Robot learning from demonstration for path planning: A review

Abstract

Learning from demonstration (LfD) is an appealing method of helping robots learn new skills. Numerous papers have presented methods of LfD with good performance in robotics. However, complicated robot tasks that need to carefully regulate path planning strategies remain unanswered. Contact or non-contact constraints in specific robot tasks make the path planning problem more difficult, as the interaction between the robot and the environment is time-varying. In this paper, we focus on the path planning of complex robot tasks in the domain of LfD and give a novel perspective for classifying imitation learning and inverse reinforcement learning. This classification is based on constraints and obstacle avoidance. Finally, we summarize these methods and present promising directions for robot application and LfD theory.