Movement Primitives, Principal Component Analysis, and the Efficient Generation of Natural Motions

Abstract

We propose a framework for robot movement coordination and learning that combines elements of movement storage, dynamic models, and optimization, with the ultimate objective of efficiently generating natural, human-like motions. One of the novel features of our approach is that each movement primitive is represented and stored as a set of joint trajectory basis functions; these basis functions are extracted via a principal component analysis of human motion capture data. By representing arbitrary movements as a linear combination of these basis functions, and by taking advantage of recently developed geometric optimization algorithms for multibody systems, dynamics-based optimization can be more efficiently performed. Case studies with a diverse set of arm movements demonstrate the feasibility of our approach.