A particle-based probabilistic CBF framework derives finite-sample safety certificates for Gaussian state estimation uncertainty by showing that barrier increments remain sub-Gaussian under Lipschitz control-affine dynamics.
Robust control barrier functions for constrained sta- bilization of nonlinear systems
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
A new robust Q-CBF framework synthesized via adversarial RL enables safety enforcement on the maximal robust safe set for black-box nonlinear systems.
Introduces boundary-focused rollouts to screen the smoothing parameter κ and augments a discrete-time CBF with a fixed robust margin to eliminate contact force violations in smoothed implicit dynamics simulations.
citing papers explorer
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Probabilistic Control Barrier Functions for Systems with State Estimation Uncertainty using Sub-Gaussian Concentration
A particle-based probabilistic CBF framework derives finite-sample safety certificates for Gaussian state estimation uncertainty by showing that barrier increments remain sub-Gaussian under Lipschitz control-affine dynamics.
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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.
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Safety-Critical Control for Smoothed Implicit Contact Dynamics
Introduces boundary-focused rollouts to screen the smoothing parameter κ and augments a discrete-time CBF with a fixed robust margin to eliminate contact force violations in smoothed implicit dynamics simulations.