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Speeding up the learning of robot kinematics

AuthorsRuiz de Angulo, Vicente ; Torras, Carme
KeywordsLearning inverse kinematics
Issue Date2005
PublisherInstitute of Electrical and Electronics Engineers
CitationIEEE Transactions on Neural Networks 16(6): 1504-1512 (2005)
AbstractThe main drawback of using neural networks or other example-based learning procedures to approximate the inverse kinematics (IK) of robot arms is the high number of training samples (i.e., robot movements) required to attain an acceptable precision. We propose here a trick, valid for most industrial robots, that greatly reduces the number of movements needed to learn or relearn the IK to a given accuracy. This trick consists in expressing the IK as a composition of learnable functions, each having half the dimensionality of the original mapping. Off-line and on-line training schemes to learn these component functions are also proposed. Experimental results obtained by using Nearest Neighbours and PSOMs, with and without the decomposition, show that the time savings granted by the proposed scheme grow polynomially with the precision required.
DescriptionAn earlier version of this paper was presented at the 2002 International Conference on Artificial Neural Networks.
Publisher version (URL)http://dx.doi.org/10.1109/TNN.2005.852970
Appears in Collections:(IRII) Artículos
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