  Autonomous robots have the potential to serve as versatile caregivers that
improve quality of life for millions of people worldwide. Yet, conducting
research in this area presents numerous challenges, including the risks of
physical interaction between people and robots. Physics simulations have been
used to optimize and train robots for physical assistance, but have typically
focused on a single task. In this paper, we present Assistive Gym, an open
source physics simulation framework for assistive robots that models multiple
tasks. It includes six simulated environments in which a robotic manipulator
can attempt to assist a person with activities of daily living (ADLs): itch
scratching, drinking, feeding, body manipulation, dressing, and bathing.
Assistive Gym models a person's physical capabilities and preferences for
assistance, which are used to provide a reward function. We present baseline
policies trained using reinforcement learning for four different commercial
robots in the six environments. We demonstrate that modeling human motion
results in better assistance and we compare the performance of different
robots. Overall, we show that Assistive Gym is a promising tool for assistive
robotics research.
