A procedural Long Term Memory for cognitive robotics

Abstract

This paper provides some insights into the advantages of using a Long-Term Memory (LTM) for optimizing the adaptive learning capabilities of a cognitive robot in dynamic environments. Specifically, a procedural LTM that stores basic models and behaviours is included in the evolutionary-based Multilevel Darwinist Brain (MDB) cognitive architecture. The memory system is based on learning error stability and instability to detect if a model is candidate to enter the LTM or to be recovered. A LTM replacement strategy has been developed that is based on context detection using functional comparison of the models' response. The LTM elements are tested in theoretical functions and in a simulated example using the AIBO robot in a dynamic context with successful adaptive learning results.