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 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:
| 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 Γ 1300 |
| Frame rate | 30 FPS |
| File foramt | MCAP |
| Modalities | 21 3D keypoints per hand, full handmesh, depth maps, task+subtask annotation |
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.
Gen-HumanEgo/
βββ README.md
βββ egov4_urdf.zip
βββ assets/
β βββ ...
βββ data/
βββ industry/
β βββ logistics/
β βββ sorting_and_packing/
β βββ daily_work/
β βββ 00/
β βββ 005c469cf7424c4c87054569b2234ebc.mcap
βββ domestic_services/
β βββ living_room/
β βββ clothing_organization/
β βββ iron_clothes/
β βββ c0/
β βββ c096609eca54434e9fa93b0fff55ce3d.mcap
βββ business/
βββ restaurant/
βββ bakery/
βββ daily_work/
βββ a9/
β βββ a91e57586c33437292d763fb8fd3777c.mcap
βββ 10/
βββ 101207375b8e41fea31bacd94db480e6.mcap
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:
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 βTidy up the sofaβ and subtasks such as βPlace the red and black throw pillow on the sofa backβ and βFlatten the sofa cover on the right seat of the sofa.β
egov4_urdf.zip, containing the DAS-Ego device link structure and sensor-frame definitions.Questions, suggestions, and requests for additional scenarios or skills are welcome.
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 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:
| 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 Γ 1300 |
| Frame rate | 30 FPS |
| File foramt | MCAP |
| Modalities | 21 3D keypoints per hand, full handmesh, depth maps, task+subtask annotation |
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.
Gen-HumanEgo/
βββ README.md
βββ egov4_urdf.zip
βββ assets/
β βββ ...
βββ data/
βββ industry/
β βββ logistics/
β βββ sorting_and_packing/
β βββ daily_work/
β βββ 00/
β βββ 005c469cf7424c4c87054569b2234ebc.mcap
βββ domestic_services/
β βββ living_room/
β βββ clothing_organization/
β βββ iron_clothes/
β βββ c0/
β βββ c096609eca54434e9fa93b0fff55ce3d.mcap
βββ business/
βββ restaurant/
βββ bakery/
βββ daily_work/
βββ a9/
β βββ a91e57586c33437292d763fb8fd3777c.mcap
βββ 10/
βββ 101207375b8e41fea31bacd94db480e6.mcap
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:
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 βTidy up the sofaβ and subtasks such as βPlace the red and black throw pillow on the sofa backβ and βFlatten the sofa cover on the right seat of the sofa.β
egov4_urdf.zip, containing the DAS-Ego device link structure and sensor-frame definitions.Questions, suggestions, and requests for additional scenarios or skills are welcome.