Synthesis and Deployment of Maximal Robust Control Barrier Functions through Adversarial Reinforcement Learning

arXiv:2604.13192 · eess.SY, cs.RO, cs.SY · Submitted 2026-04-14 · Read on arXiv

eess.SY, cs.RO, cs.SY

Submitted: 2026-04-14

Updated: 2026-09-17

Comments: 8 pages, 2 figures. Accepted to the 65th IEEE Conference on Decision and Control (CDC)

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reinforcement Learning — 7× in this paper · explained in 4 episodes
• Actor — 6× in this paper · explained in 3 episodes
• Conference on Decision and Control — 5× in this paper
• Verification — 5× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Control Barrier Functions (CBFs) — 3× in this paper · explained in 4 episodes
• Reinforcement Learning (RL) — 3× in this paper · explained in 25 episodes
• Action Space — 2× in this paper · explained in 1 episode
• Conformal Prediction — 2× in this paper · explained in 5 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Q-learning — 1× in this paper · explained in 3 episodes

Sources

• Viscosity CBFs: Bridging the Control Barrier Function and Hamilton-Jacobi Reachability Frameworks in Safe Control Theory
• Provably Optimal Reinforcement Learning under Safety Filtering
• OptimizedDP: An Efficient, User-friendly Library For Optimal Control and Dynamic Programming

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