GRDT: enhancing model based learning for its application in robot navigation

Abstract

Robotics is one of the most challenging applications for the use of machine learning. Machine learning can offer an increase in flexibility and applicability in many robotic domains. In this paper, we sketch a framework to apply inductive logic programming (ILP) techniques to learning tasks of autonomous mobile robots. We point out differences between three existing algorithms used within this framework and their results. Since all of these algorithms have problems in solving the tasks, we developed GRDT (grammar based rule discovery tool), an algorithm combining their ideas and techniques. The paper is written in English.