Theo Gruner

Theo Gruner joined the Intelligent Autonomous Systems Group as a Ph.D. student in September 2022. Currently, he is focusing on system identification approaches for sim-to-real transfer via likelihood-free inference.

Theo holds a master's degree in Computational Engineering and a bachelor's degree in Applied Mechanics, both from TU Darmstadt. His thesis on "Wasserstein-Optimal Bayesian System Identification for Domain Randomization" was supervised by Fabio Muratore, Boris Belousov, and Jan Peters and won the Freunde-Preis for "best master's thesis at the department of Computer Science."

Publications

1. •     Bib
     Gruner, T.; Belousov, B.; Muratore, F.; Palenicek, D.; Peters, J. (2023). Pseudo-Likelihood Inference, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
2. •     Bib
     Muratore, F.; Gruner, T.; Wiese, F.; Belousov, B.; Gienger, M.; Peters, J. (2021). Neural Posterior Domain Randomization, Conference on Robot Learning (CoRL).
3. •   Bib
     Lenz, J.; Gruner, T.; Palenicek, D.; Schneider, T.; Pfenning, I.; Peters J. (2024). Analysing the Interplay of Vision and Touch for Dexterous Insertion Tasks, CoRL 2024 Workshop on Learning Robot Fine and Dexterous Manipulation: Perception and Control.
4. •     Bib
     Palenicek, D.; Gruner, T.; Schneider, T.; Böhm, A.; Lenz, J.; Pfenning, I. and Krämer, E.; Peters, J. (2024). Learning Tactile Insertion in the Real World, IEEE ICRA 2024 Workshop on Robot Embodiment through Visuo-Tactile Perception.
   •     Bib
     Palenicek, D.; Gruner, T.; Schneider, T.; Böhm, A.; Lenz, J.; Pfenning, I. and Krämer, E.; Peters, J. (2024). Learning Tactile Insertion in the Real World, 40th Anniversary of the IEEE International Conference on Robotics and Automation (ICRA@40).
5. •     Bib
     Gruner, T. (2021). Wasserstein-Optimal Bayesian System Identification for Domain Randomization, Master Thesis.