Learning nonlinear multi-variate motion dynamics for real-time position and orientation control of robotic manipulators

Abstract

We present a generic framework that allows learning non-linear dynamics of motion in manipulation tasks and generating dynamical laws for control of position and orientation. This work follows a recent trend in Programming by Demonstration in which the dynamics of an arm motion is learned: position and orientation control are learned as multi-variate dynamical systems to preserve correlation within the signals. The strength of the method is three-fold: (i) it extracts dynamical control laws from demonstrations, and subsequently provides concurrent smooth control of both position and orientation; (ii) it allows to generalize a motion to unseen context; (iii) it guarantees on-line adaptation of the motion in the face of spatial and temporal perturbations. The method is validated to control a four degree of freedom humanoid arm and an industrial six degree of freedom robotic arm.