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20232026
most citedSwarm-GPT: Combining Large Language Models with Safe Motion Planning for Robot Choreography Design

5 citations · 12 across the 18 of their papers we have counts for

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6 papers · 1 filter

eess.SY2026

A Decade of Learning-Based Control: From Theoretic Foundations to Practical Deployment

Angela P. Schoellig, Matthias A. Müller, Sebastian Trimpe +8

Motivated by ten years of Learning-Based Control Invited Sessions at the IEEE Conference on Decision and Control (CDC), this tutorial paper reviews key advances at the intersection…

eess.SY2025

Addressing Relative Degree Issues in Control Barrier Function Synthesis with Physics-Informed Neural Networks

Lukas Brunke, Siqi Zhou, Francesco D'Orazio +1

In robotics, control barrier function (CBF)-based safety filters are commonly used to enforce state constraints. A critical challenge arises when the relative degree of the CBF var…

eess.SY2024

Preventing Inactive CBF Safety Filters Caused by Invalid Relative Degree Assumptions

Lukas Brunke, Siqi Zhou, Angela P. Schoellig

Control barrier function (CBF) safety filters emerged as a popular framework to certify and modify potentially unsafe control inputs, for example, provided by a reinforcement learn…

eess.SY2024

Practical Considerations for Discrete-Time Implementations of Continuous-Time Control Barrier Function-Based Safety Filters

Lukas Brunke, Siqi Zhou, Mingxuan Che +1

Safety filters based on control barrier functions (CBFs) have become a popular method to guarantee safety for uncertified control policies, e.g., as resulting from reinforcement le…

eess.SY2024

Is Data All That Matters? The Role of Control Frequency for Learning-Based Sampled-Data Control of Uncertain Systems

Ralf Römer, Lukas Brunke, Siqi Zhou +1

Learning models or control policies from data has become a powerful tool to improve the performance of uncertain systems. While a strong focus has been placed on increasing the amo…

eess.SY2023

Optimized Control Invariance Conditions for Uncertain Input-Constrained Nonlinear Control Systems

Lukas Brunke, Siqi Zhou, Mingxuan Che +1

Providing safety guarantees for learning-based controllers is important for real-world applications. One approach to realizing safety for arbitrary control policies is safety filte…