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J. Ferlez, M. Elnaggar, Y. Shoukry, and C. Fleming, “ShieldNN: A provably safe NN filter for unsafe NN controllers,” 2024. [Online]. Available: https://arxiv.org/abs/2006.09564
Original
2006
Earlier work this paper cites.
J. Kong, M. Pfeiffer, G. Schildbach, and F. Borrelli, “Kinematic and dynamic vehicle models for autonomous driving control design,” in 2015 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2015, pp. 1094–1099. [Online]. Available: http://ieeexplore.ieee.org/document/7225830/
2015
Earlier work this paper cites.
X. Xu, P. Tabuada, J. W. Grizzle, and A. D. Ames, “Robustness of Control Barrier Functions for Safety Critical Control,” IFAC-PapersOnLine , vol. 48, no. 27, pp. 54–61, 2015. [Online]. Available: http://arxiv.org/abs/1612.01554
Original
2015
Earlier work this paper cites.
M. Alshiekh, R. Bloem, R. Ehlers, B. Könighofer, S. Niekum, and U. Topcu. (2017) Safe Reinforcement Learning via Shielding. [Online]. Available: http://arxiv.org/abs/1708.08611
Original
2017
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An open urban driving simulator,” in Proceedings of the 1st Annual Conference on Robot Learning , 2017, pp. 1–16
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 , 2017
Original
2017
Earlier work this paper cites.

Similar

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
2019
Cited alongside, same era.
R. Cheng, G. Orosz, R. M. Murray, and J. W. Burdick, “End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 3387–3395
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Original
2019
Cited alongside, same era.
Z. Marvi and B. Kiumarsi, “Safe reinforcement learning: A control barrier function optimization approach,” International Journal of Robust and Nonlinear Control , vol. 31, no. 6, pp. 1923–1940, 2021
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Then

M. Aali and J. Liu. (2022) Multiple Control Barrier Functions: An Application to Reactive Obstacle Avoidance for a Multi-steering Tractor-trailer System. [Online]. Available: http://arxiv.org/abs/2209.05156
Original
2022
Closest in time.
K.-C. Hsu, H. Hu, and J. F. Fisac, “The safety filter: A unified view of safety-critical control in autonomous systems,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 7, 2023
2023
Closest in time.
M. H. Cohen and C. Belta, “Safe exploration in model-based reinforcement learning using control barrier functions,” Automatica , vol. 147, p. 110684, 2023
2023
Closest in time.
J. Breeden and D. Panagou. (2023) Compositions of Multiple Control Barrier Functions Under Input Constraints. Comment: 9 pages, 7 figures, appearing in 2023 American Control Conference, extended version contains two additional remarks. [Online]. Available: http://arxiv.org/abs/2210.01354
Original
2023
Closest in time.

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2020

ShieldNN: A Provably Safe NN Filter for Unsafe NN Controllers

Ferlez, James, Elnaggar, Mahmoud, Shoukry, Yasser et al.

Understand

In this paper, we develop a novel closed-form Control Barrier Function (CBF) and associated controller shield for the Kinematic Bicycle Model (KBM) with respect to obstacle avoidance.

The proposed CBF and shield -- designed by an algorithm we call ShieldNN -- provide two crucial advantages over existing methodologies.
First, ShieldNN considers steering and velocity constraints directly with the non-affine KBM dynamics; this is in contrast to more general methods, which typically consider only affine dynamics and do not guarantee invariance properties under control constraints.
Second, ShieldNN provides a closed-form set of safe controls for each state unlike more general methods, which typically rely on optimization algorithms to generate a single instantaneous for each state.

Built on

J. Ferlez, M. Elnaggar, Y. Shoukry, and C. Fleming, “ShieldNN: A provably safe NN filter for unsafe NN controllers,” 2024. [Online]. Available: https://arxiv.org/abs/2006.09564
Original
2006
Earlier work this paper cites.
J. Kong, M. Pfeiffer, G. Schildbach, and F. Borrelli, “Kinematic and dynamic vehicle models for autonomous driving control design,” in 2015 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2015, pp. 1094–1099. [Online]. Available: http://ieeexplore.ieee.org/document/7225830/
2015
Earlier work this paper cites.
X. Xu, P. Tabuada, J. W. Grizzle, and A. D. Ames, “Robustness of Control Barrier Functions for Safety Critical Control,” IFAC-PapersOnLine , vol. 48, no. 27, pp. 54–61, 2015. [Online]. Available: http://arxiv.org/abs/1612.01554
Original
2015
Earlier work this paper cites.
M. Alshiekh, R. Bloem, R. Ehlers, B. Könighofer, S. Niekum, and U. Topcu. (2017) Safe Reinforcement Learning via Shielding. [Online]. Available: http://arxiv.org/abs/1708.08611
Original
2017
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An open urban driving simulator,” in Proceedings of the 1st Annual Conference on Robot Learning , 2017, pp. 1–16
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 , 2017
Original
2017
Earlier work this paper cites.

Similar

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
2019
Cited alongside, same era.
R. Cheng, G. Orosz, R. M. Murray, and J. W. Burdick, “End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 3387–3395
2019
Cited alongside, same era.
A. Raffin, A. Hill, K. R. Traoré, T. Lesort, N. Díaz-Rodríguez, and D. Filliat, “Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics,” arXiv preprint arXiv:1901.08651 , 2019
Original
2019
Cited alongside, same era.
Z. Marvi and B. Kiumarsi, “Safe reinforcement learning: A control barrier function optimization approach,” International Journal of Robust and Nonlinear Control , vol. 31, no. 6, pp. 1923–1940, 2021
2021

Then

M. Aali and J. Liu. (2022) Multiple Control Barrier Functions: An Application to Reactive Obstacle Avoidance for a Multi-steering Tractor-trailer System. [Online]. Available: http://arxiv.org/abs/2209.05156
Original
2022
Closest in time.
K.-C. Hsu, H. Hu, and J. F. Fisac, “The safety filter: A unified view of safety-critical control in autonomous systems,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 7, 2023
2023
Closest in time.
M. H. Cohen and C. Belta, “Safe exploration in model-based reinforcement learning using control barrier functions,” Automatica , vol. 147, p. 110684, 2023
2023
Closest in time.
J. Breeden and D. Panagou. (2023) Compositions of Multiple Control Barrier Functions Under Input Constraints. Comment: 9 pages, 7 figures, appearing in 2023 American Control Conference, extended version contains two additional remarks. [Online]. Available: http://arxiv.org/abs/2210.01354
Original
2023
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

L. Brunke, M. Greeff, A. W. Hall, Z. Yuan, S. Zhou, J. Panerati, and A. P. Schoellig, “Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 5, no. 1, pp. 411–444, 2022. [Online]. Available: https://www.annualreviews.org/doi/10.1146/annurev-control-042920-020211

2022

Cited alongside, same era.

S. Gu, L. Yang, Y. Du, G. Chen, F. Walter, J. Wang, and A. Knoll. (2024) A Review of Safe Reinforcement Learning: Methods, Theory and Applications. [Online]. Available: http://arxiv.org/abs/2205.10330

Original

2024

Closest in time.