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dc.contributor.authorLevihn, Martin
dc.contributor.authorScholz, Jonathan
dc.contributor.authorStilman, Mike
dc.date.accessioned2012-07-19T23:01:33Z
dc.date.available2012-07-19T23:01:33Z
dc.date.issued2012-06
dc.identifier.citationLevihn, M., Scholz, J., & Stilman, M. (2012). “Hierarchical Decision Theoretic Planning for Navigation Among Movable Obstacles”. Proceedings of the Tenth International Workshop on the Algorithmic Foundations of Robotics (WAFR 2012), 13-15 June 2012.en_US
dc.identifier.urihttp://hdl.handle.net/1853/44348
dc.descriptionPresented at the Tenth International Workshop on the Algorithmic Foundations of Robotics (WAFR 2012), 13-15 June 2012, Cambridge, MA.en_US
dc.description.abstractIn this paper we present the first decision theoretic planner for the problem of Navigation Among Movable Obstacles (NAMO). While efficient planners for NAMO exist, they are challenging to implement in practice due to the inherent uncertainty in both perception and control of real robots. Generalizing existing NAMO planners to nondeterministic domains is particularly difficult due to the sensitivity of MDP methods to task dimensionality. Our work addresses this challenge by combining ideas from Hierarchical Reinforcement Learning with Monte Carlo Tree Search, and results in an algorithm that can be used for fast online planning in uncertain environments. We evaluate our algorithm in simulation, and provide a theoretical argument for our results which suggest linear time complexity in the number of obstacles for typical environments.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectMotion planningen_US
dc.subjectNavigation among movable obstaclesen_US
dc.subjectPath planningen_US
dc.titleHierarchical Decision Theoretic Planning for Navigation Among Movable Obstaclesen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machines


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