  Robot-assisted dressing could profoundly enhance the quality of life of
adults with physical disabilities. To achieve this, a robot can benefit from
both visual and force sensing. The former enables the robot to ascertain human
body pose and garment deformations, while the latter helps maintain safety and
comfort during the dressing process. In this paper, we introduce a new
technique that leverages both vision and force modalities for this assistive
task. Our approach first trains a vision-based dressing policy using
reinforcement learning in simulation with varying body sizes, poses, and types
of garments. We then learn a force dynamics model for action planning to ensure
safety. Due to limitations of simulating accurate force data when deformable
garments interact with the human body, we learn a force dynamics model directly
from real-world data. Our proposed method combines the vision-based policy,
trained in simulation, with the force dynamics model, learned in the real
world, by solving a constrained optimization problem to infer actions that
facilitate the dressing process without applying excessive force on the person.
We evaluate our system in simulation and in a real-world human study with 10
participants across 240 dressing trials, showing it greatly outperforms prior
baselines. Video demonstrations are available on our project website
(https://sites.google.com/view/dressing-fcvp).
