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

arXiv:2510.25241v2 Announce Type: replace-cross 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 s