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Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models
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DOI：10.1109/TRO.2011.2159412.png)
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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.
Keyword:
Dynamical systems (DS)
Gaussian mixture model
imitation learning
point-to-point motions
stability analysis
期刊
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10.5
论文数:
3.3K
被引数:
2.8W
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Learning and Reproduction of Gestures by Imitation An Approach Based on Hidden Markov Model and Gaussian Mixture Regression基于隐马尔可夫模型和高斯混合回归的手势模仿学习与再现方法OAAI
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