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Characterizing Smooth Safety Filters via the Implicit Function Theorem
Optimization-based safety filters, such as control barrier function (CBF) based quadratic programs (QPs), have demonstrated success in controlling autonomous systems to achieve complex goals.
Uncertainty Quantification for Recursive Estimation in Adaptive Safety-Critical Control
In this paper, we present a framework for online parameter estimation and uncertainty quantification in the context of adaptive safety-critical control.
High Order Robust Adaptive Control Barrier Functions and Exponentially Stabilizing Adaptive Control Lyapunov Functions
We first introduce the notion of a High Order Robust Adaptive Control Barrier Function (HO-RaCBF) as a means to compute control policies guaranteeing satisfaction of high relative degree safety constraints in the face of parametric model uncertainty.
Safe Exploration in Model-based Reinforcement Learning using Control Barrier Functions
This paper develops a model-based reinforcement learning (MBRL) framework for learning online the value function of an infinite-horizon optimal control problem while obeying safety constraints expressed as control barrier functions (CBFs).
Model-based Reinforcement Learning
reinforcement-learning
+2
