On-line imitative interaction with a humanoid robot using a dynamic neural network model of a mirror system

This study presents experiments on the imitative interactions between a small humanoid robot and a user. A dynamic neural network model of a mirror system 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. Further, the robot exhibits diverse interactive responses when the user demonstrates novel cyclic movement patterns. Those responses were analyzed and categorized. We propose that the dynamics of coherence and incoherence between the robot's and the user's movements could enhance close interactions between them, and that they could also explain the essential psychological mechanism of joint attention.