Safe learning-based control via function-based uncertainty quantification
Uncertainty quantification is essential when deploying learning-based control methods in safety-critical systems. This is commonly realized by constructing uncertainty tubes that enclose the unknown function of interest, e.g., the reward and constraint functions or the underlying dynamics model, with high probability. However, existing approaches for uncertainty quantification typically rely on restrictive assumptions that encode smoothness properties of the unknown function, such as a known norm in a function space. Moreover, these methods usually struggle with discontinuities. In this paper, we model the unknown function as a random function from which independent and identically distri...
| Authors | Abdullah Tokmak, Toni Karvonen, Thomas B. Schön et al. |
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| Primary category | eess.SY |
| Published | 1 April 2026 |
| Last updated at source | 23 September 2026 |
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