Learning-Assisted Multi-Step Planning

10 years 3 months ago
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.
Kris K. Hauser, Timothy Bretl, Jean-Claude Latombe
Added 25 Jun 2010
Updated 25 Jun 2010
Type Conference
Year 2005
Where ICRA
Authors Kris K. Hauser, Timothy Bretl, Jean-Claude Latombe
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