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.

Published in:
In Proceeding of the International Conference on Robotics and Automation (ICRA 2010), 2381-2388
Presented at:
International Conference on Robotics and Automation (ICRA 2010), Anchorage, Alaska, May 3-8, 2010

 Record created 2010-01-19, last modified 2018-03-17

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