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1. Gaussian Control Barrier Functions: Safe Learning and Control

   https://doi.org/10.1109/CDC42340.2020.9303753  

   Khan, Mouhyemen; Chatterjee, Abhijit (December 2020, IEEE Conference on Decision and Control)

   null (Ed.)

   Safety is a critical component in today's autonomous and robotic systems. Many modern controllers endowed with notions of guaranteed safety properties rely on accurate mathematical models of these nonlinear dynamical systems. However, model uncertainty is always a persistent challenge weakening theoretical guarantees and compromising safety. For safety-critical systems, this is an even bigger challenge. Typically, safety is ensured by constraining the system states within a safe constraint set defined a priori by relying on the model of the system. A popular approach is to use Control Barrier Functions (CBFs) that encode safety using a smooth function. However, CBFs fail in the presence of model uncertainties. Moreover, an inaccurate model can either lead to incorrect notions of safety or worse, incur system critical failures. Addressing these drawbacks, we present a novel safety formulation that leverages properties of CBFs and positive definite kernels to design Gaussian CBFs. The underlying kernels are updated online by learning the unmodeled dynamics using Gaussian Processes (GPs). While CBFs guarantee forward invariance, the hyperparameters estimated using GPs update the kernel online and thereby adjust the relative notion of safety. We demonstrate our proposed technique on a safety-critical quadrotor on SO(3) in the presence of model uncertainty in simulation. With the kernel update performed online, safety is preserved for the system. 

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2. Building Bridges: Safe Interactions with Foreign Languages through Omniglot

   Schuermann, Leon; Toubes, Jack; Potyondy, Tyler; Pannuto, Pat; Milano, Mae; Levy, Amit (July 2025, USENIX)

   Memory- and type-safe languages promise to eliminate entire classes of systems vulnerabilities by construction. In practice, though, even clean-slate systems often need to incor- porate libraries written in other languages with fewer safety guarantees. Because these interactions threaten the soundness of safe languages, they can reintroduce the exact vulnerabili- ties that safe languages prevent in the first place. This paper presents Omniglot: the first framework to effi- ciently uphold safety and soundness of Rust in the presence of unmodified and untrusted foreign libraries. Omniglot fa- cilitates interactions with foreign code by integrating with a memory isolation primitive and validation infrastructure, and avoids expensive operations such as copying or serialization. We implement Omniglot for two systems: we use it to inte- grate kernel components in a highly-constrained embedded operating system kernel, as well as to interface with conven- tional Linux userspace libraries. Omniglot performs com- parably to approaches that deliver weaker guarantees and significantly better than those with similar safety guarantees. 

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3. Model-based Operability and Safety Optimization for PEM Water Electrolysis

   https://doi.org/10.69997/sct.132227  

   Dantas, Beatriz; Akundi, Sahithi S; Liu, Yuanxing; Braniff, Austin; Niknezhad, Shayan S (July 2025, PSE Press)

   In this paper, we present a systematic approach to quantify the safe operating window of a proton exchange membrane water electrolysis (PEMWE) system considering energy intermittency and varying hydrogen demand. The PEMWE model has been developed based on first principles, with the polarization curve validated against a lab-scale experimental setup. The impact of key operational variables is investigated which include voltage, inlet temperature, and water flowrate (utilized for both feed and system cooling). Emphasis is given on operating temperature, a safety-critical variable, as its elevation can pose significant hydrogen safety risks within both the electrolyzer cells and the storage system. The impact of temperature on process safety is quantified via a risk index considering the fault probability and consequence severity. Process operability analysis is employed to assess the achievability of a safe and feasible region for design and operations. This analysis provides a comprehensive framework to optimize PEMWE systems for enhanced operational flexibility and robust performance with application to modular hydrogen production using renewable energy sources. 

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4. Safe Physics-informed Machine Learning for Dynamics and Control

   Drgona, Jan; Nghiem, Truong X; Beckers, Thomas; Fazlyab, Mahyar; Mallada, Enrique; Jones, Colin; Vrabie, Draguna; Brunton, Steven L; Findeisen, Rolf (July 2025, IEEE)

   This tutorial paper focuses on safe physics- informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models and safety guarantees. As machine learning techniques enhance the modeling and control of complex dynamical systems, ensuring safety and stability remains a critical challenge, especially in safety-critical applications like autonomous vehicles, robotics, medical decision-making, and energy systems. We explore various approaches for embedding and ensuring safety constraints, including structural priors, Lyapunov and Control Barrier Functions, predictive control, projections, and robust optimization techniques. Additionally, we delve into methods for uncertainty quantification and safety verification, including reachability analysis and neural network verification tools, which help validate that control policies remain within safe operating bounds even in uncertain environments. The paper includes illustrative examples demonstrating the implementation aspects of safe learning frameworks that combine the strengths of data-driven approaches with the rigor of physical principles, offering a path toward the safe control of complex dynamical systems. 

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5. Constraint-Conditioned Actor-Critic for Offline Safe Reinforcement Learning

   Guo, Zijian; Zhou, Weichao; Wang, Shengao; Li, Wenchao (March 2025, The Thirteenth International Conference on Learning Representations)

   Offline safe reinforcement learning (OSRL) aims to learn policies with high rewards while satisfying safety constraints solely from data collected offline. However, the learned policies often struggle to handle states and actions that are not present or out-of-distribution (OOD) from the offline dataset, which can result in violation of the safety constraints or overly conservative behaviors during their online deployment. Moreover, many existing methods are unable to learn policies that can adapt to varying constraint thresholds. To address these challenges, we propose constraint-conditioned actor-critic (CCAC), a novel OSRL method that models the relationship between state-action distributions and safety constraints, and leverages this relationship to regularize critics and policy learning. CCAC learns policies that can effectively handle OOD data and adapt to varying constraint thresholds. Empirical evaluations on the benchmarks show that CCAC significantly outperforms existing methods for learning adaptive, safe, and high-reward policies. 

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