On-line imitative interaction with a humanoid robot using a mirror neuron model

Abstract

This study presents experiments on the imitative interactions between a small humanoid robot and a user. A mirror neuron model was implemented in a humanoid robot, based on the recurrent neural network model with parametric bias (RNNPB). The experiments showed that after the robot learns multiple cyclic movement patterns as embedded in the RNNPB, it can regenerate each pattern synchronously with the movements of a human who is demonstrating the corresponding movement pattern in front of the robot. On the other hand, the robot exhibits diverse interactive responses when the user demonstrates novel cyclic movement patterns. Those responses were analyzed and categorized. We propose that both the dynamics of coherence and incoherence between the robot and the user movements could enhance close interactions between them.