Learning-assisted multi-step planning

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 IEEE International Conference on Robotics and Automation
Number of pages6
StatePublished - Dec 1 2005
Externally publishedYes
Event2005 IEEE International Conference on Robotics and Automation - Barcelona, Spain
Duration: Apr 18 2005Apr 22 2005

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729


Other2005 IEEE International Conference on Robotics and Automation


  • Climbing robot
  • Machine learning
  • Motion planning
  • Multi-step planning

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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