Learning in a Reactive Robotic Architecture

Abstract: In this licenciate thesis, we discuss how to generate actions from percepts within an autonomous robotic system. In particular, we discuss and propose an original reactive architecture suitable for response generation, learning and self-organization.The architecture uses incremental learning and supports self organization through distributed dynamic model generation and self-contained components. Signals to and from the architecture are represented using the channel representation, which is presented in that context.The components of the architecture use a novel and flexible implementation of an artificial neural network. The learning rules for this implementation are derived.A simulator is presented. It has been designed and implemented in order to test and evaluate the proposed architecture.Results of a series of experiments on the reactive architecture are discussed and accounted for. The experiments have been performed within three different scenarios, using the developed simulator.The problem of information representation in robotic architectures is illustrated by a problem of anchoring symbols to visual data. This is presented in the context of the WITAS project.