mlsanityData to 14 Oct 2026

How to Train Your Neural Control Barrier Function: Learning Safety Filters for Complex Input-Constrained Systems

arXiv 2310.15478 ↗First posted 24 Oct 2023math.OC cs.ROcited by 44

Not on the map. Why?

Abstract (arXiv)

Control barrier functions (CBF) have become popular as a safety filter to guarantee the safety of nonlinear dynamical systems for arbitrary inputs. However, it is difficult to construct functions that satisfy the CBF constraints for high relative degree systems with input constraints. To address these challenges, recent work has explored learning CBFs using neural networks via neural CBF (NCBF). However, such methods face difficulties when scaling to higher dimensional systems under input constraints. In this work, we first identify challenges that NCBFs face during training. Next, to address these challenges, we propose policy neural CBF (PNCBF), a method of constructing CBFs by learning the value function of a nominal policy, and show that the value function of the maximum-over-time cost is a CBF. We demonstrate the effectiveness of our method in simulation on a variety of systems ranging from toy linear systems to an F-16 jet with a 16-dimensional state space. Finally, we validate our approach on a two-agent quadcopter system on hardware under tight input constraints.

Key facts. Cited by 44 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Oct 2023.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 10; 2025: 15; 2026: 19.

44 papers.

Sort:

By area:

  1. 2024Agile But Safe: Learning Collision-Free High-Speed Legged Locomotioncited by 73
  2. 2025Steering Dialogue Dynamics for Robustness against Multi-turn Jailbreaking Attackscited by 20
  3. 2024Safety Filters for Black-Box Dynamical Systems by Learning Discriminating Hyperplanescited by 12
  4. 2024Verification of Neural Control Barrier Functions with Symbolic Derivative Bounds Propagationcited by 12
  5. 2025Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoningcited by 10
  6. 2025How to Train Your Latent Control Barrier Function: Smooth Safety Filtering Under Hard-to-Model Constraintscited by 9
  7. 2025Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Controlcited by 8
  8. 2026Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPUcited by 7
  9. 2024Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibriumcited by 6
  10. 2025Learning Conservative Neural Control Barrier Functions from Offline Datacited by 5
  11. 2025V-OCBF: Learning Safety Filters from Offline Data via Value-Guided Offline Control Barrier Functionscited by 5
  12. 2024Learning Performance-Oriented Control Barrier Functions Under Complex Safety Constraints and Limited Actuationcited by 4
32 more
  1. 2025Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigationcited by 3
  2. 2025Back to Base: Towards Hands-Off Learning via Safe Resets with Reach-Avoid Safety Filterscited by 2
  3. 2025ORN-CBF: Learning Observation-conditioned Residual Neural Control Barrier Functions via Hypernetworkscited by 2
  4. 2026Safe Control using Learned Safety Filters and Adaptive Conformal Inferencecited by 2
  5. 2026Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllerscited by 2
  6. 2024Deep Reinforcement Learning for Reach-Avoid-Stay Problemscited by 1
  7. 2024Domain Adaptive Safety Filters via Deep Operator Learningcited by 1
  8. 2024Safe PDE Boundary Control with Neural Operatorscited by 1
  9. 2025Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functionscited by 1
  10. 2025Learning Neural Control Barrier Functions from Expert Demonstrations using Inverse Constraint Learningcited by 1
  11. 2026CN-CBF: Composite Neural Control Barrier Function for Robot Navigation in Dynamic Environmentscited by 1
  12. 2026Dyna-Style Safety Augmented Reinforcement Learning: Staying Safe in the Face of Uncertaintycited by 1
  13. 2024Pareto Control Barrier Function for Inner Safe Set Maximization Under Input Constraintscited by 0
  14. 2024TRUST-UP: Trustworthy Reinforcement learning Using Safe Techniques for UAV Pursuitcited by 0
  15. 2025Extracting Forward Invariant Sets from Neural Network-Based Control Barrier Functionscited by 0
  16. 2025Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systemscited by 0
  17. 2025Application-Specific Component-Aware Structured Pruning of Deep Neural Networks in Control via Soft Coefficient Optimizationcited by 0
  18. 2025LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigationcited by 0
  19. 2026Kernel-Based Learning of Safety Barrierscited by 0
  20. 2026BarrierSteer: LLM Safety via Learning Barrier Steeringcited by 0
  21. 2026SAGE-LLM: Towards Safe and Generalizable LLM Controller with Fuzzy-CBF Verification and Graph-Structured Knowledge Retrieval for UAV Decisioncited by 0
  22. 2026Safe Probabilistic Planning for Human-Robot Interaction using Conformal Risk Controlcited by 0
  23. 2026Discriminative Barrier Functions for Safe Adversarial Imitation Learning from Observationcited by 0
  24. 2026End-to-End Learning of Safe Optimal Feedback Control in High Dimensions with Control Barrier Function Layerscited by 0
  25. 2026Data-Driven Time-Varying Control Barrier Functions for Adaptive Safe-Set Learning with Online Decremental Support Vector Machinescited by 0
  26. 2026No Free Checker: A Survey of Verifiers for Robot Policiescited by 0
  27. 2026VertexCBF: Improving Neural Control Barrier Functions via Vertex-Restricted Control Searchcited by 0
  28. 2026ResSafe: Learning Safety Filtering with Residual Reinforcement Learning for Humanoidscited by 0
  29. 2026LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adversarial Potentialscited by 0
  30. 2026CrossSafe: Towards Cross-Embodiment Latent Safety Filterscited by 0
  31. 2026Safe Score Matching: Diffusion Policies with Hamilton-Jacobi Reachability for Online Safe Reinforcement Learningcited by 0
  32. 2026Safe Reinforcement Learning with Receding-Horizon Safety Critic and Differentiable Action Shieldscited by 0