[2510.25241] One-shot Adaptation of Humanoid Whole-body Motion with Walking Priors

Computer Science > Robotics arXiv:2510.25241 (cs) [Submitted on 29 Oct 2025 (v1), last revised 7 Apr 2026 (this version, v2)] Title:One-shot Adaptation of Humanoid Whole-body Motion with Walking Priors Authors:Hao Huang, Geeta Chandra Raju Bethala, Shuaihang Yuan, Congcong Wen, Mengyu Wang, Anthony Tzes, Yi Fang View a PDF of the paper titled One-shot Adaptation of Humanoid Whole-body Motion with Walking Priors, by Hao Huang and 6 other authors View PDF HTML (experimental) Abstract:Whole-body humanoid motion represents a fundamental challenge in robotics, requiring balance, coordination, and adaptability to enable human-like behaviors. However, existing methods typically require multiple training samples per motion, rendering the collection of high-quality human motion datasets both labor-intensive and costly. To address this, we propose a data-efficient adaptation approach that learns a new humanoid motion from a single non-walking target sample together with auxiliary walking motions and a walking-trained base model. The core idea lies in leveraging order-preserving optimal transport to compute distances between walking and non-walking sequences, followed by interpolation along geodesics to generate new intermediate pose skeletons, which are then optimized for collision-free configurations and retargeted to the humanoid before integration into a simulated environment for policy adaptation via reinforcement learning. Experimental evaluations on the CMU MoCap dataset demon...