让机器人根据人动时的肢体姿态，智能调整穿衣动作。
Dressing in Motion: A Human Motion-Aware Diffusion Policy for Robot-Assisted Dressing
- 用扩散模型学习衣服与人体交互几何，从静态示范中泛化到动态场景。
- 在9名参与者、3种衣物、6种动作模式下，穿衣进度和舒适度优于基线。
- 适合需要动态适应用户动作的护理机器人研发人员参考。
机器人辅助穿衣对帮助行动不便的老年人具有重要意义。然而，面对人体运动时的复杂衣物-人体接触与遮挡，生成与手臂动作对齐的动作仍具挑战。本文提出一种视觉-运动策略，从静态专家示范中学习穿衣技能，并泛化至动态用户运动场景。采用针对衣物-人体交互几何设计的扩散策略，基于部分观测的点云学习不同手臂姿态下的动作。引入基于偏微分方程扩散的对象中心表示，捕捉手臂轴向分布；通过采样运动相关区域并跨连续观测注册，近似手臂运动并实时调整执行轨迹。我们在仿真环境及包含九名参与者、三种衣物类型和六种手臂运动模式的真实人类实验中评估该方法。结果表明，本方法在穿衣进展、动作自由度和用户舒适度方面均优于基线。项目网站：https://anonymous.4open.science/w/dressing-in-motion。
原文摘要 · Abstract (English)
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.
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