BM: An iterative algorithm to learn stable non linear dynamical systems with Gaussian mixture models
We model the dynamics of non linear point to point robot motions as a time independent system described by an autonomous dynamical system (DS) We propose an iterative algorithm to estimate the form of the DS through a mixture of Gaussian distributions. We prove that the resulting model is asymptotically stable at the target. We validate the accuracy of the model on a library of 2D human motions and to learn a control policy through human demonstrations for two multi degrees of freedom robots. We show the real time adaptation to perturbations of the learned model when controlling the two kinematically driven robots.
2010 IEEE International Conference on Robotics and Automation