EgoAERO: Learning Dexterous Manipulation from a Single Egocentric Video without Object Assets
EgoAERO is a novel framework that learns dexterous robot manipulation from a single egocentric RGB-D human demonstration without requiring pre-scanned object assets. It reconstructs contact-consistent hand-object trajectories and converts them into robot policies using two-stage residual learning. The framework also introduces an online quality assessment mechanism and compiles the large-scale EgoDex-R dataset. Experiments show it achieves performance close to CAD-based methods.
[2606.08057] EgoAERO: Learning Dexterous Manipulation from a Single Egocentric Video without Object Assets
[Submitted on 6 Jun 2026]
Title:EgoAERO: Learning Dexterous Manipulation from a Single Egocentric Video without Object Assets
View a PDF of the paper titled EgoAERO: Learning Dexterous Manipulation from a Single Egocentric Video without Object Assets, by Yichen Niu and 14 other authors
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Abstract: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 quality assessment mechanism and construct EgoDex-R, a large-scale egocentric dataset with 4.3M RGB-D frames for dexterous policy learning. Simulation and real-world experiments show that EgoAERO enables single-demonstration dexterous manipulation and achieves downstream performance close to CAD-based reconstructions on HOI4D.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.08057 [cs.RO]
(or arXiv:2606.08057v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.08057
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yichen Niu [view email] [v1] Sat, 6 Jun 2026 08:39:52 UTC (20,333 KB)
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