动态着装：一种用于机器人辅助着装的人体运动感知扩散策略
Dressing in Motion: A Human Motion-Aware Diffusion Policy for Robot-Assisted Dressing
浏览论文内容
中文总结 AI 辅助
本研究提出一种人体运动感知扩散策略，从静态专家演示学习着装技能，通过以物体为中心的PDE扩散表示近似手臂运动，经仿真和真人实验验证，其在机器人辅助着装的进度、自由度和舒适度上优于基线方法。
中文摘要 AI 辅助
机器人着装辅助是支持有身体障碍的老年人日常生活的有前景的解决方案。然而，在人体运动下进行着装仍然具有挑战性，因为复杂的衣物-人体接触和遮挡使得难以生成与手臂运动对齐的动作。在本研究中，我们提出了一种视觉运动策略，该策略从静态专家演示中学习着装技能，并能泛化到动态用户运动场景。一种针对衣物-人体交互几何量身定制的扩散策略从具有不同手臂姿势的部分观测点云中学习。我们随后引入了一种基于PDE扩散的以物体为中心的表示，以捕捉手臂的轴向分布。通过采样与运动相关的区域并在连续观测中配准这些区域，所提出的方法近似手臂运动并主动调整执行的轨迹。我们在仿真和涉及9名参与者、3种衣物类型及6种手臂运动模式的真实人体研究中评估了我们的方法。结果表明，我们的方法在着装进度、运动自由度和用户舒适度方面均优于基线方法。项目网站为this https URL。
英文摘要
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.
发表机构
- The Hong Kong Polytechnic University（香港理工大学）
- University of York（约克大学）
- Great Bay University（大湾区大学）
机构由 AI 辅助整理，请以论文原文为准。
