Deriving action and behavior primitives from human motion data

Abstract

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.