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Found 7 papers, 1 papers with code
Safety Filters for Black-Box Dynamical Systems by Learning Discriminating Hyperplanes
As such, we believe that the new notion of the discriminating hyperplane offers a more generalizable direction towards designing safety filters, encompassing and extending existing certificate-function-based or safe RL methodologies.
A Forward Reachability Perspective on Robust Control Invariance and Discount Factors in Reachability Analysis
We also formulate a zero-sum differential game between the control and disturbance, where the inevitable FRT is characterized by the zero-superlevel set of the value function.
Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions
This feasibility analysis results in a set of richness conditions that the available information about the system should satisfy to guarantee that a safe control action can be found at all times.
Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertainty
However, since these constraints rely on a model of the system, when this model is inaccurate the guarantees of safety and stability can be easily lost.
Robust Control Barrier-Value Functions for Safety-Critical Control
This paper works towards unifying two popular approaches in the safety control community: Hamilton-Jacobi (HJ) reachability and Control Barrier Functions (CBFs).
Scalable Learning of Safety Guarantees for Autonomous Systems using Hamilton-Jacobi Reachability
However, work to learn and update safety analysis is limited to small systems of about two dimensions due to the computational complexity of the analysis.
Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics
This paper presents a method to design a min-norm Control Lyapunov Function (CLF)-based stabilizing controller for a control-affine system with uncertain dynamics using Gaussian Process (GP) regression.
