Simultaneous Synthesis and Verification of Neural Control Barrier Functions through Branch-and-Bound Verification-in-the-loop Training

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural networks

1989

Earlier work this paper cites.

I. M. Mitchell et al

2007

Earlier work this paper cites.

A. A. Ahmadi and A. Majumdar, “Some Applications of Polynomial Optimization in Operations Research and Real-Time Decision Making,” arXiv e-prints

Original

2015

Earlier work this paper cites.

Q. Nguyen, A. Hereid, J. W. Grizzle, A. D. Ames, and K. Sreenath, “3d dynamic walking on stepping stones with control barrier functions,” in 2016 IEEE 55th Conference on Decision and Control (CDC)

2016

Earlier work this paper cites.

A. D. Ames, X. Xu, J. W. Grizzle, and P. Tabuada, “Control barrier function based quadratic programs for safety critical systems,” IEEE Transactions on Automatic Control

2016

Earlier work this paper cites.

X. Xu, T. Waters, D. Pickem, P. Glotfelter, M. Egerstedt, P. Tabuada, J. W. Grizzle, and A. D. Ames, “Realizing simultaneous lane keeping and adaptive speed regulation on accessible mobile robot testbeds,” in 2017 IEEE Conference on Control Technology and Applications (CCTA)

2017

Earlier work this paper cites.

J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347

Original

2017

Earlier work this paper cites.

H. Zhang, T.-W. Weng, P.-Y. Chen, C.-J. Hsieh, and L. Daniel, “Efficient neural network robustness certification with general activation functions,” Advances in neural information processing systems

2018

Earlier work this paper cites.

L. Weng, H. Zhang, H. Chen, Z. Song, C.-J. Hsieh, L. Daniel, D. Boning, and I. Dhillon, “Towards fast computation of certified robustness for relu networks,” in International Conference on Machine Learning

2018

Earlier work this paper cites.

A. Abate, C. David, P. Kesseli, D. Kroening, and E. Polgreen, “Counterexample guided inductive synthesis modulo theories,” in International Conference on Computer Aided Verification

2018

Earlier work this paper cites.

Y.-C. Chang, N. Roohi, and S. Gao, “Neural lyapunov control,” in Advances in Neural Information Processing Systems

2019

Cited alongside, same era.

A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada, “Control barrier functions: Theory and applications,” in 2019 18th European Control Conference (ECC)

2019

Cited alongside, same era.

J. F. Fisac, N. F. Lugovoy, V. Rubies-Royo, S. Ghosh, and C. J. Tomlin, “Bridging hamilton-jacobi safety analysis and reinforcement learning,” in 2019 International Conference on Robotics and Automation (ICRA)

2019

Cited alongside, same era.

M. Srinivasan, A. Dabholkar, S. Coogan, and P. A. Vela, “Synthesis of control barrier functions using a supervised machine learning approach,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2020

Cited alongside, same era.

B. Dai, P. Krishnamurthy, and F. Khorrami, “Learning a better control barrier function,” in 2022 IEEE 61st Conference on Decision and Control (CDC)

2022

Later among the works it cites.

F. B. Mathiesen, S. C. Calvert, and L. Laurenti, “Safety certification for stochastic systems via neural barrier functions,” IEEE Control Systems Letters

2022

Later among the works it cites.

Z. Shi, Y. Wang, H. Zhang, J. Z. Kolter, and C.-J. Hsieh, “Efficiently computing local lipschitz constants of neural networks via bound propagation,” Advances in Neural Information Processing Systems

2022

Later among the works it cites.

J. Laurel, R. Yang, G. Singh, and S. Misailovic, “A dual number abstraction for static analysis of clarke jacobians,” Proceedings of the ACM on Programming Languages

2022

Later among the works it cites.

M. Streeter and J. V. Dillon, “Automatically bounding the taylor remainder series: Tighter bounds and new applications,” arXiv preprint arXiv:2212.11429