  We investigate robotic assistants for dressing that can anticipate the motion
of the person who is being helped. To this end, we use reinforcement learning
to create models of human behavior during assistance with dressing. To explore
this kind of interaction, we assume that the robot presents an open sleeve of a
hospital gown to a person, and that the person moves their arm into the sleeve.
The controller that models the person's behavior is given the position of the
end of the sleeve and information about contact between the person's hand and
the fabric of the gown. We simulate this system with a human torso model that
has realistic joint ranges, a simple robot gripper, and a physics-based cloth
model for the gown. Through reinforcement learning (specifically the TRPO
algorithm) the system creates a model of human behavior that is capable of
placing the arm into the sleeve. We aim to model what humans are capable of
doing, rather than what they typically do. We demonstrate successfully trained
human behaviors for three robot-assisted dressing strategies: 1) the robot
gripper holds the sleeve motionless, 2) the gripper moves the sleeve linearly
towards the person from the front, and 3) the gripper moves the sleeve linearly
from the side.
