Abstract-This paper presents a method to learn discrete robot motions from a set of demonstrations. We model a motion as a nonlinear autonomous (i.e., time-invariant) dynamical system (DS) and define sufficient conditions to ensure global asymptotic stability at the target. We propose a learning method, which is called Stable Estimator of Dynamical Systems (SEDS), to learn the parameters of the DS to ensure that all motions closely follow the demonstrations while ultimately reaching and stopping at the target. Timeinvariance and global asymptotic stability at the target ensures that the system can respond immediately and appropriately to perturbations that are encountered during the motion. The method is evaluated through a set of robot experiments and on a library of human handwriting motions. Index Terms-Dynamical systems (DS), Gaussian mixture model, imitation learning, point-to-point motions, stability analysis.
al. REF introduce the Stable Estimator of Dynamical Systems (SEDS) approach.
Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models
IEEE Transactions on Robotics
IEEE Transactions on Robotics
Mathematics Computer Science