Robotic Embodiment of Human-Like Motor Skills via Sim-to-Real Reinforcement Learning

Abstract

State of the art methods continue to face difficulties automating many tasks, particularly those which require human-like dexterity. The proposed "Internet of Skills" enables robots to learn advanced skills from a small set of expert demonstrations, bridging the gap between human and robot abilities. In this work, I train Reinforcement Learning (RL) control policies for the tasks of hand following and block pushing. I build a sim-to-real pipeline and demonstrate these policies on a Kinova Gen3 robot. Lastly, I test a prototype system that allows an expert to control the Kinova robot using only their arm movements, captured using a Vicon motion tracking system. My results show that performance of state of the art RL methods could be improved through the use of demonstrations, and I build a shared representation of human and robot action that will enable robots to learn new skills from observing expert actions.