Analysis of an Intention-Response Model Inspired by Brain Nervous System for Cognitive Robot

A service robot requires natural and interactive interaction with users without explicit commands. It is still one of the difficult problems to generate robust reactions for the robot in the real environment with unreliable sensor data to satisfy user’s requests. This paper presents an intention-response model based on mirror neuron and theory of mind, and analyzes the performance for a humanoid to show the usefulness. The model utilizes the modules of behavior selection networks to realize prompt response and goal-oriented characteristics of the mirror neuron, and performs reactions according to an action plan based on theory of mind. To cope with conflicting goals, behaviors of the sub-goal unit are generated using a hierarchical task network. Experiments with various scenarios reveal that appropriate reactions are generated according to external stimuli.