1 paper accepted to CDC

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Our paper on recurrent control barrier functions [1] has been accepted to the 64th IEEE Conference on Decision and Control. Congrats Jixian!

[1] [doi] J. Liu and E. Mallada, “Recurrent Control Barrier Functions: A Path Towards Nonparametric Safety Verification,” in 64th IEEE Conference on Decision and Control (CDC), 2025.
[Bibtex] [Abstract] [Download PDF]

Ensuring the safety of complex dynamical systems often relies on Hamilton-Jacobi (HJ) Reachability Analysis or Control Barrier Functions (CBFs). Both methods require computing a function that characterizes a safe set that can be made (control) invariant. However, the computational burden of solving high-dimensional partial differential equations (for HJ Reachability) or large-scale semidefinite programs (for CBFs) makes finding such functions challenging. In this paper, we introduce the notion of Recurrent Control Barrier Functions (RCBFs), a novel class of CBFs that leverages a recurrent property of the trajectories, i.e., coming back to a safe set, for safety verification. Under mild assumptions, we show that the RCBF condition holds for the signed-distance function, turning function design into set identification. Notably, the resulting set need not be invariant to certify safety. We further propose a data-driven nonparametric method to compute safe sets that is massively parallelizable and trades off conservativeness against computational cost.

@inproceedings{lm2025cdc,
  abstract = {Ensuring the safety of complex dynamical systems often relies on Hamilton-Jacobi (HJ) Reachability Analysis or Control Barrier Functions (CBFs). Both methods require computing a function that characterizes a safe set that can be made (control) invariant. However, the computational burden of solving high-dimensional partial differential equations (for HJ Reachability) or large-scale semidefinite programs (for CBFs) makes finding such functions challenging. In this paper, we introduce the notion of Recurrent Control Barrier Functions (RCBFs), a novel class of CBFs that leverages a recurrent property of the trajectories, i.e., coming back to a safe set, for safety verification. Under mild assumptions, we show that the RCBF condition holds for the signed-distance function, turning function design into set identification. Notably, the resulting set need not be invariant to certify safety. We further propose a data-driven nonparametric method to compute safe sets that is massively parallelizable and trades off conservativeness against computational cost.},
  author = {Liu, Jixian and Mallada, Enrique},
  booktitle = {64th IEEE Conference on Decision and Control (CDC)},
  doi = {10.1109/CDC57313.2025.11312572},
  grants = {CPS-2136324; Global-Centers-2330450},
  month = {12},
  record = {presented Dec. 2025, accepted Jul. 2025, submitted Mar. 2025},
  title = {Recurrent Control Barrier Functions: A Path Towards Nonparametric Safety Verification},
  url = {https://mallada.ece.jhu.edu/pubs/2025-CDC-LM.pdf},
  year = {2025}
}