7 papers
SQ-CBF: Signed Distance Functions for Numerically Stable 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 provide an effective framework for real-time sa…
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…
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…
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…
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…
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…