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This paper focuses on learning a model of system dynamics online while satisfying safety constraints.Our motivation is to avoid offline system identification or hand-specified dynamics models and allowa system to safely and autonomously estimate and adapt its own model during online operation.Given streaming observations of the system state, we use Bayesian learning to obtain a distributionover the system dynamics.
Linear models
Shayle R Searle and Marvin HJ Gruber · 1971
Earlier work this paper cites.
Nonlinear systems; 3rd ed
Hassan K Khalil · 2002
Earlier work this paper cites.
Posterior consistency of Gaussian process prior for nonparametric binary regression
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Earlier work this paper cites.
Gaussian processes for machine learning , volume 2
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Kernels for vector-valued functions: A review
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