8 papers
Online Bayesian Learning of Agent Behavior in Differential Games
Francesco Bianchin, Robert Lefringhausen, Sandra Hirche
This work introduces an online Bayesian game-theoretic method for behavior identification in multi-agent dynamical systems. By casting Hamilton-Jacobi-Bellman optimality conditions…
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.…