MobileH2R: Learning Generalizable Human to Mobile Robot Handover Exclusively from Scalable and Diverse Synthetic Data

Zifan Wang; Ziqing Chen; Junyu Chen; Jilong Wang; Yuxin Yang; Yunze Liu; Xueyi Liu; He Wang; Li Yi

doi:10.1109/cvpr52734.2025.01614

CVPR2025顶会

MobileH2R: Learning Generalizable Human to Mobile Robot Handover Exclusively from Scalable and Diverse Synthetic Data

Zifan Wang, Ziqing Chen, Junyu Chen, Jilong Wang, Yuxin Yang, Yunze Liu, Xueyi Liu, He Wang, Li Yi

DOI 出版方

2025年份

1顶会引用

摘要

https://MobileH2R.github.io (a) (b) (c) (d) Figure 1. The overview of MobileH2R. We propose a framework for generalizable human-to-mobile-robot handover, including a scalable pipeline for diverse full-body human motion synthesis (a), an automatic method for producing safe, imitation-friendly demonstrations (b), an efficient 4D imitation learning approach to learn coordinated base-arm actions (c), and successful sim2real transfer (d).