An Architecture for Robot Learning

Abstract

We propose a new approach to develop a learning robotic system. Learning starts from simple basic representations and actions. The robot is driven by the realization of goals that will need more complex and adapted representations and actions which are synthesized and selected according to their capacity of better achieving the goals. The system is based on sets of pulsed neural networks.