3 citations · 7 across the 26 of their papers we have counts for
8 papers · 1 filter
Learning Dynamics from Infrequent Output Measurements for Uncertainty-Aware Optimal Control
Robert Lefringhausen, Theodor Springer, Sandra Hirche
Reliable optimal control is challenging when the dynamics of a nonlinear system are unknown and only infrequent, noisy output measurements are available. This work addresses this s…
HumanHalo: Safe and Efficient 3D Navigation Among Humans via Minimally Conservative MPC
Simon Schaefer, Helen Oleynikova, Sandra Hirche +1
Safe and efficient robotic navigation among humans is essential for integrating robots into everyday environments. Most existing approaches focus on simplified 2D crowd navigation…
Learning Safe Control via On-the-Fly Bandit Exploration
Alexandre Capone, Ryan Cosner, Aaaron Ames +1
Control tasks with safety requirements under high levels of model uncertainty are increasingly common. Machine learning techniques are frequently used to address such tasks, typica…
UniConFlow: A Unified Constrained Flow-Matching Framework for Certified Motion Planning
Zewen Yang, Xiaobing Dai, Dian Yu +4
Generative models have become increasingly powerful tools for robot motion generation, enabling flexible and multimodal trajectory generation across various tasks. Yet, most existi…
Learning Geometrically-Informed Lyapunov Functions with Deep Diffeomorphic RBF Networks
Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche
The practical deployment of learning-based autonomous systems would greatly benefit from tools that flexibly obtain safety guarantees in the form of certificate functions from data…
SafeFlow: Safe Robot Motion Planning with Flow Matching via Control Barrier Functions
Xiaobing Dai, Zewen Yang, Dian Yu +4
Recent advances in generative modeling have led to promising results in robot motion planning, particularly through diffusion and flow matching (FM)-based models that capture compl…
A Set-Theoretic Robust Control Approach for Linear Quadratic Games with Unknown Counterparts
Francesco Bianchin, Robert Lefringhausen, Elisa Gaetan +2
Ensuring robust decision-making in multi-agent systems is challenging when agents have distinct, possibly conflicting objectives and lack full knowledge of each other's strategies.…
Barrier Certificates for Unknown Systems with Latent States and Polynomial Dynamics using Bayesian Inference
Robert Lefringhausen, Sami Leon Noel Aziz Hanna, Elias August +1
Certifying safety in dynamical systems is crucial, but barrier certificates - widely used to verify that system trajectories remain within a safe region - typically require explici…