A robot demonstration method based on LWR and Q-learning algorithm

被引：4

作者：

Zhao, Guangzhe ^{[1
,2
,3
]}

Tao, Yong ^{[4
]}

Liu, Hui ^{[4
]}

Deng, Xianling ^{[5
]}

Chen, Youdong ^{[4
]}

Xiong, Hegen ^{[6
]}

Xie, Xianwu ^{[6
]}

Fang, Zengliang ^{[4
]}

机构：

[1] Beijing Univ Civil Engn & Architecture, Beijing, Peoples R China

[2] Univ Chinese Acad Sci, Beijing, Peoples R China

[3] Yanbian Univ, Yanji, Peoples R China

[4] Beihang Univ, Beijing 100191, Peoples R China

[5] Chongqing Univ Sci & Technol, Chongqing, Peoples R China

[6] Wuhan Univ Sci & Technol, Wuhan, Hubei, Peoples R China

来源：

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2018年 / 35卷 / 01期

关键词：

Reinforcement learning; Q-learning; locally weighted regression; program by demonstration;

D O I：

10.3233/JIFS-169564

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A robot demonstration method is proposed based on the combination of locally weighted regression (LWR) and Q-learning algorithm. It is applied on a 6-DOF hitting-ball-system. This method can adapt to the work task by learning from demonstration and generating new actions. With the LWR algorithm, the mapping between target values and actions is established. According to deviation of landing position, a Q-learning algorithm is proposed to adjust the parameters of manipulator and compensate the errors caused by model and the controller. The model of LWR fits a local small space to approximate the global state and decision space. It turns out to reduce the dimension and simplify the training of Qlearning. The convergence rate is enhanced and the precision of performing task is improved. The simulation and experiment demonstrate the applicability of the proposed method.

引用

页码：35 / 46

页数：12

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