A new robust Q-CBF framework synthesized via adversarial RL enables safety enforcement on the maximal robust safe set for black-box nonlinear systems.
Verification of neural reachable tubes via sce- nario optimization and conformal prediction,
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Synthesis and Deployment of Maximal Robust Control Barrier Functions through Adversarial Reinforcement Learning
A new robust Q-CBF framework synthesized via adversarial RL enables safety enforcement on the maximal robust safe set for black-box nonlinear systems.