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

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 this https URL.

Robotics (cs.RO)

arXiv:2609.04759 [cs.RO]

(or arXiv:2609.04759v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.04759 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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