Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models

被引：567

作者：

Khansari-Zadeh, S. Mohammad ^{[1
]}

Billard, Aude ^{[1
]}

机构：

[1] Ecole Polytech Fed Lausanne, Sch Engn, CH-1015 Lausanne, Switzerland

来源：

IEEE TRANSACTIONS ON ROBOTICS | 2011年 / 27卷 / 05期

关键词：

Dynamical systems (DS); Gaussian mixture model; imitation learning; point-to-point motions; stability analysis;

D O I：

10.1109/TRO.2011.2159412

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

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. Time-invariance 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.

引用

页码：943 / 957

页数：15

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