Human movement learning with dynamic movement primitives combined with mixture models

Abstract

The proposed research is to provide a probabilistic approach to learn human movements. Dynamical Movement Primitives (DMP) have been extensively used in robotics in order to learn human motions [1]. The DMP modulates a virtual spring with a learned non-linear force profile /(x), perturbing the system to make it follow a desired trajectory.