Imitation and motion classification  A model of movement
imitation and implementation for a simulated humanoid to imitate
the behavior of a human performer. The tracking data used for
this implementation were obtained using a 2.5D upper body video-
based pose tracking system. The attention mechanism in this
system focuses on the locations of the endpoints (i.e. hands).
In place of a learned set of primitives, a human subject
performed a sequence of motions, including line, circle, and arc
trajectories of the endpoints, to yield a set movements that
serve as a set of perceptual-motor primitives. Using these
primitives, we implemented a vector quantization based
classification mechanism. With postprocessing of the
classification results, the classifier provides a desired via-
point trajectory for each arm endpoint. These trajectories are
then actuated using impedance control on our 20 DOF humanoid
simulation, Adonis.