A new robust Q-CBF framework synthesized via adversarial RL enables safety enforcement on the maximal robust safe set for black-box nonlinear systems.
Safety with Agency: Human-Centered Safety Filter with Application to AI-Assisted Motorsports,
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.