Egocentric human data provides a scalable source of experience for robot learning, but varies substantially in human-robot alignment, behavioral coverage, and available supervision. Existing work shows favorable scaling with increasing human data, but it remains unclear which data properties drive downstream robot gain...
Zhi-Hao Sun, Liu Liu, Xin-Jiang Wang et al.· 0 citations
World-action models jointly learn robot policies and predict future observations, making the representation space an interface between control and prediction. We study the design of this space through controlled comparisons, finding that neither reconstruction fidelity nor pre-trained perceptual features alone ensure e...
Hao-Yi Jiang, Liu Liu, Xin-Jiang Wang et al.· 1 citation
This work introduces HandWorld, a unified generative framework that focuses on hand-object interaction and jointly models ego-centric videos and hand actions and learns shared cross-domain conditions through a dual-branch condition network that integrates information from both video and action domains.
Zhihao Sun, Zhi-Ying Du, Xitong Yang et al.· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
