  In this paper, we present a novel nonparametric motion flow model that
effectively describes a motion trajectory of a human and its application to
human robot cooperation. To this end, motion flow similarity measure which
considers both spatial and temporal properties of a trajectory is proposed by
utilizing the mean and variance functions of a Gaussian process. We also
present a human robot cooperation method using the proposed motion flow model.
Given a set of interacting trajectories of two workers, the underlying reward
function of cooperating behaviors is optimized by using the learned motion
description as an input to the reward function where a stochastic trajectory
optimization method is used to control a robot. The presented human robot
cooperation method is compared with the state-of-the-art algorithm, which
utilizes a mixture of interaction primitives (MIP), in terms of the RMS error
between generated and target trajectories. While the proposed method shows
comparable performance with the MIP when the full observation of human
demonstrations is given, it shows superior performance with respect to given
partial trajectory information.
