Technical University of Munich

@inproceedings{belker_learning_2001,
 author = {T Belker and M Beetz},
 title = {Learning to Execute Robot Navigation Plans},
 booktitle = {Proceedings of the 25th German Conference on Artificial Intelligence
	({KI} 01)},
 year = {2001},
 address = {Wien, Austria},
 publisher = {Springer Verlag},
 abstract = {Most state-of-the-art navigation systems for autonomous service robots
	decompose navigation into global navigation planning and local reactive
	navigation. While the methods for navigation planning and local navigation
	are well understood, the plan execution problem, the problem of how
	to generate and parameterize local navigation tasks from a given
	navigation plan, is largely unsolved. This article describes how
	a robot can autonomously learn to execute navigation plans. We formalize
	the problem as a Markov Decision Problem ({MDP)}, discuss how it
	can be simplified to make its solution feasible, and describe how
	the robot can acquire the necessary action models. We show, both
	in simulation and on a {RWI} B21 mobile robot, that the learned models
	are able to produce competent navigation behavior.},
}