Adding Active Learning to LWR for Ping-Pong Playing Robot

被引：19

作者：

Huang, Yanlong ^{[1
]}

Xu, De ^{[2
]}

Tan, Min ^{[1
]}

Su, Hu ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China

[2] Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China

来源：

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY | 2013年 / 21卷 / 04期

基金：

中国国家自然科学基金; 国家高技术研究发展计划(863计划);

关键词：

Active learning; fuzzy cerebellar model articulation controller (FCMAC); lazy learning; locally weighted regression (LWR); ping-pong playing robot; DESIGN;

D O I：

10.1109/TCST.2012.2208193

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this brief, we consider the problem of controlling the racket attached to the ping-pong playing robot, so that the incoming ball is returned to a desired position. The maps that are used to calculate the racket's initial parameters are described. They are implemented with the locally weighted regression (LWR). An active learning approach based on the fuzzy cerebellar model articulation controller (FCMAC) is proposed, and then it is added to the LWR, which is regarded as lazy learning. A learning algorithm that is used for updating the experience data in the fuzzy CMAC according to the errors between the actual and desired landing positions is presented. A series of experiments has been performed to demonstrate the applicability of the proposed method.

引用

页码：1489 / 1494

页数：6

共 19 条

[1]

Acosta L, 2003, IEEE ROBOT AUTOM MAG, V10, P44, DOI 10.1109/MRA.2003.1256297

[2]

Albus J. S., 1975, Transactions of the ASME. Series G, Journal of Dynamic Systems, Measurement and Control, V97, P220, DOI 10.1115/1.3426922

[3]

Andersson R.L., 1988, ROBOT PING PONG PLAY

[4]

Atkeson CG, 1997, ARTIF INTELL REV, V11, P11, DOI 10.1023/A:1006559212014