Deriving action and behavior primitives from human motion data

We address the problem of creating basis behaviors for modularizing humanoid robot control and representing human activity. These behaviors, called perceptual-motor primitives, serve as a substrate for linking a system's perception of human activities and the ability to perform those activities. We present a data-driven method for deriving perceptual-motor action and behavior primitives from human motion capture data. In order to find these primitives, we employ a spatio-temporal non-linear dimension reduction technique on a set of motion segments. From this transformation, motions representing the same action can be clustered and generalized. Further dimension reduction iterations are applied to derive extended-duration behaviors.