mlsanityData to 13 Oct 2026

Learning Hybrid Control Barrier Functions from Data

arXiv 2011.04112 ↗First posted 8 Nov 2020eess.SY cs.LG cs.SYcited by 24

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Abstract (arXiv)

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. In particular, we assume a setting in which the system dynamics are known and in which data exhibiting safe system behavior is available. We propose hybrid control barrier functions for hybrid systems as a means to synthesize safe control inputs. Based on this notion, we present an optimization-based framework to learn such hybrid control barrier functions from data. Importantly, we identify sufficient conditions on the data such that feasibility of the optimization problem ensures correctness of the learned hybrid control barrier functions, and hence the safety of the system. We illustrate our findings in two simulations studies, including a compass gait walker.

Key facts. Cited by 24 papers in the map (data through 13 Oct 2026). First posted on arXiv 8 Nov 2020.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2020: 1; 2021: 3; 2022: 5; 2023: 2; 2024: 8; 2025: 3; 2026: 2.

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  1. 2023The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systemscited by 79
  2. 2022Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environmentscited by 54
  3. 2020Imitation Learning with Stability and Safety Guaranteescited by 34
  4. 2025Steering Dialogue Dynamics for Robustness against Multi-turn Jailbreaking Attackscited by 20
  5. 2021Learning Robust Output Control Barrier Functions from Safe Expert Demonstrationscited by 19
  6. 2021Learning Robust Hybrid Control Barrier Functions for Uncertain Systemscited by 14
  7. 2023Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approachcited by 13
  8. 2024Gameplay Filters: Robust Zero-Shot Safety through Adversarial Imaginationcited by 13
  9. 2024Verification of Neural Control Barrier Functions with Symbolic Derivative Bounds Propagationcited by 12
  10. 2022Joint Differentiable Optimization and Verification for Certified Reinforcement Learningcited by 11
  11. 2024Data-Driven Distributionally Robust Safety Verification Using Barrier Certificates and Conditional Mean Embeddingscited by 9
  12. 2024Verification-Aided Learning of Neural Network Barrier Functions with Termination Guaranteescited by 7
12 more
  1. 2025Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Controlcited by 7
  2. 2022Learning Globally Smooth Functions on Manifoldscited by 4
  3. 2024Learning Control Barrier Functions and their application in Reinforcement Learning: A Surveycited by 4
  4. 2026LMI-Net: Linear Matrix Inequality--Constrained Neural Networks via Differentiable Projection Layerscited by 4
  5. 2022Safe Inverse Reinforcement Learning via Control Barrier Functioncited by 3
  6. 2022Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamicscited by 1
  7. 2024Safe PDE Boundary Control with Neural Operatorscited by 1
  8. 2021Failing with Grace: Learning Neural Network Controllers that are Boundedly Unsafecited by 0
  9. 2024Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attackscited by 0
  10. 2024Stability properties of gradient flow dynamics for the symmetric low-rank matrix factorization problemcited by 0
  11. 2025Worst-Case Distance-Aware Error Bounds for Neural Networkscited by 0
  12. 2026Safe Probabilistic Planning for Human-Robot Interaction using Conformal Risk Controlcited by 0