16 páginas, 18 figuras. ; 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 work, we introduce the Kinematic Bézier Map (KB-Map), a parametrizable 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. ; The work described in this paper was partially conducted within the EU Cognitive Systems projects IntellAct (FP-7-269959) and GRASP (FP-7-215821) funded by the European Commission. V. Ruiz de Angulo and C. Torras acknowledge support from the Generalitat de Catalunya under the consolidated Robotics group, and from the Spanish Ministry of Science and Education, under the projects DPI2010-18449 and CSD2007-00018, respecively. ; Peer reviewed
Kinematic Bézier maps
2012-08-01
doi:10.1109/TSMCB.2012.2188507
Article (Journal)
Electronic Resource
English
DDC: | 629 |
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