Learning action oriented perceptual features for robot navigation

Abstract

Machine learning can offer an increase in the flexibility and applicability of robotics at several levels of control. In this paper, we characterize two symbolic learning tasks in the field of robotics. We outline an approach for learning features from sensory data and for using these features to learn more complex ones. We illustrate our approach with first experiments in the field of navigation. The paper is written in English.