Foresight and reconsideration in hierarchical planning and execution
Author(s)
Levihn, Martin; Stilman, Mike; Kaelbling, Leslie P.; Lozano-Perez, Tomas
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We present a hierarchical planning and execution architecture that maintains the computational efficiency of hierarchical decomposition while improving optimality. It provides mechanisms for monitoring the belief state during execution and performing selective replanning to repair poor choices and take advantage of new opportunities. It also provides mechanisms for looking ahead into future plans to avoid making short-sighted choices. The effectiveness of this architecture is shown through comparative experiments in simulation and demonstrated on a real PR2 robot.
Date issued
2013-11Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Levihn, Martin, Leslie Pack Kaelbling, Tomas Lozano-Perez, and Mike Stilman. “Foresight and Reconsideration in Hierarchical Planning and Execution.” 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (November 2013).
Version: Author's final manuscript
ISBN
978-1-4673-6358-7
978-1-4673-6357-0