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Motivated by the lack of systematic tools to obtain safe control laws for hybrid systems, we propose an optimization-based framework for learning certifiably safe control laws from data.
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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 Proceedings of the Conference on Decision and Control (CDC) , Los Angeles, CA,, December 2014, pp. 6271–6278
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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 Trans. Autom. Control , vol. 62, no. 8, pp. 3861–3876, 2017
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M. Saveriano and D. Lee, “Learning barrier functions for constrained motion planning with dynamical systems,” in IEEE International Conference on Intelligent Robots and Systems , 2019
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M. Fazlyab, A. Robey, H. Hassani, M. Morari, and G. Pappas, “Efficient and accurate estimation of lipschitz constants for deep neural networks,” in Advances in Neural Information Processing Systems , 2019, pp. 11 427–11 438
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R. Tedrake and the Drake Development Team, “Drake: Model-based design and verification for robotics,” 2019. [Online]. Available: https://drake.mit.edu
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2020
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A. Bisoffi and D. V. Dimarogonas, “A hybrid barrier certificate approach to satisfy linear temporal logic specifications,” in 2018 Annual American Control Conference (ACC) . IEEE, 2018, pp. 634–639
2018
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L. Wang, D. Han, and M. Egerstedt, “Permissive barrier certificates for safe stabilization using sum-of-squares,” in 2018 Annual American Control Conference (ACC) . IEEE, 2018, pp. 585–590
2018
Cited alongside, same era.
2019
Cited alongside, same era.
P. Glotfelter, I. Buckley, and M. Egerstedt, “Hybrid nonsmooth barrier functions with applications to provably safe and composable collision avoidance for robotic systems,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 1303–1310, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
M. Ohnishi, L. Wang, G. Notomista, and M. Egerstedt, “Barrier-certified adaptive reinforcement learning with applications to brushbot navigation,” IEEE Transactions on robotics , vol. 35, no. 5, pp. 1186–1205, 2019
2019
Cited alongside, same era.
M. Maghenem and R. G. Sanfelice, “Characterizations of safety in hybrid inclusions via barrier functions,” in Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control , 2019, pp. 109–118
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) , Naples, Italy, June 2019, pp. 3420–3431
2019
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2020
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2020
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2020
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J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, and S. Wanderman-Milne, “Jax: composable transformations of python+ numpy programs, 2018,” URL http://github. com/google/jax , p. 18, 2020
2020
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