Adaptation of Surrogate Tasks for Bipedal Walk Optimization

Abstract

In many learning and optimization tasks, the sample cost of performing the task is prohibitively expensive or time consuming. Learning is instead often performed on a less expensive task that is believed to be a reasonable approximation or surrogate of the actual target task. This paper focuses on the challenging open problem of performing learning on an approximation of a true target task, while simultaneously adapting the surrogate task used for learning to be a better representation of the true target task. Our work is evaluated in the RoboCup 3D simulation environment where we attempt to learn configuration parameters for an omnidirectional walk engine used by humanoid soccer playing robots.