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CP-NCBF: A Conformal Prediction-based Approach to Synthesize Verified Neural Control Barrier Functions
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Control Barrier Functions (CBFs) are a practical approach for designing safety-critical controllers, but constructing them for arbitrary nonlinear dynamical systems remains a challenge. Recent efforts have explored learning-based methods, such as neural CBFs (NCBFs), to address this issue. However, ensuring the validity of NCBFs is difficult due to potential learning errors. In this letter, we propose a novel framework that leverages split-conformal prediction to generate formally verified neural CBFs with probabilistic guarantees based on a user-defined error rate, referred to as CP-NCBF. Unlike existing methods that impose Lipschitz constraints on neural CBF-leading to scalability limitations and overly conservative safe sets--our approach is sample-efficient, scalable, and results in less restrictive safety regions. We validate our framework through case studies on obstacle avoidance in autonomous driving and geo-fencing of aerial vehicles, demonstrating its ability to generate larger and less conservative safe sets compared to conventional techniques.
Forward citations
Cited by 7 Pith papers
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Fixed time convergence guarantees for Higher Order Control Barrier Functions
A repeated-root differential constraint for higher-order control barrier functions is proposed to guarantee reaching a safe set within a user-specified fixed time, with second-order formulas and robot simulations.
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CPED-NCBFs: A Conformal Prediction for Expert Demonstration-based Neural Control Barrier Functions
CPED-NCBF uses split-conformal prediction to set safety margins in neural CBFs learned from expert demonstrations, improving simulated safety rates, but the guarantee is weakened by retraining after calibration.
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Safe and Performant Controller Synthesis using Gradient-based Model Predictive Control and Control Barrier Functions
A two-stage controller that uses L-BFGS gradient-based MPC for performance and a CBF-QP filter for hard safety constraints is demonstrated on simulated unicycle and planar quadrotor navigation.
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Safety Certification in the Latent space using Control Barrier Functions and World Models
A semi-supervised framework learns a control barrier certificate in the latent space of a DINO-v2-based world model for safe visuomotor control.
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A Python implementation of a standard vertex-cover branching algorithm is benchmarked against SageMath, but the claimed O(n·1.71^k) complexity is only empirically fitted and the experiments are under-specified.
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