Applying evolution strategies for biped locomotion learning in RoboCup 3D Soccer Simulation

This paper addresses parameter tuning methods for bipedal locomotion of a humanoid model in the RoboCup 3D Soccer Simulation environment. A gait pattern of this humanoid is generated by a desired foot trajectory, joint control systems and nonlinear oscillators. To build a good gait pattern, the parameters of the walking system should be adjusted suitably. In this paper, a usage of evolution strategies that is depending on only a performance evaluation of the robot, is considered for adjusting the parameters. We apply two type evolution strategies in order to tune the parameters. The one is an evolution strategy with mask operation where the portion of individual to avoid mutation. The other is a covariance matrix adaptation evolution strategy. Numerical simulation studies are carried out to evaluate the performance of the proposed approaches by using the RoboCup 3D Soccer Simulator.