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Robot-assisted dressing could benefit the lives of many people such as olderadults and individuals with disabilities. Despite such potential,robot-assisted dressing remains a challenging task for robotics as it involvescomplex manipulation of deformable cloth in 3D space. Many prior works aim tosolve the robot-assisted dressing task, but they make certain assumptions suchas a fixed garment and a fixed arm pose that limit their ability to generalize.In this work, we develop a robot-assisted dressing system that is able to dressdifferent garments on people with diverse poses from partial point cloudobservations, based on a learned policy. We show that with proper design of thepolicy architecture and Q function, reinforcement learning (RL) can be used tolearn effective policies with partial point cloud observations that work wellfor dressing diverse garments. We further leverage policy distillation tocombine multiple policies trained on different ranges of human arm poses into asingle policy that works over a wide range of different arm poses. We conductcomprehensive real-world evaluations of our system with 510 dressing trials ina human study with 17 participants with different arm poses and dressedgarments. Our system is able to dress 86% of the length of the participants'arms on average. Videos can be found on our project webpage:https://sites.google.com/view/one-policy-dress.

One Policy to Dress Them All: Learning to Dress People with Diverse Poses and Garments