The paper derives a finite-sample complexity bound for sampling Gaussian process dynamics and uses it to build a recursively feasible, safety-guaranteed model predictive controller.
Bayesian time series learning with Gaussian processes
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Finite-Sample-Based Reachability for Safe Control with Gaussian Process Dynamics
The paper derives a finite-sample complexity bound for sampling Gaussian process dynamics and uses it to build a recursively feasible, safety-guaranteed model predictive controller.