Dressing in Motion: A Human Motion-Aware Diffusion Policy for Robot-Assisted Dressing

2026-09-04

AI Summary

本文提出了一种基于扩散策略的方法，通过学习静态演示来解决机器人辅助穿衣过程中的人体运动适应问题，提高了穿衣进度、活动自由度和用户舒适度。

Abstract

Robotic dressing assistance is a promising solution for supporting older adults with physical impairments in daily living. However, dressing under human motion remains challenging, as complex garment--human contact and occlusions make it difficult to generate actions aligned with arm movements. In this letter, we propose a visuomotor policy that learns dressing skills from static expert demonstrations and generalizes to dynamic user-motion scenarios. A diffusion policy tailored to garment--human interaction geometry learns from partially observed point clouds with varied arm postures. We then introduce an object-centric representation based on PDE diffusion to capture the axial distribution of the arm. By sampling motion-relevant regions and registering them across consecutive observations, the proposed method approximates arm motion and reactively adapts the executed trajectory. We evaluate our method in simulation and a real-world human study involving nine participants, three garment types, and six arm-motion patterns. Results show that our method outperforms baselines in dressing progress, freedom of movement, and user comfort. The project website is https://anonymous.4open.science/w/dressing-in-motion.

Problem

• robotic dressing assistance
• human motion
• garment-human contact

Innovation

• visuomotor policy
• diffusion policy
• object-centric representation
• PDE diffusion