Foresight and reconsideration in hierarchical planning and execution

• Main Authors: Levihn, Martin (Author), Stilman, Mike (Author), Kaelbling, Leslie P. (Contributor), Lozano-Perez, Tomas (Contributor)
• Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers (IEEE), 2014-09-22T18:50:46Z.
• Subjects: Article
• Online Access: Get fulltext

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.
National Science Foundation (U.S.) (Grant IIS-1117325)
National Science Foundation (U.S.) (Grant IIS-1017076)
United States. Office of Naval Research (Grant N00014-12-1-0143)
United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-09-1-1051)
United States. Air Force Office of Scientific Research (Grant FA2386-10-1-4135)