Exemplar-based primitives for humanoid movement classification and control

We present a unified methodology for humanoid robot control and activity classification using motor primitives [1], computationally efficient behaviors capable of perception and control. These primitives constitute a vocabulary for humanoid control capable of generating a large variety of complex movement through sequencing and superposition. We demonstrate how such primitives can be automatically derived from human motion-capture data, how they can be used to construct upperbody controllers, and how they can be applied to classification of observed humanoid behavior in real time.