Humanoid human-like reaching control based on movement primitives

This paper deals with the problem of generating realistic human-like reaching movements from a small set of movement primitives. Two kinds of movement databases are used as reference. The first one is obtained numerically, by applying biological principles of motor control on the dynamic model of the robot arm. The second one is obtained by recording reaching movements of human subjects. From these databases, primitives are extracted and analyzed by using Principal Component Analysis. An original generalization method is then proposed for generating movements that did not belong to the initial database. We show that twenty primitives allow to produce new movements, having characteristics similar to that of humans. Experiments on the humanoid robot HRP-2 are presented to illustrate the result.