RI: Medium: Robotic Assistance with Dressing using Simulation-Based Optimization

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

The aging population, rising healthcare costs, and shortage of healthcare workers in the United States create a pressing need for affordable and effective personalized care. Physical disabilities due to illness, injury, or aging can result in people having difficulty dressing themselves, and the healthcare community has found that dressing is an important task for independent living. The goal of this research is to develop techniques that enable robots to assist people with putting on clothing, which is a challenging task for robots due to the complexities of cloth, the human body, and robots. A key aspect of this research is that robots will discover how they can help people by quickly trying out many options in a computer simulation. Success in this research would make progress towards robots capable of giving millions of people greater independence and a higher quality of life. In addition to healthcare applications, this research will result in better computer tools for fruitful collaborations between robots and humans in other scenarios. This research uses efficient physics simulation and optimization tools to substantially automate the design of assistive robots for dressing. The approach considers the robot to be an assistive device that a human learns to use. The system optimizes the assistive robot based on what a particular human with impairments is capable of doing comfortably, rather than what he/she typically does. This approach automatically optimizes personalized assistive controllers for a particular user and article of clothing via simulation. Due to frequent line-of-sight occlusion and the importance of controlling forces applied to the user's body, controllers that use data-driven haptic perception are trained using simulation-generated data. These capabilities critically depend on advancements in the efficient physical simulation of cloth, robots, and humans, as well as the discovery of appropriate human motions for a given assistive robot. This work advances the state of the art in assistive robotics, haptic perception, human modeling, optimization and efficient physical simulation. Evaluation of the system is in simulation and in the real world with test rigs that model aspects of dressing, a PR2 robot dressing a humanoid robot, and a PR2 dressing able-bodied participants with restricted motion.