Robot-assisted dressing could profoundly enhance the quality of life of adults with physical disabilities. To achieve this, a robot can benefit from both visual and force sensing. The former enables the robot to ascertain human body pose and garment deformations, while the latter helps maintain safety and comfort during the dressing process. In this paper, we introduce a new technique that leverages both vision and force modalities for this assistive task. Our approach first trains a vision-based dressing policy using reinforcement learning in simulation with varying body sizes, poses, and types of garments. We then learn a force dynamics model for action planning to ensure safety. Due to limitations of simulating accurate force data when deformable garments interact with the human body, we learn a force dynamics model directly from real-world data. Our proposed method combines the vision-based policy, trained in simulation, with the force dynamics model, learned in the real world, by solving a constrained optimization problem to infer actions that facilitate the dressing process without applying excessive force on the person. We evaluate our system in simulation and in a real-world human study with 10 participants across 240 dressing trials, showing it greatly outperforms prior baselines. Video demonstrations are available on our project website (https://sites.google.com/view/dressing-fcvp).

翻译：机器人辅助穿衣技术有望显著提升肢体残障成人的生活品质。为实现这一目标，机器人需同时依赖视觉与力觉感知——前者使机器人能够获取人体姿态与服装形变信息，后者则有助于在穿衣过程中维持安全性与舒适度。本文提出一种整合视觉与力觉模态的新技术。该方法首先在仿真环境中，针对不同体型、姿态及服装类型，通过强化学习训练基于视觉的穿衣策略，随后学习面向动作规划的力动力学模型以确保安全性。由于变形服装与人体交互过程中难以模拟精确的力数据，我们直接从真实数据中学习力动力学模型。本方法通过求解约束优化问题，将仿真训练得到的视觉策略与真实环境习得的力动力学模型相结合，推导出既能促进穿衣进程、又不会对人体施加过度作用力的动作。我们在仿真环境及包含10名受试者、共计240次穿衣试验的真实人体实验中进行了系统评估，结果表明该方法显著优于现有基线方案。项目网站（https://sites.google.com/view/dressing-fcvp）提供视频演示。