Title:
Hierarchical Decision Theoretic Planning for Navigation Among Movable Obstacles

dc.contributor.author Levihn, Martin
dc.contributor.author Scholz, Jonathan
dc.contributor.author Stilman, Mike
dc.contributor.corporatename Georgia Institute of Technology. Center for Robotics and Intelligent Machines
dc.date.accessioned 2012-07-19T23:01:33Z
dc.date.available 2012-07-19T23:01:33Z
dc.date.issued 2012-06
dc.description Presented at the Tenth International Workshop on the Algorithmic Foundations of Robotics (WAFR 2012), 13-15 June 2012, Cambridge, MA. en_US
dc.description.abstract In 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.identifier.citation Levihn, 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.uri http://hdl.handle.net/1853/44348
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Motion planning en_US
dc.subject Navigation among movable obstacles en_US
dc.subject Path planning en_US
dc.title Hierarchical Decision Theoretic Planning for Navigation Among Movable Obstacles en_US
dc.type Text
dc.type.genre Proceedings
dspace.entity.type Publication
local.contributor.corporatename Humanoid Robotics Laboratory
relation.isOrgUnitOfPublication 05bf85fb-965e-425d-af8b-dbf56e0d9797
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