  Robot-assisted dressing could benefit the lives of many people such as older
adults and individuals with disabilities. Despite such potential,
robot-assisted dressing remains a challenging task for robotics as it involves
complex manipulation of deformable cloth in 3D space. Many prior works aim to
solve the robot-assisted dressing task, but they make certain assumptions such
as 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 dress
different garments on people with diverse poses from partial point cloud
observations, based on a learned policy. We show that with proper design of the
policy architecture and Q function, reinforcement learning (RL) can be used to
learn effective policies with partial point cloud observations that work well
for dressing diverse garments. We further leverage policy distillation to
combine multiple policies trained on different ranges of human arm poses into a
single policy that works over a wide range of different arm poses. We conduct
comprehensive real-world evaluations of our system with 510 dressing trials in
a human study with 17 participants with different arm poses and dressed
garments. 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.
