mlsanityData to 14 Oct 2026

Learning Robust Hybrid Control Barrier Functions for Uncertain Systems

arXiv 2101.06492 ↗First posted 16 Jan 2021eess.SY cs.LG cs.SYcited by 14

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Abstract (arXiv)

The need for robust control laws is especially important in safety-critical applications. We propose robust hybrid control barrier functions as a means to synthesize control laws that ensure robust safety. Based on this notion, we formulate an optimization problem for learning robust hybrid control barrier functions from data. We identify sufficient conditions on the data such that feasibility of the optimization problem ensures correctness of the learned robust hybrid control barrier functions. Our techniques allow us to safely expand the region of attraction of a compass gait walker that is subject to model uncertainty.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 18 of its 30 references that are in the map. First posted on arXiv 16 Jan 2021.

Builds on

18 of 30 references.

References · 18

  1. 2019Control Barrier Functions: Theory and Applicationscited by 372
  2. 2016Control Barrier Function Based Quadratic Programs for Safety Critical Systemscited by 287
  3. 2020Learning Control Barrier Functions from Expert Demonstrationscited by 92
  4. 2020Probably Approximately Correct Constrained Learningcited by 47
  5. 2020Learning Stability Certificates from Datacited by 45
  6. 2020Synthesis of Control Barrier Functions Using a Supervised Machine Learning Approachcited by 35
  7. 2020Neural Certificates for Safe Control Policiescited by 35
  8. 2016Robustness of Control Barrier Functions for Safety Critical Controlcited by 30
  9. 2020Learning Hybrid Control Barrier Functions from Datacited by 24
  10. 2020Learning Lyapunov Functions for Hybrid Systemscited by 24
  11. 2018Barrier-Certified Adaptive Reinforcement Learning with Applications to Brushbot Navigationcited by 18
  12. 2020Learning Barrier Functions for Constrained Motion Planning with Dynamical Systemscited by 17
6 more
  1. 2020ShieldNN: A Provably Safe NN Filter for Unsafe NN Controllerscited by 17
  2. 2018Permissive Barrier Certificates for Safe Stabilization Using Sum-of-squarescited by 15
  3. 2018Input-to-State Safety With Control Barrier Functionscited by 15
  4. 2020Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbancescited by 9
  5. 2016Correctness Guarantees for the Composition of Lane Keeping and Adaptive Cruise Controlcited by 7
  6. 2020A hybrid barrier certificate approach to satisfy linear temporal logic specificationscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 4; 2023: 1; 2024: 3; 2025: 0; 2026: 2.

14 papers.

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  1. 2023The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systemscited by 88
  2. 2021Safe Pontryagin Differentiable Programmingcited by 26
  3. 2021Learning Robust Output Control Barrier Functions from Safe Expert Demonstrationscited by 20
  4. 2024Gameplay Filters: Robust Zero-Shot Safety through Adversarial Imaginationcited by 16
  5. 2022Joint Differentiable Optimization and Verification for Certified Reinforcement Learningcited by 11
  6. 2022Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Modelscited by 5
  7. 2021Model Free Barrier Functions via Implicit Evading Maneuverscited by 3
  8. 2022Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safetycited by 2
  9. 2022Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamicscited by 1
  10. 2026Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunitiescited by 1
  11. 2021Policy Search using Dynamic Mirror Descent MPC for Model Free Off Policy RLcited by 0
  12. 2024Scaling Learning based Policy Optimization for Temporal Logic Tasks by Controller Network Dropoutcited by 0
2 more
  1. 2024Stability properties of gradient flow dynamics for the symmetric low-rank matrix factorization problemcited by 0
  2. 2026SafeMind: A Risk-Aware Differentiable Control Framework for Adaptive and Safe Quadruped Locomotioncited by 0