitree G1) and **robot-free egocentric human demonstration** (PICO VR + ZED) data for quick experimentation and fine-tuning.

<p align="center">
  <a href="https://opendrivelab.com/EgoHumanoid/"><img alt="Project Page" src="https://img.shields.io/badge/Project-EgoHumanoid-1f6feb?style=for-the-badge"></a>
  <a href="https://github.com/OpenDriveLab/EgoHumanoid"><img alt="GitHub" src="https://img.shields.io/badge/Code-GitHub-181717?style=for-the-badge&logo=github"></a>
  <a href="https://arxiv.org/abs/2602.10106"><img alt="arXiv" src="https://img.shields.io/badge/Paper-arXiv-b31b1b?style=for-the-badge&logo=arxiv"></a>
  <a href="https://huggingface.co/datasets/OpenDriveLab/EgoHumanoid"><img alt="HF Dataset" src="https://img.shields.io/badge/Dataset-HuggingFace-ffd21e?style=for-the-badge&logo=huggingface"></a>
</p>

---

## Overview

This repository hosts a **sample subset** with two data sources:

| Subset | Description | Episodes |
| --- | --- | --- |
| `example/robot/` | Robot teleoperation demonstrations (Unitree G1) | 50 |
| `example/human/` | Egocentric human demonstrations (PICO VR + ZED) | 1 |

Intended for:

- Smoke-testing the EgoHumanoid co-training pipeline (robot + human data)
- Fine-tuning a pretrained VLA policy on a small G1 task
- Validating data loaders and the LeRobot-compatible storage format

For the **full release** and broader documentation, please visit the project page and GitHub repository linked below.

## Links

| Resource | URL |
| --- | --- |
| Project Page | <https://opendrivelab.com/EgoHumanoid/> |
| GitHub Repo  | <https://github.com/OpenDriveLab/EgoHumanoid> |
| Paper (arXiv)| <https://arxiv.org/abs/2602.10106> |

## Dataset Structure

```
EgoHumanoid/
└── example/
    ├── robot/                          # robot teleoperation demonstrations
    │   ├── data/chunk-000/
    │   │   └── episode_*.parquet       # per-episode state / action trajectories
    │   ├── meta/
    │   │   ├── info.json               # schema & dataset metadata
    │   │   ├── modality.json           # input / output modality definitions
    │   │   ├── episodes.jsonl          # episode index
    │   │   ├── episodes_stats.jsonl    # per-episode statistics
    │   │   └── tasks.jsonl             # language task descriptions
    │   ├── videos/                     # egocentric RGB recordings
    │   └── norm_stats.json             # normalization statistics
    └── human/                          # egocentric human demonstrations
        ├── data/chunk-000/
        │   └── episode_*.parquet
        ├── meta/
        │   ├── info.json
        │   ├── episodes.jsonl
        │   ├── episodes_stats.jsonl
        │   └── tasks.jsonl
        └── videos/                     # egocentric RGB recordings
```

Both subsets follow the **LeRobot v2** convention, so any LeRobot-compatible loader can read them out of the box.

## Quick Start

```python
from huggingface_hub import snapshot_download

local_dir = snapshot_download(
    repo_id="OpenDriveLab/EgoHumanoid",
    repo_type="dataset",
    allow_patterns="example/*",
)
print(f"Downloaded to: {local_dir}/example")
```

Or with the CLI:

```bash
hf download OpenDriveLab/EgoHumanoid --repo-type=dataset --local-dir ./EgoHumanoid
```

## Citation

If you find this dataset useful, please cite the EgoHumanoid paper:

```bibtex
@article{shi2026egohumanoid,
    title={EgoHumanoid: Unlocking In-the-Wild Loco-Manipulation with Robot-Free Egocentric Demonstration},
    author={Shi, Modi and Peng, Shijia and Chen, Jin and Jiang, Haoran and Li, Yinghui and Huang, Di and Luo, Ping and Li, Hongyang and Chen, Li},
    journal={arXiv preprint arXiv:2602.10106},
    year={2026}
}
```

## License

Released under the **Apache 2.0** license.