Learning-Assisted Multi-Step Planning

Abstract, Probabilistic sampling-based motion planners are unable to detect when no feasible path exists. A common heuristic is to declare a query infeasible if a path is not found in a fixed amount of time. In applications where many queries must be processed-for instance, robotic manipulation, multi-limbed locomotion, and contact motion-a critical question arises: what should this time limit be? This paper presents a machine-learning approach to deal with this question. In an off-line learning phase, a classifier is trained to quickly predict the feasibility of a query. Then, an improved multi-step motion planning algorithm uses this classifier to avoid wasting time on infeasible queries. This approach has been successfully demonstrated in simulation on a four-limbed, free-climbing robot. Index Terms, Motion planning, multi-step planning, ma-chine learning, climbing robot.