




















Abstract:Formal verification of neural control barrier functions (NCBFs) remains challenging, especially for neural networks with nonlinear activations like \(\tanh\). Existing CROWN-based methods rely on conservative linear relaxations for Jacobian bounds, limiting scalability. We propose LightCROWN, which computes tighter Jacobian bounds by exploiting the analytical properties of activation functions. Experiments on nonlinear control systems including the inverted pendulum, Dubins car, and planar quadrotor demonstrate that LightCROWN improves verification success rates up to 100\%, while enhancing speed and scalability. Our approach provides a generalizable improvement for CROWN-based frameworks, enabling more efficient verification of complex NCBFs. The code can be found at this http URL.
| Comments: | 9 pages, 4 figures |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.07757 [cs.LG] |
| (or arXiv:2605.07757v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.07757 arXiv-issued DOI via DataCite (pending registration) |
From: Zhang Jun [view email]
[v1]
Fri, 8 May 2026 13:59:53 UTC (235 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。