Get Out of the Way – Obstacle Avoidance and Learning by Demonstration for Manipulation

Abstract Humans acquire manipulation skills by trial and error within a few trials, whereas programming a robot to perform the same task requires robotic expertise and effort. This paper presents a robot which learns a movement from demonstrations with the ability to generalize the movement to new goal poses and avoid the collision with obstacles in the workspace. The general movement is represented by dynamic movement primitives (DMP) augmented by potential fields in order to modulate the motion in the presence of obstacles. The approach is validated in experiments with a robotic arm in which dynamic obstacles partially blocking the movement are detected by a Photonic-Mixer-Devices (PMD) camera.