English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/30608
logo share SHARE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Learning inverse kinematics: Reduced sampling through decomposition into virtual robots

AuthorsRuiz de Angulo, Vicente ; Torras, Carme
KeywordsFunction approximation
Learning inverse kinematics
Parametrized self-organizing maps (PSOMs)
Robot kinematics
Automatic theorem proving
Issue Date2008
PublisherInstitute of Electrical and Electronics Engineers
CitationIEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 38(6): 1571-1577 (2008)
AbstractWe propose a technique to speed up the learning of the inverse kinematics of a robot manipulator by decomposing it into two or more virtual robot arms. Unlike previous decomposition approaches, this one does not place any requirement on the robot architecture and, thus, it is completely general. Parametrized Self-Organizing Maps (PSOM) are particularly adequate for this type of learning, and permit comparing results obtained directly and through the decomposition. Experimentation shows that time reductions of up to two orders of magnitude are easily attained.
DescriptionAn earlier version of this paper was presented at IWANN-2005.
Publisher version (URL)http://dx.doi.org/10.1109/TSMCB.2008.928232
Appears in Collections:(IRII) Artículos
Files in This Item:
File Description SizeFormat 
Learning inverse kinematics.pdf559,27 kBAdobe PDFThumbnail
Show full item record
Review this work

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.