Imitation Learning of Robot Movement Using Evolutionary Algorithm

This paper presents a new framework to generate human-like movement of a humanoid robot in real time using the movement primitive database of a human. The framework consists of two processes: (1) the offline motion imitation learning based on Evolutionary Algorithm and (2) the movement generation of a humanoid robot using the database updated by the motion imitation learning. For the offline process, the initial database contains the kinetic characteristics of a human, since it is full of humans captured motions. The database then develops through the proposed framework of motion learning based on Evolutionary Algorithm, having characteristics of a humanoid in aspect of minimal torque. The humanoid generates a human-like movement corresponding to a given task in real-time by linearly interpolating the primitive movements in the developed database. The proposed framework is a systematic methodology for a humanoid robot to learn human motions, considering the dynamics of the robot. The experiment of catching a ball thrown by a man is performed to show the feasibility of the proposed framework.