  We investigated the application of haptic feedback control and deep
reinforcement learning (DRL) to robot-assisted dressing. Our method uses DRL to
simultaneously train human and robot control policies as separate neural
networks using physics simulations. In addition, we modeled variations in human
impairments relevant to dressing, including unilateral muscle weakness,
involuntary arm motion, and limited range of motion. Our approach resulted in
control policies that successfully collaborate in a variety of simulated
dressing tasks involving a hospital gown and a T-shirt. In addition, our
approach resulted in policies trained in simulation that enabled a real PR2
robot to dress the arm of a humanoid robot with a hospital gown. We found that
training policies for specific impairments dramatically improved performance;
that controller execution speed could be scaled after training to reduce the
robot's speed without steep reductions in performance; that curriculum learning
could be used to lower applied forces; and that multi-modal sensing, including
a simulated capacitive sensor, improved performance.
