Personal identity module using psychoevolutionary emotion theory for social robots

Robots that interacts socially in human being environment should be able to understand and act over it using clues from humans; it includes all types of expression: facial, non-facial, verbal and nonverbal affective expressions. These behaviors need a model capable to compute external and internal information to construct autonomous robots. In this paper, we are proposing a basis for modeling some reflexes inherent to human beings that are responsible by personal identity of each individual. The proposed model is constituted by two reflex modules and an emotion module that are based on psychoevolutionary theory. Then, this model has been inserted in our social architecture as an improvement of old motivational module and evaluated in a simulator. The advantage of the proposed approach from others is the possibility of representing the emotion as an internal function which varies over the time with different types of interaction. The experimental results show that this module is capable to emulate adequately human beings basic reflex in a robot during a simple interaction with a caregiver.