Gribovskaya, ElenaBillard, Aude2009-09-142009-09-142009-09-14200910.1109/ICHR.2009.5379536https://infoscience.epfl.ch/handle/20.500.14299/42623We 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 multivariate 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.text::conference output::conference proceedings::conference paper