Diffusion Policy for Robot-Assisted Dressing with Moving Human Arms

Abstract

Robot-assisted dressing remains challenging due to the close physical human–robot interaction and the highly deformable nature of garments. This work presents a purely vision-based approach that transfers human-mastered dressing skills to robots while accommodating dynamic human arm movements. The proposed method adopts a hierarchical structure. At the high level, a diffusion model serves as the policy to learn action distributions conditioned on point cloud observations. During execution, a diffused scalar field is constructed to infer an object-centric axial distribution of the human arm from cluttered points. Local point cloud registration across consecutive frames further captures arm motion, enabling real-time adaptation of robot actions to user dynamics. Comprehensive evaluations have been conducted in both simulation and real-world dressing scenarios using a UR10e robot with human participants of diverse genders and body types.