Infants gradually improve their grasping competences, both in terms of motor abilities as well as in terms of the internal shape grasp representations. Grasp densities [3] provide a statistical model of such an internal learning process. In the concept of grasp densities, kernel density estimation is used based on a six-dimensional kernel representing grasps with given position and orientation. For this so far an isotropic kernel has been used which exact shape have only been weakly justified. Instead in this paper, we use an anisotropic kernel that is statistically based on measured conditional probabilities representing grasp success in the neighborhood of a successful grasp. The anisotropy has been determined utilizing a simulation environment that allowed for evaluation of large scale experiments. The anisotropic kernel has been fitted to the conditional probabilities obtained from the experiments. We then show that convergence is an important problem associated with the grasp density approach and we propose a measure for the convergence of the densities. In this context, we show that the use of the statistically grounded anisotropic kernels leads to a significantly faster convergence of grasp densities.
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