Ryan Cosner — accepted AI-conference papers

2023

Robust Safety under Stochastic Uncertainty with Discrete-Time Control Barrier Functions

Ryan Cosner, Preston Culbertson, Andrew Taylor, Aaron Ames

RSS 2023poster

Robots deployed in unstructured, real-world environments operate under considerable uncertainty due to imperfect state estimates, model error, and disturbances. The goal of this paper is to develop controllers that are provably safe under uncertainties. To this end, we leverage Control Barrier Funct…

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2020

Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions

Sarah Dean, Andrew Taylor, Ryan Cosner, Benjamin Recht, Aaron Ames

CoRL 2020

Modern nonlinear control theory seeks to develop feedback controllers that endow systems with properties such as safety and stability. The guarantees ensured by these controllers often rely on accurate estimates of the system state for determining control actions. In practice, measurement model unce

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