Method for optimizing a policy for a robot

Abstract

A method for optimizing a predefined policy for a robot, the policy being a Gaussian mixture model. The method begins with an initialization of a Gaussian process, the Gaussian process including at least one kernel k which, as an input parameter, obtains a distance that is ascertained between probability distributions, which are characterized in each case by the Gaussian mixture model and the Gaussian process, according to the probability product kernel. This is followed by an optimization of the Gaussian process in such a way that it predicts the costs as a function of the parameters of the Gaussian mixture model. This is followed by an ascertainment of optimal parameters of the Gaussian mixture model as a function of the Gaussian process, the parameters being selected, as a function of the Gaussian process, in such a way that the Gaussian process outputs the optimal cost function.