Learning users' and personality-gender preferences in close human-robot interaction

Robots are expected to interact with persons in their everyday activities and should learn the preferences of their users in order to deliver a more natural interaction. Having a memory system that remembers past events and using them to generate an adapted robot's behavior is a useful feature that robots should have. Nevertheless, robots will have to face unknown situations and behave appropriately. We propose the usage of user's personality (introversion/extroversion) to create a model to predict user's preferences so as to be used when there are no past interactions for a certain robot's task. For this, we propose a framework that combines an Emotion System based on the OCC Model with an Episodic-Like Memory System. We did an experiment where a group of participants customized robot's behavior with respect to their preferences (personal distance, gesture amplitude, gesture speed). We tested the obtained model against preset behaviors based on the literature about extroversion preferences on interaction. For this, a different group of participants was recruited. Results shows that our proposed model generated a behavior that was more preferred by the participants than the preset behaviors. Only the group of introvert-female participants did not present any significant difference between the different behaviors.