A modular approach to self-organization of robot control based on language instruction

Abstract

In this paper we focus on how instructions for actions can be modelled in a self-organizing memory. Our approach draws from the concepts of regional distributed modularity and self-organization. We describe a self-organizing model that clusters action representations into different locations dependent on the body part they are related to. In the first case study we consider semantic representations of action verb meaning and then extend this concept significantly in a second case study by using actual sensor readings from our MIRA robot. Furthermore, we outline a modular model for a self-organizing robot action control system using language for instruction. Our approach for robot control using language incorporates some evidence related to the architectural and processing characteristics of the brain (Wermter et al. 2001 b). This paper focuses on the neurocognitive clustering of actions and regional modularity for language areas in the brain. In particular, we describe a self-organizing network that realizes action clustering (Pulvermüller 2003).