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Motor Skills Learning and Generalization with Adapted Curvilinear Gaussian Mixture Model
Zhang HW(张会文)1,2,3; Leng YQ(冷雨泉)4
Department空间自动化技术研究室
Source PublicationJournal of Intelligent and Robotic Systems: Theory and Applications
ISSN0921-0296
2019
Pages1-19
Indexed ByEI
EI Accession number20191106627388
Contribution Rank1
KeywordSkill learning Cross entropy Curvilinear Gaussian model Imitation learning
AbstractThis 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 compactly. More critically, it is inherently suitable for representing data with strong non-linearity. To infer the parameters of this model, a Cross Entropy Optimization (CEO) algorithm is proposed, where the cross entropy loss of the training data is minimized. Compared with the traditional Expectation Maximization (EM) algorithm, the CEO can automatically infer the optimal number of components. Finally, the generalized trajectories are retrieved by an Adapted Curvilinear Gaussian Mixture Regression (AdC-GMR) model. To encode observations from different frames, the sophisticated task parameterization (TP) technique is introduced. All above proposed algorithms are verified by comprehensive tasks. The CEO is evaluated by a hand writing task. Another goal-directed reaching task is used to evaluate the AdC-GMM and AdC-GMR algorithm. A novel hammer-over-a-nail task is designed to verify the task parameterization technique. Experimental results demonstrate the proposed CEO is superior to the EM in terms of encoding accuracy and the AdC-GMM can achieve more compact representation by reducing the number of components by up to 50%. In addition, the trajectory retrieved by the AdC-GMR is smoother and the approximation error is comparable to the Gaussian process regression (GPR) even far fewer parameters need to be estimated. Because of this, the AdC-GMR is much faster than the GPR. Finally, simulation experiments on the hammer-over-a-nail task demonstrates the proposed methods can be deployed and used in real-world applications.
Language英语
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/24412
Collection空间自动化技术研究室
Corresponding AuthorLeng YQ(冷雨泉)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.Southern University of Science and Technology, Shenzhen Guangdong, China
Recommended Citation
GB/T 7714
Zhang HW,Leng YQ. Motor Skills Learning and Generalization with Adapted Curvilinear Gaussian Mixture Model[J]. Journal of Intelligent and Robotic Systems: Theory and Applications,2019:1-19.
APA Zhang HW,&Leng YQ.(2019).Motor Skills Learning and Generalization with Adapted Curvilinear Gaussian Mixture Model.Journal of Intelligent and Robotic Systems: Theory and Applications,1-19.
MLA Zhang HW,et al."Motor Skills Learning and Generalization with Adapted Curvilinear Gaussian Mixture Model".Journal of Intelligent and Robotic Systems: Theory and Applications (2019):1-19.
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