Paper I. Robust Filtering and Smoothing with Gaussian Processes. Edited version of the paper: M. P. Deisenroth, R. D. Turner, M. F. Huber, U. D. Hanebeck, and C. E. Rasmussen. Robust Filtering and Smoothing with Gaussian Processes. In IEEE Transactions on Automatic Control, vol. 57, no. 7, pages 1865-1871, July 2012

We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and themeasurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing,machine learning, robotics, and control for representing unknown system functions by posterior probability distributions. This modern way of "systemidentification" is more robust than finding point estimates of a parametric function representation. In this article, we present a principled algorithm for robust analytic smoothing in GP dynamic systems, which are increasingly used in robotics and control. ... mehrOur numerical evaluations demonstrate the robustness of the proposed approach in situations where other state-of-the-art Gaussian filters and smoothers can fail.

We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and themeasurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing,machine learning, robotics, and control for representing unknown system functions by posterior probability distributions. This modern way of "systemidentification" is more robust than finding point estimates of a parametric function
representation. In this article, we present a principled algorithm for robust analytic smoothing in GP dynamic systems, which are increasingly used in robotics and control. ... mehrOur numerical evaluations demonstrate the robustness of the proposed approach in situations where other state-of-the-art Gaussian filters and smoothers can fail.