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This paper addresses learning safe output feedback control laws from partial observations of expert demonstrations.
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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
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L. Wang, D. Han, and M. Egerstedt, “Permissive barrier certificates for safe stabilization using sum-of-squares,” in Proc. Am. Control Conf. , Milwaukee, WI, June 2018, pp. 585–590
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W. Xiao and C. Belta, “Control barrier functions for systems with high relative degree,” in Proc. Conf. Decis. Control , Nice, France, December 2019, pp. 474–479
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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
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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 Proc. European Control Conf. , Naples, Italy, June 2019, pp. 3420–3431
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L. Chamon and A. Ribeiro, “Probably approximately correct constrained learning,” Advances in Neural Information Processing Systems , vol. 33, pp. 16 722–16 735, 2020
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J. J. Choi, D. Lee, K. Sreenath, C. J. Tomlin, and S. L. Herbert, “Robust control barrier–value functions for safety-critical control,” in 2021 60th IEEE Conference on Decision and Control (CDC) . IEEE, 2021, pp. 6814–6821
2021
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P. Seiler, M. Jankovic, and E. Hellstrom, “Control barrier functions with unmodeled input dynamics using integral quadratic constraints,” IEEE Control Systems Letters , vol. 6, pp. 1664–1669, 2021
2021
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2021
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M. Saveriano and D. Lee, “Learning barrier functions for constrained motion planning with dynamical systems,” in Proc. Conf. Intel. Robot Syst. , Macau, China, November 2019, pp. 112–119
2019
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2019
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S. Dean, A. J. Taylor, R. K. Cosner, B. Recht, and A. D. Ames, “Guaranteeing safety of learned perception modules via measurement-robust control barrier functions,” in Proc. Conf. Robot Learning , Boston, Massachusetts, November 2020, pp. 1–17
2020
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2020
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B. T. Lopez, J.-J. E. Slotine, and J. P. How, “Robust adaptive control barrier functions: An adaptive and data-driven approach to safety,” IEEE Control Syst. Lett. , vol. 5, no. 3, pp. 1031–1036, 2020
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A. J. Taylor and A. D. Ames, “Adaptive safety with control barrier functions,” in Proc. Am. Control Conf. , Denver, CO, July 2020, pp. 1399–1405
2020
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A. Taylor, A. Singletary, Y. Yue, and A. Ames, “Learning for safety-critical control with control barrier functions,” in Proc. Conf. Learning Dynamics Control , San Francisco, CA, June 2020, pp. 708–717
2020
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W. S. Cortez and D. V. Dimarogonas, “Correct-by-design control barrier functions for euler-lagrange systems with input constraints,” in Proc. Am. Control Conf. , Denver, CO, July 2020, pp. 950–955
2020
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2021
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K. Garg and D. Panagou, “Robust control barrier and control lyapunov functions with fixed-time convergence guarantees,” in Proc. Am. Control Conf. , New Orleans, LA, May 2021, pp. 2292–2297
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N. Csomay-Shanklin, R. K. Cosner, M. Dai, A. J. Taylor, and A. D. Ames, “Episodic learning for safe bipedal locomotion with control barrier functions and projection-to-state safety,” in Proc. Conf. Learning Dynamics Control , Zurich, Switzerland, June 2021, pp. 1041–1053
2021
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H. Yin, P. Seiler, M. Jin, and M. Arcak, “Imitation learning with stability and safety guarantees,” IEEE Control Syst. Lett. , 2021
2021
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M. Ohnishi, G. Notomista, M. Sugiyama, and M. Egerstedt, “Constraint learning for control tasks with limited duration barrier functions,” Automatica , vol. 127, p. 109504, 2021
2021
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K. Long, C. Qian, J. Cortés, and N. Atanasov, “Learning barrier functions with memory for robust safe navigation,” IEEE Robot. Autom. Lett. , vol. 6, no. 3, pp. 4931–4938, 2021
2021
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S. Chen, M. Fazlyab, M. Morari, G. J. Pappas, and V. M. Preciado, “Learning lyapunov functions for hybrid systems,” in Proc. Conf. Hybrid Syst.: Comp. Control , Nashville, TN, May 2021, pp. 1–11
2021
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A. Abate, D. Ahmed, A. Edwards, M. Giacobbe, and A. Peruffo, “FOSSIL: a software tool for the formal synthesis of lyapunov functions and barrier certificates using neural networks,” in Proc. Conf. Hybrid Syst.: Comp. Control , Nashville, TN, May 2021, pp. 1–11
2021
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A. Robey, L. Lindemann, S. Tu, and N. Matni, “Learning robust hybrid control barrier functions for uncertain systems,” in Proc. Conf. Anal. Design Hybrid Syst. , Brussels, Belgium, July 2021, pp. 1–6
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C. Dawson, Z. Qin, S. Gao, and C. Fan, “Safe nonlinear control using robust neural lyapunov-barrier functions,” in Conference on Robot Learning . PMLR, 2022, pp. 1724–1735
2022
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Y. S. Quan, J. S. Kim, S.-H. Lee, and C. C. Chung, “Tube-based control barrier function with integral quadratic constraints for unknown input delay,” IEEE Control Systems Letters , 2023
2023
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