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20202023
most citedSafe Exploration in Model-based Reinforcement Learning using Control Barrier Functions

102 citations · 152 across the 8 of their papers we have counts for

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5 papers · 1 filter

eess.SY2023

Uncertainty Quantification for Recursive Estimation in Adaptive Safety-Critical Control

Max H. Cohen, Makai Mann, Kevin Leahy +1

In this paper, we present a framework for online parameter estimation and uncertainty quantification in the context of adaptive safety-critical control. The key insight enabling ou…

eess.SY2023

Modular Adaptive Safety-Critical Control

Max Cohen, Calin Belta

This paper presents an adaptive control approach for uncertain nonlinear systems subject to safety constraints that allows for modularity in the selection of the parameter estimati…

eess.SY2022★ 46 cited

High Order Robust Adaptive Control Barrier Functions and Exponentially Stabilizing Adaptive Control Lyapunov Functions

Max H. Cohen, Calin Belta

This paper studies the problem of utilizing data-driven adaptive control techniques to guarantee stability and safety of uncertain nonlinear systems with high relative degree. We f…

eess.SY2021

Model-Based Reinforcement Learning for Approximate Optimal Control with Temporal Logic Specifications

Max Cohen, Calin Belta

In this paper we study the problem of synthesizing optimal control policies for uncertain continuous-time nonlinear systems from syntactically co-safe linear temporal logic (scLTL)…

eess.SY2020

Approximate Optimal Control for Safety-Critical Systems with Control Barrier Functions

Max Cohen, Calin Belta

Control Barrier Functions (CBFs) have become a popular tool for enforcing set invariance in safety-critical control systems. While guaranteeing safety, most CBF approaches are myop…