Luzia Knödler — accepted AI-conference papers

2025

Hey Robot! Personalizing Robot Navigation Through Model Predictive Control with a Large Language Model

Diego Martinez-Baselga, Oscar de Groot, Luzia Knödler, Javier Alonso-Mora, Luis Riazuelo, Luis Montano

ICRA 2025

Robot navigation methods allow mobile robots to operate in applications such as warehouses or hospitals. While the environment in which the robot operates imposes requirements on its navigation behavior, most existing methods do not allow the end-user to configure the robot's behavior and priorities

Cited by 3 Source ↗Scholar ↗

2025

Safety on the Fly: Constructing Robust Safety Filters via Policy Control Barrier Functions at Runtime

Luzia Knödler, Oswin So, Ji Yin, Mitchell Black, Zachary Serlin, Panagiotis Tsiotras +2

RA-L 2025

Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a difficult problem. In this work, we propose the R

Cited by 9 Source ↗Scholar ↗

2022

Improving Pedestrian Prediction Models With Self-Supervised Continual Learning

Luzia Knödler, Chadi Salmi, Hai Zhu, Bruno Brito, Javier Alonso-Mora

RA-L 2022

Autonomous mobile robots require accurate human motion predictions to safely and efficiently navigate among pedestrians, whose behavior may adapt to environmental changes. This letter introduces a self-supervised continual learning framework to improve data-driven pedestrian prediction models online

Cited by 17 Source ↗code ↗Scholar ↗