Hannes Homburger
HTWG Hochschule Konstanz - Technik, Wirtschaft und Gestaltung
Papers
1
Total Citations
3
H-Index
1
About
Hannes Homburger is an emerging researcher whose work sits at the intersection of optimal control theory, robotics, and reinforcement learning, with a particular focus on stochastic and probabilistic control frameworks. His most notable contribution to date is a 2025 paper examining the optimality and suboptimality of Model Predictive Path Integral (MPPI) control — a sampling-based control method that has garnered significant interest across the robotics and machine learning communities. In this work, Homburger bridges a critical gap by making the MPPI framework more accessible and rigorous for the classical optimal control community, presenting three distinct classes of optimal control problems and analyzing when MPPI performs well versus where its limitations arise. Though early in his academic trajectory, the paper has already accumulated 3 citations within its publication year, signaling growing interest from researchers across disciplines. Homburger's research is characterized by its commitment to theoretical clarity and cross-community accessibility — translating cutting-edge techniques from machine learning into the language of formal control theory, a contribution that stands to meaningfully advance both fields as autonomous systems grow increasingly complex.
Research Focus
Key Achievements
Top Papers
- 1