Safety-Critical Control

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Safe Control using Learned Safety Filters and Adaptive Conformal Inference
Apr 20, 2026Sacha Huriot, Ihab Tabbara, Hussein SibaiSafety-Critical Control Conformal Prediction
Computer Science & Engineering Washington University in St. Louis
Harness as an Asset: Enforcing Determinism via the Convergent AI Agent Framework (CAAF)
Apr 18, 2026Tianbao ZhangSafety-Critical Control LLM Guardrails
Independent Researcher
Risk-Constrained Belief-Space Optimization for Safe Control under Latent Uncertainty
Apr 4, 2026Clinton Enwerem, John S. Baras, Calin BeltaBelief-Space Planning Safety-Critical Control
Institute for Systems Research, University of Maryland, College Park, MD, USA
Safe learning-based control via function-based uncertainty quantification
Apr 1, 2026Abdullah Tokmak, Toni Karvonen, Thomas B. Schön +1Uncertainty Quantification Safety-Critical Control
Cyber-physical Systems Group, Aalto University, Espoo, Finland · School of Engineering Sciences, Lappeenranta–Lahti University of Technology LUT, Lappeenranta, Finland · Department of Information Technology, Uppsala University, Uppsala, Sweden
Agentic Physical AI toward a Domain-Specific Foundation Model for Energy Systems: A Case Study on Nuclear Reactor Control
Dec 29, 2025Yoon Pyo Lee, Samrendra Roy, Kazuma Kobayashi +5Language Model-Based Control Safety-Critical Control
The Grainger College of Engineering, Nuclear, Plasma & Radiological Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA · Department of Nuclear Engineering, Hanyang University, Republic of Korea · University of Texas - El Paso, University Ave, El Paso, USA +4
SB-TRPO: Towards Safe Reinforcement Learning with Hard Constraints
Dec 29, 2025Dominik Wagner, Ankit Kanwar, Luke OngSafety-Critical Control Policy Optimization
NTU Singapore · Sony Corporation
Least Restrictive Hyperplane Control Barrier Functions
Oct 21, 2025Mattias Trende, Petter ÖgrenSafety-Critical Control Control Barrier Functions
The authors are with the Robotics, Perception and Learning Lab., School of Electrical Engineering and Computer Science, Royal Institute of Technology (KTH), SE-100 44 Stockholm, Sweden
Captivity-Escape Games as a Means for Safety in Online Motion Generation
Jun 2, 2025Christopher Bohn, Manuel Hess, Sören HohmannTrajectory Generation Safety-Critical Control
Institute of Control Systems, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
Learning-based Adaptive Safety-Critical Control With Evolving Unsafe Regions
Mar 19, 2025Songqiao Hu, Zidong Wang, Zeyi Liu +2Safety-Critical Control Control Barrier Functions
Department of Automation and the Institute for Embodied Intelligence and Robotics, Tsinghua University, Beijing 100084, China · Department of Computer Science, Brunel University London, UB8 3PH Uxbridge, U.K. · State Key Laboratory of Multimodal Artificial Intelligence Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
From Uncertain to Safe: Conformal Adaptation of Diffusion Models for Safe PDE Control
Feb 4, 2025Peiyan Hu, Xiaowei Qian, Wenhao Deng +8Diffusion Model Guidance Safety-Critical Control
Work done as an intern at Westlake University · Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China · School of Engineering, Westlake University, Hangzhou, China +2
Layered Safety: Enhancing Autonomous Collision Avoidance via Multistage CBF Safety Filters
Date pendingErina Yamaguchi, Ryan M. Bena, Gilbert Bahati +1Safety-Critical Control Control Barrier Functions
Department of Mechanical and Civil Engineering and the Department of Aerospace, Caltech, Pasadena, CA 91125, USA

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Safe Control using Learned Safety Filters and Adaptive Conformal Inference

Sacha Huriot, Ihab Tabbara, Hussein Sibai

Computer Science & Engineering Washington University in St. Louis

Safety-Critical Control Conformal Prediction Safety Filtering Adaptive Conformal Inference

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Safety filters have been shown to be effective tools to ensure the safety of control systems with unsafe nominal policies. To address scalability challenges in traditional synthesis methods, learning-based approaches have been proposed for designing safety filters for systems with high-dimensional state and control spaces. However, the inevitable errors in the decisions of these models raise concerns about their reliability and the safety guarantees they offer. This paper presents Adaptive Conformal Filtering (ACoFi), a method that combines learned Hamilton-Jacobi reachability-based safety filters with adaptive conformal inference. Under ACoFi, the filter dynamically adjusts its switching criteria based on the observed errors in its predictions of the safety of actions. The range of possible safety values of the nominal policy's output is used to quantify uncertainty in safety assessment. The filter switches from the nominal policy to the learned safe one when that range suggests it might be unsafe. We show that ACoFi guarantees that the rate of incorrectly quantifying uncertainty in the predicted safety of the nominal policy is asymptotically upper bounded by a user-defined parameter. This gives a soft safety guarantee rather than a hard safety guarantee. We evaluate ACoFi in a Dubins car simulation and a Safety Gymnasium environment, empirically demonstrating that it significantly outperforms the baseline method that uses a fixed switching threshold by achieving higher learned safety values and fewer safety violations, especially in out-of-distribution scenarios.

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