A data-efficient geometrically inspired polynomial kernel for robot inverse dynamics

30 Apr 2019Alberto Dalla LiberaRuggero Carli

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 novel kernel, called Geometrically Inspired Polynomial Kernel (GIP)... (read more)

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