---
title: Gen-HumanEgo
canonical_url: "https://www.modelscope.cn/datasets/GenRobot.AI/Gen-HumanEgo"
md_url: "https://www.modelscope.cn/datasets/GenRobot.AI/Gen-HumanEgo.md"
repository: GenRobot.AI/Gen-HumanEgo
last_updated: 2026-10-13
license: cc-by-sa-4.0
storage_size: "51 TB"
downloads: 152133
stars: 3
---

# Gen-HumanEgo

> Gen-HumanEgo - GenRobot.AI 在 ModelScope 开源的数据集。1,800+ hours of egocentric human demonstrations with synchronized, structured supervision for embodied AI and robot learning.

GenRobot.AI/Gen-HumanEgo 是 ModelScope 魔搭社区上的数据集，存储大小 51 TB，采用 cc-by-sa-4.0 许可。

- **Repository**: GenRobot.AI/Gen-HumanEgo
- **License**: cc-by-sa-4.0
- **Storage size**: 51 TB
- **Downloads**: 152133
- **Stars**: 3
- **Last updated**: 2026-10-13

Source: https://www.modelscope.cn/datasets/GenRobot.AI/Gen-HumanEgo

---

# Gen-HumanEgo

<p align="center">
  <img src="assets/banner.jpg" width="100%">
</p>

**1,800+ hours of egocentric human demonstrations with synchronized, structured supervision for embodied AI and robot learning.**

Gen-HumanEgo contains real-world first-person demonstrations collected across diverse tasks, people, environments, and ways of performing activities using a unified six-camera [DAS-Ego](https://www.genrobot.ai/products/ego) setup. GenRobot's **Data Foundation Model (DFM)** processes the recordings to provide complementary training signals for human motion, spatial understanding, and task understanding.

The dataset includes:

- **Egocentric RGB:** synchronized multi-view video recordings.
- **3D hand reconstruction:** 3D hand keypoints, MANO parameters, and full hand geometry.
- **Spatial understanding:** large-field-of-view Ego-Depth.
- **Semantic understanding:** hierarchical video-, task-, and subtask-level annotations with temporal boundaries.

For a detailed introduction to the dataset, see our [Gen-HumanEgo Technical Blog](https://www.genrobot.ai/blog/gen-human-ego)


## Dataset at a glance

| Attribute | Value |
|---|---:|
| Total duration | **1,847.7 hours** |
| Episodes | **44,632** |
| Unique tasks | **10,257** |
| Domains | Home, business, industry, agriculture |
| Native RGB views | **6 synchronized cameras** |
| Resolution | **1600 × 1300** |
| Frame rate | **30 FPS** |
| File foramt| MCAP |
| Modalities | **21 3D keypoints per hand, full handmesh, depth maps, task+subtask annotation** |


## Repository structure

The task-organized directory tree follows the dataset's scenario/skill hierarchy. The final two levels are a two-character file-prefix directory and the episode's MCAP filename.

```text
Gen-HumanEgo/
├── README.md
├── egov4_urdf.zip
├── assets/
│   └── ...
└── data/
    ├── industry/
    │   └── logistics/
    │       └── sorting_and_packing/
    │           └── daily_work/
    │               └── 00/
    │                   └── 005c469cf7424c4c87054569b2234ebc.mcap
    ├── domestic_services/
    │   └── living_room/
    │       └── clothing_organization/
    │           └── iron_clothes/
    │               └── c0/
    │                   └── c096609eca54434e9fa93b0fff55ce3d.mcap
    └── business/
        └── restaurant/
            └── bakery/
                └── daily_work/
                    ├── a9/
                    │   └── a91e57586c33437292d763fb8fd3777c.mcap
                    └── 10/
                        └── 101207375b8e41fea31bacd94db480e6.mcap
```


## What's inside an episode?

Each episode is packaged as an MCAP recording. The release includes synchronized video and structured outputs, summarized below.

| Data | What it provides | Exact topic/schema |
| --- | --- | --- |
| Multi-view RGB | First-person visual observations | /robot0/sensor/camera[0-6]/compressed |
| Hand reconstruction | 3D keypoints, MANO parameters, hand geometry and associated validity/quality information where provided | /robot0/handtracking/left, /robot0/handtracking/right |
| Ego-Depth | Large-FOV depth for the surrounding workspace | /robot0/sensor/camera2/depth |
| Hierarchical annotations | Episode description, task segments, and fine-grained subtasks | /robot0/annotation_v2/ |

The hierarchical annotation design describes activity at three levels:

1. **Video:** an overall description of the episode.
2. **Task:** the objective of a continuous segment, with time/frame boundaries and scene information.
3. **Subtask:** a more detailed action caption, time/frame boundaries, and a success field (`is_success`), with objects, observable attributes, and spatial relationships expressed in the caption when applicable.

For example, an episode about tidying a sofa can include the task *“Tidy up the sofa”* and subtasks such as *“Place the red and black throw pillow on the sofa back”* and *“Flatten the sofa cover on the right seat of the sofa.”*



## Documentation and tools

- **GenRobot DAS-Ego Data Introduction** — topic definitions, message schemas, coordinate systems, timestamp conventions, and transformation definitions. [GenRobot DAS-Ego Data Introduction](https://docs.genrobot.ai/guides/das-ego-data-introduction)
- **GenRobot MCAP Visualization Tool** — browser-based visualization of MCAP episodes. [GenRobot MCAP Visualization Tool](https://monitor.genrobot.click/#/index)
- **GenRobot DAS DataKit** — tools for reading, validating, visualizing, and converting DAS-Ego data. [GenRobot DAS DataKit](https://github.com/genrobot-ai/das-datakit)
- **Ego URDF** — [`egov4_urdf.zip`](egov4_urdf.zip), containing the DAS-Ego device link structure and sensor-frame definitions.


## Contact

Questions, suggestions, and requests for additional scenarios or skills are welcome.

- X: [@GenrobotAI](https://x.com/GenrobotAI)
- LinkedIn: [GenRobot](https://www.linkedin.com/company/108767412/)
- Email: opendata@genrobot.ai
- Discord: https://discord.gg/rSSb5thgu