Learning-Assisted Multi-Step Planning

— 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, multilimbed 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.

• Type: Conference
• Year: 2005
• Where: ICRA
• Authors: Kris K. Hauser, Timothy Bretl, Jean-Claude Latombe
• Keywords: Feasible Path Exists, ICRA 2005, Improved Multi-step Motion, Probabilistic Sampling-based Motion, Robotics