A behavior-based architecture for teaching more than reactive behaviors to mobile robots

Abstract

The article gives an overview of learning aspects regarding behavior based control architectures for autonomous mobile robots. We propose a common, modularized architecture, which is particularly suited for learning experiments. The main focus lies in "Learning from Demonstration" in a spatial domain, which means teaching motor behaviors by humans or other robots. First results applying RBF approximation, growing neural cell structures and probabilistic models for progress estimation are presented.