A new robust Q-CBF framework synthesized via adversarial RL enables safety enforcement on the maximal robust safe set for black-box nonlinear systems.
MAGICS: Adversarial RL with Minimax Actors Guided by Implicit Critic Stackelberg for Convergent Neural Synthesis of Robot Safety,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SY 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.