The field of safety verification and control in stochastic systems is moving towards more innovative and advanced techniques. Researchers are focusing on developing refined barrier conditions, adaptive override control methods, and scalable safety verification algorithms to ensure the safety and reliability of complex systems. These advancements are crucial for the development of autonomous systems, robotic systems, and other safety-critical applications. Notable papers in this area include: Refined Barrier Conditions for Finite-Time Safety and Reach-Avoid Guarantees in Stochastic Systems, which presents a key relaxation of existing barrier certificate methods. Formal Safety Verification and Refinement for Generative Motion Planners via Certified Local Stabilization, which provides a method for formal safety verification of learning-based generative motion planners. Scalable and Approximation-free Symbolic Control for Unknown Euler-Lagrange Systems, which proposes a novel symbolic control framework for enforcing temporal logic specifications in Euler-Lagrange systems.
Safety Verification and Control in Stochastic Systems
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Refined Barrier Conditions for Finite-Time Safety and Reach-Avoid Guarantees in Stochastic Systems
Adaptive Override Control under High-Relative-Degree Nonovershooting Constraints
Automatic and Scalable Safety Verification using Interval Reachability with Subspace Sampling
linrax: A JAX Compatible, Simplex Method Linear Program Solver
Formal Safety Verification and Refinement for Generative Motion Planners via Certified Local Stabilization
Scalable and Approximation-free Symbolic Control for Unknown Euler-Lagrange Systems
A note on the compactness properties of discontinuous Galerkin time discretizations
Orbital Stabilization and Time Synchronization of Unstable Periodic Motions in Underactuated Robots
Hybrid Safety Verification of Multi-Agent Systems using $\psi$-Weighted CBFs and PAC Guarantees
From Zonotopes to Proof Certificates: A Formal Pipeline for Safe Control Envelopes