Abstract-We model the dynamics of non-linear point-topoint 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 multidegrees of freedom robots. We show the real-time adaptation to perturbations of the learned model when controlling the two kinematically-driven robots.
Billard proposed to model the non-linear dynamical point-to-point robot motions as a time-independent system using an iterative algorithm to estimate the form of the dynamical system through a mixture of Gaussian distributions REF .
BM: An iterative algorithm to learn stable non-linear dynamical systems with Gaussian mixture models
2010 IEEE International Conference on Robotics and Automation
2010 IEEE International Conference on Robotics and Automation
Mathematics Computer Science