14 citations · 35 across the 21 of their papers we have counts for
21 papers
SQ-CBF: Signed Distance Functions for Reliable Superquadric-Based Safety Filtering
Haocheng Zhao, Lukas Brunke, Oliver Lagerquist +2
Ensuring safe robot operation in cluttered and dynamic environments remains a fundamental challenge. While control barrier functions (CBFs) provide an effective framework for real-…
Improving Drone Racing Performance Through Iterative Learning MPC
Haocheng Zhao, Niklas Schlüter, Lukas Brunke +1
Autonomous drone racing presents a challenging control problem, requiring real-time decision-making and robust handling of nonlinear system dynamics. While iterative learning model…
Where Did I Leave My Glasses? Open-Vocabulary Semantic Exploration in Real-World Semi-Static Environments
Benjamin Bogenberger, Oliver Harrison, Orrin Dahanaggamaarachchi +4
Robots deployed in real-world environments, such as homes, must not only navigate safely but also understand their surroundings and adapt to changes in the environment. To perform…
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…
Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agents
Federico Pizarro Bejarano, Lukas Brunke, Angela P. Schoellig
Reinforcement learning (RL) controllers are flexible and performant but rarely guarantee safety. Safety filters impart hard safety guarantees to RL controllers while maintaining fl…
Semantically Safe Robot Manipulation: From Semantic Scene Understanding to Motion Safeguards
Lukas Brunke, Yanni Zhang, Ralf Römer +4
Ensuring safe interactions in human-centric environments requires robots to understand and adhere to constraints recognized by humans as "common sense" (e.g., "moving a cup of wate…
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…
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…
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…
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…
Swarm-GPT: Combining Large Language Models with Safe Motion Planning for Robot Choreography Design
Aoran Jiao, Tanmay P. Patel, Sanjmi Khurana +6
This paper presents Swarm-GPT, a system that integrates large language models (LLMs) with safe swarm motion planning - offering an automated and novel approach to deployable drone…
Multi-Step Model Predictive Safety Filters: Reducing Chattering by Increasing the Prediction Horizon
Federico Pizarro Bejarano, Lukas Brunke, Angela P. Schoellig
Learning-based controllers have demonstrated superior performance compared to classical controllers in various tasks. However, providing safety guarantees is not trivial. Safety, t…
A Remote Sim2real Aerial Competition: Fostering Reproducibility and Solutions' Diversity in Robotics Challenges
Spencer Teetaert, Wenda Zhao, Niu Xinyuan +21
Shared benchmark problems have historically been a fundamental driver of progress for scientific communities. In the context of academic conferences, competitions offer the opportu…
What is the Impact of Releasing Code with Publications? Statistics from the Machine Learning, Robotics, and Control Communities
Siqi Zhou, Lukas Brunke, Allen Tao +4
Open-sourcing research publications is a key enabler for the reproducibility of studies and the collective scientific progress of a research community. As all fields of science dev…
Robust Predictive Output-Feedback Safety Filter for Uncertain Nonlinear Control Systems
Lukas Brunke, Siqi Zhou, Angela P. Schoellig
In real-world applications, we often require reliable decision making under dynamics uncertainties using noisy high-dimensional sensory data. Recently, we have seen an increasing n…
Barrier Bayesian Linear Regression: Online Learning of Control Barrier Conditions for Safety-Critical Control of Uncertain Systems
Lukas Brunke, Siqi Zhou, Angela P. Schoellig
In this work, we consider the problem of designing a safety filter for a nonlinear uncertain control system. Our goal is to augment an arbitrary controller with a safety filter suc…
RLO-MPC: Robust Learning-Based Output Feedback MPC for Improving the Performance of Uncertain Systems in Iterative Tasks
Lukas Brunke, Siqi Zhou, Angela P. Schoellig
In this work we address the problem of performing a repetitive task when we have uncertain observations and dynamics. We formulate this problem as an iterative infinite horizon opt…
safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics
Zhaocong Yuan, Adam W. Hall, Siqi Zhou +4
In recent years, both reinforcement learning and learning-based control -- as well as the study of their safety, which is crucial for deployment in real-world robots -- have gained…
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
Lukas Brunke, Melissa Greeff, Adam W. Hall +4
The last half-decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learni…
Evaluating Input Perturbation Methods for Interpreting CNNs and Saliency Map Comparison
Lukas Brunke, Prateek Agrawal, Nikhil George
Input perturbation methods occlude parts of an input to a function and measure the change in the function's output. Recently, input perturbation methods have been applied to genera…
Learning Model Predictive Control for Competitive Autonomous Racing
Lukas Brunke
The goal of this thesis is to design a learning model predictive controller (LMPC) that allows multiple agents to race competitively on a predefined race track in real-time. This t…