Fetching the paper…
Reading the bibliography…
Inspired by the success of imitation and inverse reinforcement learning in replicating expert behavior through optimal control, we propose a learning based approach to safe controller synthesis based on control barrier functions (CBFs).
E. D. Sontag, “A ’universal’ construction of artstein’s theorem on nonlinear stabilization,” Systems & control letters , vol. 13, no. 2, pp. 117–123, 1989
1989
Earlier work this paper cites.
G. Wood and B. Zhang, “Estimation of the lipschitz constant of a function,” Journ. of Global Opt. , vol. 8, no. 1, pp. 91–103, 1996
1996
Earlier work this paper cites.
F. Blanchini, “Set invariance in control,” Automatica , vol. 35, no. 11, pp. 1747–1767, 1999
1999
Earlier work this paper cites.
P. A. Parrilo, “Structured semidefinite programs and semialgebraic geometry methods in robustness and optimization,” Ph.D. dissertation, California Institute of Technology, 2000
2000
Earlier work this paper cites.
S. Prajna, A. Jadbabaie, and G. J. Pappas, “A framework for worst-case and stochastic safety verification using barrier certificates,” IEEE Trans. Autom. Control , vol. 52, no. 8, pp. 1415–1428, 2007
2007
Earlier work this paper cites.
P. Wieland and F. Allgöwer, “Constructive safety using control barrier functions,” in Proc. IFAC Symp. Nonlin. Control Syst. , Pretoria, South Africa, August 2007, pp. 462–467
2007
Earlier work this paper cites.
A. Rahimi and B. Recht, “Random features for large-scale kernel machines,” in Proc. Advances Neur. Inform. Proc. Syst. , Vancouver, Canada, December 2008, pp. 1177–1184
2008
Earlier work this paper cites.
A. D. Ames, J. W. Grizzle, and P. Tabuada, “Control barrier function based quadratic programs with application to adaptive cruise control,” in Proc. Conf. Decis. Control , Los Angeles, CA, December 2014, pp. 6271–6278
2014
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,” in Proc. Conf. Analys. Design Hybrid Syst. , Atlanta, GA, October 2015, pp. 54–61
2015
Earlier work this paper cites.
S. Diamond and S. Boyd, “CVXPY: A Python-embedded modeling language for convex optimization,” Journal of Machine Learning Research , vol. 17, no. 83, pp. 1–5, 2016
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 Trans. Autom. Control , vol. 62, no. 8, pp. 3861–3876, 2017
2017
Cited alongside, same era.
X. Xu, J. W. Grizzle, P. Tabuada, and A. D. Ames, “Correctness guarantees for the composition of lane keeping and adaptive cruise control,” IEEE Transactions on Automation Science and Engineering , vol. 15, no. 3, pp. 1216–1229, 2017
2017
Cited alongside, same era.
U. Rosolia and F. Borrelli, “Learning model predictive control for iterative tasks. a data-driven control framework,” IEEE Trans. Autom. Control , vol. 63, no. 7, pp. 1883–1896, 2017
2017
Cited alongside, same era.
L. Wang, E. A. Theodorou, and M. Egerstedt, “Safe learning of quadrotor dynamics using barrier certificates,” in Proc. Conf. Robot. Automat. , Brisbane, Australia, May 2018, pp. 2460–2465
2018
Cited alongside, same era.
M. Saveriano and D. Lee, “Learning barrier functions for constrained motion planning with dynamical systems,” in Proc. Conf. Intelligent Robots Systems , Macau, China, November 2019
2019
Later among the works it cites.
M. Fazlyab, A. Robey, H. Hassani, M. Morari, and G. Pappas, “Efficient and accurate estimation of lipschitz constants for deep neural networks,” in Proc. Advances Neur. Inform. Proc. Syst. , Vancouver, Canada, December 2019, pp. 11 423–11 434
2019
Later among the works it cites.
S. Kolathaya and A. D. Ames, “Input-to-state safety with control barrier functions,” IEEE Control Systems Letters , vol. 3, no. 1, pp. 108–113, 2019
2019
Later among the works it cites.
M. ApS, The MOSEK optimization toolbox for MATLAB manual. Version 9.0. , 2019. [Online]. Available: http://docs.mosek.com/9.0/toolbox/index.html
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
E. Squires, P. Pierpaoli, and M. Egerstedt, “Constructive barrier certificates with applications to fixed-wing aircraft collision avoidance,” in Proc. Conf. Control Techn. Appl. , Copenhagen, Denmark, August 2018, pp. 1656–1661
2018
Cited alongside, same era.
R. Vershynin, High-dimensional prob.: An introduction with applications in data science . Cambridge university press, 2018, vol. 47
2018
Cited alongside, same era.
A. Virmaux and K. Scaman, “Lipschitz regularity of deep neural networks: analysis and efficient estimation,” in Proc. Advances Neur. Inform. Proc. Syst. , Montréal, Canada, December 2018, pp. 3835–3844
2018
Cited alongside, same era.
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. [Online]. Available: http://github.com/google/jax
2018
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 Proc. European Control Conf. , Naples, Italy, June 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 Proc. Conf. Artificial Intel. , Honolulu, HI, February 2019, pp. 3387–3395
2019
Cited alongside, same era.
L. Wang, D. Han, and M. Egerstedt, “Permissive barrier certificates for safe stabilization using sum-of-squares,” in Proc. American Control Conf. , Milwaukee, WI, June, pp. 585–590
Cited in the paper.
A. Taylor, A. Singletary, Y. Yue, and A. Ames, “Learning for safety-critical control with control barrier functions,” in Proc. Conf. Learning for Dynamics and Control , June 2020, pp. 708–717
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.