EgoAERO: Learning Dexterous Manipulation from a Single Egocentric Video without Object Assets
Egocentric RGB-D videos offer a natural source of human dexterous manipulation demonstrations, but existing data is difficult to use for robot learning because object pose, geometry, and contact information are often missing or require pre-scanned object assets. We present EgoAERO, the first framework that learns dexterous manipulation from a single egocentric RGB-D human demonstration without object assets. EgoAERO reconstructs contact-consistent hand-object trajectories through asset-free object tracking and reconstruction, ego motion compensation, and adaptive contact optimization, then converts them into robot policies using two-stage residual learning. We further introduce an online ...
| Authors | Yichen Niu, Haoran Lv, Xinrui Zhang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 6 June 2026 |
| Last updated at source | 6 June 2026 |
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