Applying conversion matrix to robots for imitating motion using genetic algorithms

In this paper, we propose a method using a genetic algorithm (GA) for motion imitation between two different types of humanoid robots. Although motion imitation between humans and robots has been a popular research topic for a long time, the imitation between different types of robots still remains an unsolved task. The selection of the correct joint angles is critical for robot motion. However, different robots have different anatomies, with each joint´s position and movable range uniquely defined for each type of robot. This discrepancy is an obstacle when converting a motion to another type of robot. The proposed method uses a genetic algorithm in order to find the conversion matrix needed to map one robot´s joint angles to joint angles of another robot. This is done with two objectives in mind; one is to reduce the difference between the sample imitation and the converted imitation. The other one is to keep the stability. Two experiments were conducted; one stable and one unstable experiment. The experiments were made with two different types of robots in a simulation environment. The stable experiment showed a concordance rate of 93.7% with the test motion. The imitation also tested with the real robot and succeeded to keep standing. In the unstable experiment, the student robot keeps its balance for most of the simulation time. It showed a concordance rate of 95.5%, which is slightly higher than that in the stable experiment. These results show great promise for the proposed method as a way to realize motion imitation between different types of robots.