Visual Primitives for Imitation Learning

We present a new method for representing human movement compactly to help in imitation learning. This paper describes a method to represent movement in terms of a linear superimposition of simpler movements termed primitives. If control strategies can be developed that can be easily transformed based on the variation of the contributions of these primitives to the movement, learning can be performed in the space of these primitives. Data from an experiment on human imitation was used to test the theory that movement can be represented as a superimposition of components. PCA performed on segments from these data give us a set of basis vectors. Clustering in the space of projections of segments on to the eigenvectors leads to the often-used movements. Finally, the movement obtained by expanding the cluster points in terms of the eigenvectors is used as a sequence of via points to control Adonis our humanoids test bed.