Foresight and reconsideration in hierarchical planning and execution

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 t...

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Bibliographic Details
Main Authors: Levihn, Martin, Stilman, Mike, Kaelbling, Leslie P., Lozano-Perez, Tomas
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:en_US
Published: Institute of Electrical and Electronics Engineers (IEEE) 2014
Online Access:http://hdl.handle.net/1721.1/90271
https://orcid.org/0000-0002-8657-2450
https://orcid.org/0000-0001-6054-7145
Description
Summary: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.