Safety on the Fly: Constructing Robust Safety Filters via Policy Control Barrier Functions at Runtime
L. Knödler (TU Delft - Learning & Autonomous Control)
Oswin So (Massachusetts Institute of Technology)
Ji Yin (Georgia Institute of Technology)
Mitchell Black (MIT Lincoln Laboratory)
Zachary Serlin (MIT Lincoln Laboratory)
Panagiotis Tsiotras (Georgia Institute of Technology)
J. Alonso-Mora (TU Delft - Learning & Autonomous Control)
Chuchu Fan (Massachusetts Institute of Technology)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a difficult problem. In this work, we propose the Robust Policy CBF (RPCBF), a practical approach for constructing robust CBF approximations online via the estimation of a value function. We establish conditions under which the approximation qualifies as a valid CBF and demonstrate the effectiveness of the RPCBF-safety filter in simulation on a variety of high relative degree input-constrained systems. Finally, we demonstrate the benefits of our method in compensating for model errors on a hardware quadcopter platform by treating the model errors as disturbances.
Files
File under embargo until 11-02-2026