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Control barrier functions (CBFs) have become a popular tool to enforce safety of a control system.
W. Xiao, C. A. Belta, and C. G. Cassandras, “Feasibility-guided learning for constrained optimal control problems,” in 2020 59th IEEE Conference on Decision and Control (CDC) , 2020, pp. 1896–1901
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H. K. Khalil, “Nonlinear systems,” 2002
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I. Cizelj, X. C. D. Ding, M. Lahijanian, A. Pinto, and C. Belta, “Probabilistically safe vehicle control in a hostile environment,” IFAC Proceedings Volumes , vol. 44, no. 1, pp. 11 803–11 808, 2011
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A. D. Ames, J. W. Grizzle, and P. Tabuada, “Control barrier function based quadratic programs with application to adaptive cruise control,” in 53rd IEEE Conference on Decision and Control . IEEE, 2014
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S.-C. Hsu, X. Xu, and A. D. Ames, “Control barrier function based quadratic programs with application to bipedal robotic walking,” in 2015 American Control Conference (ACC) . IEEE, 2015, pp. 4542–4548
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2015
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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 , vol. 62, pp. 3861–3876, 2016
2016
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2016
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L. Wang, A. D. Ames, and M. Egerstedt, “Multi-objective compositions for collision-free connectivity maintenance in teams of mobile robots,” in 2016 IEEE 55th Conference on Decision and Control (CDC) . IEEE, 2016, pp. 2659–2664
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B. Amos and J. Z. Kolter, “Optnet: Differentiable optimization as a layer in neural networks,” in International Conference on Machine Learning . PMLR, 2017, pp. 136–145
2017
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S. Bansal, M. Chen, S. Herbert, and C. J. Tomlin, “Hamilton-jacobi reachability: A brief overview and recent advances,” in 2017 IEEE 56th Annual Conference on Decision and Control (CDC) . IEEE, 2017, pp. 2242–2253
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B. Amos, I. Jimenez, J. Sacks, B. Boots, and J. Z. Kolter, “Differentiable mpc for end-to-end planning and control,” Advances in Neural Information Processing Systems , vol. 31, pp. 8289–8300, 2018
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Y. Chow, O. Nachum, E. Duenez-Guzman, and M. Ghavamzadeh, “A lyapunov-based approach to safe reinforcement learning,” in Proceedings of the 32nd International Conference on Neural Information Processing Systems , 2018, pp. 8103–8112
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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) . IEEE, 2019, pp. 3420–3431
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K. Lee, S. Maji, A. Ravichandran, and S. Soatto, “Meta-learning with differentiable convex optimization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 657–10 665
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2021
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W. Jin, S. Mou, and G. Pappas, “Safe pontryagin differentiable programming,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
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A. Majumdar, A. Farid, and A. Sonar, “Pac-bayes control: learning policies that provably generalize to novel environments,” The International Journal of Robotics Research , vol. 40, no. 2-3, pp. 574–593, 2021
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J. Choi, F. Castañeda, C. Tomlin, and K. Sreenath, “Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions,” in Proceedings of Robotics: Science and Systems , Corvalis, Oregon, USA, July 2020
2020
Cited alongside, same era.
P. L. Donti, M. Roderick, M. Fazlyab, and J. Z. Kolter, “Enforcing robust control guarantees within neural network policies,” in International Conference on Learning Representations , 2020
2020
Cited alongside, same era.
2020
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A. Robey, H. Hu, L. Lindemann, H. Zhang, D. V. Dimarogonas, S. Tu, and N. Matni, “Learning control barrier functions from expert demonstrations,” in 2020 59th IEEE Conference on Decision and Control (CDC) . IEEE, 2020, pp. 3717–3724
2020
Cited alongside, same era.
U. Rosolia and A. D. Ames, “Multi-rate control design leveraging control barrier functions and model predictive control policies,” IEEE Control Systems Letters , vol. 5, no. 3, pp. 1007–1012, 2020
2020
Cited alongside, same era.
A. Taylor, A. Singletary, Y. Yue, and A. Ames, “Learning for safety-critical control with control barrier functions,” in Learning for Dynamics and Control . PMLR, 2020, pp. 708–717
2020
Cited alongside, same era.
J. Zhang, B. Cheung, C. Finn, S. Levine, and D. Jayaraman, “Cautious adaptation for reinforcement learning in safety-critical settings,” in International Conference on Machine Learning . PMLR, 2020, pp. 11 055–11 065
2020
Cited alongside, same era.
C. Dawson, Z. Qin, S. Gao, and C. Fan, “Safe nonlinear control using robust neural lyapunov-barrier functions,” in 5th Annual Conference on Robot Learning , 2021
2021
Cited alongside, same era.
E. Scukins and P. Ögren, “Using reinforcement learning to create control barrier functions for explicit risk mitigation in adversarial environments,” in IEEE International Conference on Robotics and Automation (ICRA) . IEEE Robotics and Automation Society, 2021
2021
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2021
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2021
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W. Xiao and C. Belta, “High order control barrier functions,” IEEE Transactions on Automatic Control , 2021
2021
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J. Zeng, B. Zhang, Z. Li, and K. Sreenath, “Safety-critical control using optimal-decay control barrier function with guaranteed point-wise feasibility,” in 2021 American Control Conference (ACC) , 2021, pp. 3856–3863
2021
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J. Zeng, B. Zhang, and K. Sreenath, “Safety-critical model predictive control with discrete-time control barrier function,” in 2021 American Control Conference (ACC) . IEEE, 2021, pp. 3882–3889
2021
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W. Zhao, T. He, and C. Liu, “Model-free safe control for zero-violation reinforcement learning,” in 5th Annual Conference on Robot Learning , 2021
2021
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F. S. Barbosa, L. Lindemann, D. V. Dimarogonas, and J. Tumova, “Provably safe control of lagrangian systems in obstacle-scattered environments,” in 2020 59th IEEE Conference on Decision and Control (CDC) , 2020, pp. 2056–2061
2061
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