Reduced-dimension representations of human performance data for human-to-robot skill transfer

Abstract

Despite the large amount of research currently directed toward programming robots by demonstration, a significant problem with this method of human-to-robot skill transfer has not yet been addressed: developing representations of human performances which isolate the intrinsic dimensions of the performances (and thus the skills which guide them) within high-dimensional, raw human performance data. In this paper we propose the use of three methods for representing high-dimensional human performance data within lower-dimensional spaces: principal-component analysis (PCA), nonlinear principal-component analysis (NLPCA), and sequential nonlinear principal-component analysis (SNLPCA). We compare the appropriateness of these methods for modeling a simple human grasping operation.