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Reinforcement Learning Tracking Control for Robotic Manipulator With Kernel-Based Dynamic Model
Hu YZ(胡亚洲)1,2,3; Wang WX(王文学)1,2; Liu, Hao4; Liu LQ(刘连庆)1,2
Department机器人学研究室
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
2020
Volume31Issue:9Pages:3570-3578
Indexed BySCI ; EI
EI Accession number20203809200259
WOS IDWOS:000566342500033
Contribution Rank1
Funding OrganizationNational Key R&D Program of China [2016YFE0206200] ; Key R&D and Technology Transfer Program of Shenyang Science and Technology Plan [18-400-6-16]
KeywordManipulator dynamics Heuristic algorithms Task analysis Kernel Adaptation models Kernel function reinforcement learning (RL) reward function robotics tracking control
Abstract

Reinforcement learning (RL) is an efficient learning approach to solving control problems for a robot by interacting with the environment to acquire the optimal control policy. However, there are many challenges for RL to execute continuous control tasks. In this article, without the need to know and learn the dynamic model of a robotic manipulator, a kernel-based dynamic model for RL is proposed. In addition, a new tuple is formed through kernel function sampling to describe a robotic RL control problem. In this algorithm, a reward function is defined according to the features of tracking control in order to speed up the learning process, and then an RL tracking controller with a kernel-based transition dynamic model is proposed. Finally, a critic system is presented to evaluate the policy whether it is good or bad to the RL control tasks. The simulation results illustrate that the proposed method can fulfill the robotic tracking tasks effectively and achieve similar and even better tracking performance with much smaller inputs of force/torque compared with other learning algorithms, demonstrating the effectiveness and efficiency of the proposed RL algorithm.

Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/27639
Collection机器人学研究室
Corresponding AuthorWang WX(王文学)
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 110169, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.Department of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332 USA
Recommended Citation
GB/T 7714
Hu YZ,Wang WX,Liu, Hao,et al. Reinforcement Learning Tracking Control for Robotic Manipulator With Kernel-Based Dynamic Model[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2020,31(9):3570-3578.
APA Hu YZ,Wang WX,Liu, Hao,&Liu LQ.(2020).Reinforcement Learning Tracking Control for Robotic Manipulator With Kernel-Based Dynamic Model.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,31(9),3570-3578.
MLA Hu YZ,et al."Reinforcement Learning Tracking Control for Robotic Manipulator With Kernel-Based Dynamic Model".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 31.9(2020):3570-3578.
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