BM: An Iterative Method to Learn Stable Non-Linear Dynamical Systems with Gaussian Mixture Models
We model the dynamics of non-linear discrete (i.e. 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.
Khansari_Billard_ICRA10_FinalVersion_1.pdf
openaccess
2.21 MB
Adobe PDF
562559c2ae9e72f45afd0af09465bfea