An architecture for emotional and context-aware associative learning for robot companions

Abstract

This work proposes a theoretical architectural model based on the brain's fear learning system with the purpose of generating artificial fear conditioning at both stimuli and context abstraction levels in robot companions. The proposed architecture is inspired by the different brain regions involved in fear learning, here divided into four modules that work in an integrated and parallel manner: the sensory system, the amygdala system, the hippocampal system and the working memory. Each of these modules is based on a different approach and performs a different task in the process of learning and memorizing environmental cues to predict the occurrence of unpleasant situations. The main contribution of the model proposed here is the integration of fear learning and context awareness in order to fuse emotional and contextual artificial memories. The purpose is to provide robots with more believable social responses, leading to more natural interactions between humans and robots.