Converting Motion between Different Types of Humanoid Robots Using Genetic Algorithms

Abstract

The imitation between different types of robots remains an unsolved task for a long time. The assignment of the correct angles to each joint is critical for robot motion. However, different robots have different structures, thus this discrepancy causes a difficulty when converting a motion to another type of robot. For solving this problem, we propose a GA-based method that can find the conversion matrix needed to map joint angles of one robot to another. There are two objectives to consider when creating an imitation; reducing the difference between the ideal imitation and the converted imitation and keeping the stability. Three experiments were conducted; a stable experiment, an unstable experiment and a double learning experiment. As a result, the double experiment showed a high concordance rate of 93.5%, the highest stability and the fastest speed of all experiments. These results show great promise for the proposed method as a way to realize motion imitation between different types of robots.