AIS:Learning Motor Skills from Trajectory-based Reinforcement Learning

University Of Southern California, Los Angeles CA

Investigators

Abstract

This research addresses the question of how complex future robotic systems, e.g., like humanoid assistive robots, can acquire, refine, and maintain a variety of motor skills that enable them to operate autonomously in normal human environments. Humans excel in their abilities to perform motor skills due to various aspects, including i) imitation learning, which allows them to transfer prior knowledge about a task from a teacher to a student, ii) trial-and-error learning, which provides them with means to refine skills, iii) reactive behaviors, which can deal with dynamic and stochastic environments, and iv) compliant control, which is a basic mechanism for robustness against disturbances and promotes safety to act amongst other humans. Understanding the basic mechanisms of these abilities will lead to technological advances towards truly autonomous robotic systems. Our technical work includes research on modular representations of motor control in terms of movement primitives, research on trial-and-error improvement of motor primitives and sequences of motor primitives with trajectory-based reinforcement learning using novel techniques from probabilistic reinforcement learning and path-integral reinforcement learning, research on reactive behavior using direct coupling of motor primitives to perceptual variables, and compliant control with the help of operational space controllers that can be learned. Besides traditional benchmark simulation studies, our evaluations will include the learning of motor skills with a full-body humanoid robot, a system that significantly challenges the scalability of our methods.