Learning-assisted multi-step planning


Conference Paper

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. ©2005 IEEE.

Full Text

Duke Authors

Cited Authors

  • Hauser, K; Bretl, T; Latombe, JC

Published Date

  • December 1, 2005

Published In

Volume / Issue

  • 2005 /

Start / End Page

  • 4575 - 4580

International Standard Serial Number (ISSN)

  • 1050-4729

International Standard Book Number 10 (ISBN-10)

  • 078038914X

International Standard Book Number 13 (ISBN-13)

  • 9780780389144

Digital Object Identifier (DOI)

  • 10.1109/ROBOT.2005.1570825

Citation Source

  • Scopus