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Learning Action Models for the Improved Execution of Navigation Plans (bibtex)
  author = {Belker, Thorsten and Beetz, Michael and Cremers, Armin},
  title = {Learning Action Models for the Improved Execution of Navigation Plans},
  journal = {Robotics and Autonomous Systems},
  year = {2002},
  volume = {38},
  pages = {137–148},
  number = {3–4},
  month = mar,
  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
	themselves 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 Process ({MDP)} and derive
	a decision theoretic action selection function from it. The action
	selection function employs models of the robot's navigation actions,
	which are autonomously acquired from experience using neural network
	or regression tree learning algorithms. We show, both in simulation
	and on a {RWI} B21 mobile robot, that the learned models together
	with the derived action selection function achieve competent navigation
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Last edited 29.01.2013 17:37 by Quirin Lohr