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.


learning (artificial intelligence), robot kinematics, robots.

Author keywords

learning, robot kinematics, humanoid robots

Scientific reference

S. Ulbrich, V. Ruiz de Angulo, C. Torras, T. Asfour and R. Dillman. Kinematic Bézier maps. IEEE Transactions on Systems, Man and Cybernetics: Part B, 42(4): 1215-1230, 2012.