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. ; Preprint
Kinematic Bézier maps
2012-01-01
Article (Journal)
Electronic Resource
English
Artificial intelligence , robot kinematics , Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial , learning (artificial intelligence) robot kinematics robots PARAULES AUTOR: learning , Classificació INSPEC::Cybernetics::Artificial intelligence , Intel·ligència artificial , humanoid robots
DDC: | 629 |
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