On Intelligent Robot Motion Planning via Learning

Abstract

Automatic motion planning is one of the basic modules that are needed to increase robot intelligence and usability. Unfortunately, the inherent complexity of motion planning has rendered traditional search algorithms incapable of solving every problem in real time. To circumvent this difficulty, we explore the alternative of allowing human operators to participate in the problem solving process. By having the human operator teach during difficult motion planning episodes, the robot should be able to learn and improve its own motion planning capability and gradually reduce its reliance on the human operator. In this paper, we present such a learning framework in which both human and robot can cooperate to achieve real-time automatic motion planning. To enable a deeper understanding of the framework in terms of performance, we present it as a simple learning algorithm and provide theoretical analysis of its behavior. In particular, we characterize the situations in which learning is useful, and provide quantitative bounds to predict the necessary training time and the maximum achievable speedup in planning time.