Diffusion Policy for Robot-Assisted Dressing with Moving Human Arms
Haoxiang Sun, David Navarro-Alarcon
AI summary
Problem
Robot-assisted dressing struggles with deformable garments, visual occlusions, and the restrictive assumption that users must remain static, limiting natural interaction. Existing methods lack robust, real-time adaptation to dynamic human arm movements.
Approach
The system uses a hierarchical vision-based framework where a diffusion model learns action distributions from point clouds, while a local axial scalar field and point cloud registration continuously adapt the robot's trajectory to the user's moving arm in real time.
Key results
- High success rates across 3 garment types, 4 body types, and 10 dynamic motion scenarios
- Outperforms diffusion, imitation learning, and MPC baselines in sleeve insertion and dressing ratio metrics
- Enables real-time trajectory adaptation to non-static arm movements without full reconstruction
- Generalizes to unseen human poses and garment configurations beyond expert demonstrations
Why it matters
Makes robot-assisted dressing more practical and comfortable for daily living by accommodating natural human movement during interaction.
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 dress- ing 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.