2 research outputs found
Assistive Gym: A Physics Simulation Framework for Assistive Robotics
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.Comment: 8 pages, 5 figures, 2 table
Learning to Collaborate from Simulation for Robot-Assisted Dressing
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.Comment: 8 pages, 8 figures, 3 tables; simulation to reality experiment added
to evaluation; authors added; modified: title, abstract, conclusion,
references; figure adde