A scalable verification framework for neural control barrier functions uses linear bound propagation on network gradients combined with McCormick relaxations to certify safety conditions for control-affine systems.
Discrete control bar- rier functions for safety-critical control of discrete systems with application to bipedal robot naviga- tion
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
A DRL-trained unrolled QP network serves as a model-free safety filter with formal persistent safety guarantees.
Sigmoid-based constraint lifting transforms state-constrained discrete-time nonlinear systems into unconstrained backstepping designs that guarantee asymptotic stability and forward invariance of the safe set.
citing papers explorer
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Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation
A scalable verification framework for neural control barrier functions uses linear bound propagation on network gradients combined with McCormick relaxations to certify safety conditions for control-affine systems.
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Verifiable Model-Free Safety Filters via Reinforcement Learning
A DRL-trained unrolled QP network serves as a model-free safety filter with formal persistent safety guarantees.
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State-Constrained Control of Discrete-Time Nonlinear Systems via Constraint Lifting
Sigmoid-based constraint lifting transforms state-constrained discrete-time nonlinear systems into unconstrained backstepping designs that guarantee asymptotic stability and forward invariance of the safe set.