Learning action models for the improved execution of navigation plans

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 paper 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 networks or regression tree learning algorithms. We show, both in simulation and on an RWI B21 mobile robot, that the learned models together with the derived action selection function achieve competent navigation behavior.