Integration of Online Learning into HTN Planning for Robotic Tasks

This paper extends hierarchical task network (HTN) planning with lightweight learning, considering that in robotics, actions have a non-zero probability of failing. Our work applies to A*-based HTN planners with lifting. We prove that the planner finds the plan of maximal expected utility, while retaining its lifting capability and efficient heuristic-based search. We show how to learn the probabilities online, which allows a robot to adapt by replanning on execution failures. The idea behind this work is to use the HTN domain to constrain the space of possibilities, and then to learn on the constrained space in a way requiring few training samples, rendering the method applicable to autonomous mobile robots.

Published in:
Proceedings of the AAAI Spring Symposium 2012: Designing Intelligent Robots, Reintegrating AI
Presented at:
Designing Intelligent Robots: Reintegrating AI, AAAI Spring Symposium 2012, Stanford, March 26th-28th, 2012

Note: The status of this file is: Anyone

 Record created 2012-01-10, last modified 2020-07-30

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