Robot posture generation based on genetic algorithm for imitation

Human-like-motion performed by robots can have a contribution to exert a strong influence on human-robot interaction, because bodily expressions convey important and effective information. If the robots could adapt the features of human behavior to their motions and skills, the communication would become more smooth and natural. In this paper, we develop a posture measurement system for a robot imitation using a 3D image sensor. This paper proposes a method of robot posture generation based on a steady-state genetic algorithm (SSGA). SSGA is one of evolutionary optimization methods using selection, mutation, and crossover operators. Since SSGA is a simplified model, it is easy to implement into a real-time processing. Furthermore, we apply a continuous model of generation for an adaptive search in dynamical environment.