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Kinematic Bézier maps
Ulbrich, Stefan; Ruiz de Angulo García, Vicente; Torras, Carme; Asfour, Tamim; Dillmann, Rudiger
Universitat Politècnica de Catalunya. Institut de Robòtica i Informàtica Industrial; Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
The kinematics of a robot with many degrees of freedom is a very complex function. Learning this function for a large workspace with a good precision requires a huge number of training samples, i.e., robot movements. In this paper, we introduce the Kinematic Bézier Map (KB-Map), a parameterizable model without the generality of other systems but whose structure readily incorporates some of the geometric constraints of a kinematic function. In this way, the number of training samples required is drastically reduced. Moreover, the simplicity of the model reduces learning to solving a linear least squares problem. Systematic experiments have been carried out showing the excellent interpolation and extrapolation capabilities of KB-Maps and their relatively low sensitivity to noise.
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Artificial intelligence
learning (artificial intelligence) robot kinematics robots PARAULES AUTOR: learning
robot kinematics
humanoid robots
Intel·ligència artificial
Classificació INSPEC::Cybernetics::Artificial intelligence
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Artículo - Borrador
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