Motor Skills Learning and Generalization with Adapted Curvilinear Gaussian Mixture Model

This paper is intended to solve the motor skills learning, representation and generalization problems in robot imitation learning. To this end, we present an Adapted Curvilinear Gaussian Mixture Model (AdC-GMM) , which is a general extension of the GMM. The proposed model can encode data more compac...

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Vydáno v:	Journal of intelligent & robotic systems Ročník 96; číslo 3-4; s. 457 - 475
Hlavní autoři:	Zhang, Huiwen, Leng, Yuquan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.12.2019 Springer Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Comparative analysis Computer simulation Control Economic models Electrical Engineering Engineering Entropy (Information theory) Gaussian process Gaussian processes Hammers Learning Linearity Mechanical Engineering Mechatronics Motor ability Optimization Parameter estimation Parameterization Parameters Probabilistic models Regression models Representations Robot learning Robotics Robots Simulation Skills Trajectories Cross entropy Imitation learning Curvilinear Gaussian model Skill learning
ISSN:	0921-0296, 1573-0409
On-line přístup:	Získat plný text
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