Foresight and Reconsideration in Hierarchical Planning and Execution
Kaelbling, Leslie Pack
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We present a hierarchical planning and execution architecture that maintains the computational efficiency of hierar- chical decomposition while improving optimality. It provides mech- anisms for monitoring the belief state during execution and per- forming selective replanning to repair poor choices and take advan- tage 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.