Learning forward models for robots
File(s)
Accepted version
Author(s)
Dearden,A.
Demiris,Y.K.
Type
Conference Paper
Abstract
Forward models enable a robot to predict the effects of its actions on its own motor system and its environment. This is a vital aspect of intelligent behaviour, as the robot can use predictions to decide the best set of actions to achieve a goal. The ability to learn forward models enables robots to be more adaptable and autonomous; this paper describes a system whereby they can be learnt and represented as a Bayesian network. The robot’s motor system is controlled and explored using 'motor babbling'. Feedback about its motor system comes from computer vision techniques requiring no prior information to perform tracking. The learnt forward model can be used by the robot to imitate human movement.
Editor(s)
Kaelbling, LP
Saffiotti, A
Date Issued
2005-08
Citation
2005, pp.1440-1445
ISBN
9780938075936
0-9380-7593-4
Publisher
International Joint Conferences on Artificial Intelligence
Start Page
1440
End Page
1445
Journal / Book Title
19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05)
Copyright Statement
© 2005 International Joint Conference on Artificial Intelligence
Description
29.01.14 KB. Ok to add accepted version to spiral, publisher grants permission.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=000290233000228&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Source
International Joint Conference on Artificial Intelligence (IJCAI)
Source Place
Edinburgh, Scotland
Publication Status
Published
Start Date
2005-07-30
Finish Date
2005-08-05
Coverage Spatial
Edinburgh, Scotland