Data to 7 Oct 2026

Learning for Safety-Critical Control with Control Barrier Functions

arXiv 1912.10099 ↗First posted 20 Dec 2019eess.SY cs.LG cs.SYcited by 66

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Abstract (arXiv)

Modern nonlinear control theory seeks to endow systems with properties of stability and safety, and have been deployed successfully in multiple domains. Despite this success, model uncertainty remains a significant challenge in synthesizing safe controllers, leading to degradation in the properties provided by the controllers. This paper develops a machine learning framework utilizing Control Barrier Functions (CBFs) to reduce model uncertainty as it impact the safe behavior of a system. This approach iteratively collects data and updates a controller, ultimately achieving safe behavior. We validate this method in simulation and experimentally on a Segway platform.

Key facts. Cited by 66 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Dec 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 22; 2022: 13; 2023: 9; 2024: 7; 2025: 8; 2026: 7.

66 papers.

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2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 337
2021Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificatescited by 71
2022Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environmentscited by 54
2021safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Roboticscited by 42
2022Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarkingcited by 31
2021Safe Driving via Expert Guided Policy Optimizationcited by 29
2021Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learningcited by 25
2021Learning Robust Output Control Barrier Functions from Safe Expert Demonstrationscited by 19
2021Distributional Depth-Based Estimation of Object Articulation Modelscited by 18
2023Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractionscited by 16
2022Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interactioncited by 15
2021Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertaintycited by 13

38 more

2021Safe Exploration by Solving Early Terminated MDPcited by 13
2021Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noisecited by 13
2021Risk-averse autonomous systems: A brief history and recent developments from the perspective of optimal controlcited by 12
2024Safe Reinforcement Learning on the Constraint Manifold: Theory and Applicationscited by 12
2021BarrierNet: A Safety-Guaranteed Layer for Neural Networkscited by 11
2023Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learningcited by 11
2021Youla-REN: Learning Nonlinear Feedback Policies with Robust Stability Guaranteescited by 10
2022Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Drivingcited by 10
2022End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functionscited by 10
2025Constrained Diffusers for Safe Planning and Controlcited by 9
2021Safe Exploration in Model-based Reinforcement Learning using Control Barrier Functionscited by 8
2022Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environmentscited by 8
2023Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidancecited by 8
2023Maximum diffusion reinforcement learningcited by 8
2024Safety Filters for Black-Box Dynamical Systems by Learning Discriminating Hyperplanescited by 8
2021A Theoretical Overview of Neural Contraction Metrics for Learning-based Control with Guaranteed Stabilitycited by 6
2023Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specificationscited by 6
2022KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systemscited by 5
2022Safe Reinforcement Learning via Confidence-Based Filterscited by 5
2022Neural Solvers for Fast and Accurate Numerical Optimal Controlcited by 4
2024Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learningcited by 4
2021Learning Contraction Policies from Offline Datacited by 3
2022Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Modelscited by 3
2024Learning safety critics via a non-contractive binary bellman operatorcited by 3
2024DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safetycited by 3
2025Estimating Control Barriers from Offline Datacited by 3
2025Reachability Barrier Networks: Learning Hamilton-Jacobi Solutions for Smooth and Flexible Control Barrier Functionscited by 3
2021Learning to Act Safely with Limited Exposure and Almost Sure Certaintycited by 2
2021Adversarially Robust Stability Certificates can be Sample-Efficientcited by 2
2022Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functionscited by 2
2023Learning Feasibility Constraints for Control Barrier Functionscited by 2
2024Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learningcited by 2
2025Safety Certification in the Latent space using Control Barrier Functions and World Modelscited by 2
2022Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functionscited by 1
2023Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learningcited by 1
2024The Autonomy-Alignment Problem in Open-Ended Learning Robots: Formalising the Purpose Frameworkcited by 1
2026Reinforcement Learning for Control Systems with Time Delays: A Comprehensive Surveycited by 1
2021Safe Learning of Uncertain Environmentscited by 0

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