Learning to adapt: A network and learning algorithm for flexible robotic control

被引：0

作者：

Earon, EJP ^{[1
]}

D'Eleuterio, GMT ^{[1
]}

机构：

[1] Univ Toronto, Inst Aerosp Studies, Space Robot Res Grp, Toronto, ON, Canada

来源：

PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS 2003, VOL 1-3 | 2003年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

One of the final hurdles to the widespread integration of robotic systems is the lack of true robotic autonomy. To this end a network structure, a Behaviour Network (BNet) and learning algorithm for the control of autonomous robotics is presented. The goal of this network learning algorithm is to enable flexible, adaptable control of autonomous systems, and in particular, robotic systems. The flexibility of the networks arises from its ability to learn online and adjust both architecture and function. Also, the network is designed with the goal of enveloping it within a three-fold approach to controller development in mind.

引用

页码：447 / 452

页数：6

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