A method for learning complex and dexterous behaviors through knowledge array network

To meet the demand for robot to perform complex tasks, it is desirable to develop a methodology for intelligent behavior evolution in which a robot learns behaviors just as a human acquires dexterity, by repeated practice and use. Presented is a method for learning complex and dexterous behaviors through a knowledge array network, i.e., a network of knowledge arrays that play most important role as behavioral building blocks for robot behavior learning and evolution based on the intelligent composite motion control (ICMC). The process to realize a behavior from component element motions is presented. It is shown how a ball shooting behavior by a legged robot in robot soccer is realized according to the proposed method. Component element motions, are optimized. The optimal parameters obtained are then stored as a knowledge array, with which the robot can adaptively execute sub-optimal motions even for inexperienced situations. With the element motions optimized beforehand for a wide range of situations, the desirable shooting is obtained by combining them with additional optimization. The numerical result is given to demonstrate the presented method