mlsanityData to 7 Oct 2026

Neural Certificates for Safe Control Policies

arXiv 2006.08465 ↗First posted 15 Jun 2020eess.SY cs.LG cs.SYcited by 35

Not on the map. Why?

Abstract (arXiv)

This paper develops an approach to learn a policy of a dynamical system that is guaranteed to be both provably safe and goal-reaching. Here, the safety means that a policy must not drive the state of the system to any unsafe region, while the goal-reaching requires the trajectory of the controlled system asymptotically converges to a goal region (a generalization of stability). We obtain the safe and goal-reaching policy by jointly learning two additional certificate functions: a barrier function that guarantees the safety and a developed Lyapunov-like function to fulfill the goal-reaching requirement, both of which are represented by neural networks. We show the effectiveness of the method to learn both safe and goal-reaching policies on various systems, including pendulums, cart-poles, and UAVs.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jun 2020.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 7; 2022: 12; 2023: 8; 2024: 7; 2025: 1.

35 papers.

Sort:

By area:

  1. 2021Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificatescited by 71
  2. 2023Empowering Autonomous Driving with Large Language Models: A Safety Perspectivecited by 34
  3. 2021Safe Pontryagin Differentiable Programmingcited by 25
  4. 2021Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learningcited by 25
  5. 2024Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulationcited by 23
  6. 2022Safety Certification for Stochastic Systems via Neural Barrier Functionscited by 22
  7. 2022Learning Control Policies for Stochastic Systems with Reach-avoid Guaranteescited by 21
  8. 2021Learning Robust Output Control Barrier Functions from Safe Expert Demonstrationscited by 19
  9. 2023In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution Statescited by 15
  10. 2021Learning Robust Hybrid Control Barrier Functions for Uncertain Systemscited by 14
  11. 2021BarrierNet: A Safety-Guaranteed Layer for Neural Networkscited by 11
  12. 2022Neural Koopman Lyapunov Controlcited by 11
23 more
  1. 2022Joint Differentiable Optimization and Verification for Certified Reinforcement Learningcited by 11
  2. 2022Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Drivingcited by 10
  3. 2022On the Forward Invariance of Neural ODEscited by 10
  4. 2023Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functionscited by 9
  5. 2023Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidancecited by 8
  6. 2024Verification-Aided Learning of Neural Network Barrier Functions with Termination Guaranteescited by 7
  7. 2022Reachable Polyhedral Marching (RPM): An Exact Analysis Tool for Deep-Learned Control Systemscited by 6
  8. 2023A General Framework for Verification and Control of Dynamical Models via Certificate Synthesiscited by 6
  9. 2024Actor-Critic Physics-informed Neural Lyapunov Controlcited by 6
  10. 2022KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systemscited by 5
  11. 2024Safe Reach Set Computation via Neural Barrier Certificatescited by 5
  12. 2022Learning Provably Stabilizing Neural Controllers for Discrete-Time Stochastic Systemscited by 4
  13. 2022FI-ODE: Certifiably Robust Forward Invariance in Neural ODEscited by 3
  14. 2024Certified Training with Branch-and-Bound for Lyapunov-stable Neural Controlcited by 3
  15. 2021Adversarially Robust Stability Certificates can be Sample-Efficientcited by 2
  16. 2022Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functionscited by 2
  17. 2023Meta-Learning-Based Adaptive Stability Certificates for Dynamical Systemscited by 2
  18. 2024Transfer of Safety Controllers Through Learning Deep Inverse Dynamics Modelcited by 1
  19. 2025Safe Online Control-Informed Learningcited by 1
  20. 2022Revisiting PGD Attacks for Stability Analysis of Large-Scale Nonlinear Systems and Perception-Based Controlcited by 0
  21. 2023A Policy Optimization Method Towards Optimal-time Stabilitycited by 0
  22. 2023Deep Bayesian Reinforcement Learning for Spacecraft Proximity Maneuvers and Dockingcited by 0
  23. 2024Structured Deep Neural Network-Based Backstepping Trajectory Tracking Control for Lagrangian Systemscited by 0