Incremental robot skill learning by human motion retargetting and physical human guidance

Abstract

Research on skill acquisition and generalization to a different scenario has grown steadily in importance and became now a main topic of robotics research. Imitation learning, one of the main streams for robot learning, provides an efficient way to learn new skills through human guidance, which can reduce time and cost to program the robot. This extended abstract presents our research on incremental skill learning through physical human robot interactions. We introduce our method to teach a robot how to learn synchronized and coordinated whole body motions. Our controller provides a human user comfortable assistance for physical guidance beyond the gravity compensation. External force torque estimation allows further possibilities. One is teaching motion primitives of a legged humanoid robot by taking human intervention into consideration for a balancing problem. Another is teaching multiple tasks like end-effector motions and null space motions. The proposed algorithms are verified on multiple robotic systems including full size humanoid robots.