A novel geometrically inspired polynomial kernel for robot inverse dynamics
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In this paper we introduce a novel data driven inverse dynamics estimator based on Gaussian Process Regression. Driven by the fact that the inverse dynamics can be described as a polynomial function on a suitable input space, we propose the use of a polynomial kernel, based on a set of parameters which is different from the one typically considered in the literature. This novel parametrization allows for an higher flexibility in selecting only the needed information to model the complexity of the problem. We tested the proposed approach in a simulated environment, and also in real experiments with a UR10 robot. The obtained results confirm that, compared to standard data driven estimators, the proposed approach is more data efficient and exhibits better generalization properties.
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