Foresight and reconsideration in hierarchical planning and execution
Name
Lozano-Perez_Foresight and.pdf
Size
1.35 MB
Format
Adobe PDF
Checksum (MD5)
87323dc0d73df208f00de2869e0e937f
Author(s)
Levihn, Martin
Stilman, Mike
Kaelbling, Leslie P.
Lozano-Perez, Tomas
Date Issued
November 2013
Journal
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
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/IROS.2013.6696357
