Human-robot communication using multiple recurrent neural networks

On the methodology of robotic design from the traditional view of communication which is assumed as a symbol process, robots are forced to confront the symbol grounding problem. However, if communication is assumed as the analog dynamics and robots are driven by it, robots can avoid the problem and be situated in the environment and to other agents. In this paper we will introduce a new communication system constructed from the view of dynamical systems to achieve the situatedness. This system is that there is a robot in a virtual environment and the control of the robot is shared by human operation using a joystick and a robot controller. As the controller, we adopt multiple recurrent neural networks (MRNN) which are able to cope with complex environments and broad communication that single recurrent net cannot cope with. We conduct two experiments in order to evaluate the effectiveness of MRNN to a low level communication task such as nonverbal interaction. First, we examine the effect of the number of RNNs contained in MRNN. Second, we examine the effect of the context dependency of MRNN. These experiments show the capability of MRNN as a new-type controller of communication robot.