Developmental Learning for Autonomous Robots

Abstract

Developmental robotics is concerned with the design of algorithms that promote robot adaptation and learning through qualitative growth of behaviour and increasing levels of competence. This paper uses ideas and inspiration from early infant psychology (up to three months of age) to examine how robot systems could discover the structure of their local sensory-motor spaces and learn how to coordinate these for the control of action. An experimental learning model is described and results from robotic experiments using the model are presented and discussed.