Dirichlet-based Dynamic Movement Primitives for encoding periodic motions with predefined accuracy

Abstract

In this work, the utilization of Dirichlet (periodic sinc) base functions in DMPs for encoding periodic motions is proposed. By utilizing such kernels, we are able to analytically compute the minimum required number of kernels based only on the predefined accuracy, which is a hyperparameter that can be intuitively selected. The computation of the minimum required number of kernels is based on the frequency content of the demonstrated motion. The learning procedure essentially consists of the sampling of the demonstrated trajectory. The approach is validated through simulations and experiments with the KUKA LWR4+ robot, which show that utilizing the automatically calculated number of basis functions, the pre-defined accuracy is achieved by the proposed DMP model.