@article{Bel02Lea,
  author  = {Thorsten Belker and Michael Beetz and Armin Cremers},
  title   = {Learning Action Models for the Improved Execution of Navigation Plans},
  journal = {Robotics and Autonomous Systems},
  volume  = {38},
  number  = {3--4},
  pages   = {137--148},
  month   = {March},
  year    = {2002},
  bib2html_pubtype  = {Journal},
  bib2html_rescat   = {Robot Learning, Plan-based Robot Control},
  bib2html_groups   = {IAS},
  bib2html_funding  = {ignore},
  bib2html_keywords = {Learning, Robot, Planning},
  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 behavior.}
}