Learning a humanoid kick with controlled distance

Abstract

We investigate the learning of a flexible humanoid robot kick controller, i.e., the controller should be applicable for multiple contexts, such as different kick distances, initial robot position with respect to the ball or both. Current approaches typically tune or optimise the parameters of the biped kick controller for a single context, such as a kick with longest distance or a kick with a specific distance. Hence our research question is that, how can we obtain a flexible kick controller that controls the robot (near) optimally for a continuous range of kick distances? The goal is to find a parametric function that given a desired kick distance, outputs the (near) optimal controller parameters. We achieve the desired flexibility of the controller by applying a contextual policy search method. With such a contextual policy search algorithm, we can generalize the robot kick controller for different distances, where the desired distance is described by a real-valued vector. We will also show that the optimal parameters of the kick controller is a non-linear function of the desired distances and a linear function will fail to properly generalize the kick controller over desired kick distances.