Motivational System for Human-Robot Interaction

Abstract

Human-Robot Interaction (HRI) has recently drawn increased attention. Robots can not only passively receive information but also actively emit actions. We present a motivational system for human-robot interaction. The motivational system signals the occurrence of salient sensory inputs, modulates the mapping from sensory inputs to action outputs, and evaluates candidate actions. No salient feature is predefined in the motivational system but instead novelty based on experience, which is applicable to any task. Novelty is defined as an innate drive. Reinforcer is integrated with novelty. Thus, the motivational system of a robot can be developed through interactions with trainers. We treat vision-based neck action selection as a behavior guided by the motivational system. The experimental results are consistent with the attention mechanism in human infants.