Learning Based Robot Control with Sequential Gaussian Process

被引：0

作者：

Park, Sooho ^{[1
]}

Mustafa, Shabbir Kurbanhusen ^{[2
]}

Shimada, Kenji ^{[1
]}

机构：

[1] Carnegie Mellon Univ, 5000 Forbes Ave, Pittsburgh, PA 15213 USA

[2] Singapore Inst Mfg Technol, \ Singapore 638075, Singapore

来源：

PROCEEDINGS OF THE 2013 IEEE WORKSHOP ON ROBOTIC INTELLIGENCE IN INFORMATIONALLY STRUCTURED SPACE (RIISS) | 2013年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In recent years, robots have started being utilized in applications with complex/unknown interaction environment, which makes system/interface modeling to be very challenging. In order to meet the demand from such applications, the experience based learning approach can be a suitable tool. In this paper, a general algorithm for learning based robot control is presented, and a novel online algorithm using sequential Gaussian process is introduced. As a case study, a simple inverted pendulum is tested to present the capabilities of the proposed algorithm.

引用

页码：120 / 127

页数：8

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