Theo Gruner
Research Interests
Approximate Bayesian inference, adaptive domain randomization, model learning, system identification
Affiliations
1. TU Darmstadt, Intelligent Autonomous Systems, Computer Science Department
2. Hessian Centre for Artificial Intelligence
Contact
theo_sunao.gruner@tu-darmstadt.de
Room D202, Building S2|02, TU Darmstadt, FB-Informatik, FG-IAS, Hochschulstr. 10, 64289 Darmstadt
+49-6151-16-25385
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
Adaptive Domain Randomization
- Gruner, T.; Belousov, B.; Muratore, F.; Palenicek, D.; Peters, J. (2023). Pseudo-Likelihood Inference, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
- Muratore, F.; Gruner, T.; Wiese, F.; Belousov, B.; Gienger, M.; Peters, J. (2021). Neural Posterior Domain Randomization, Conference on Robot Learning (CoRL).
- Gruner, T. (2021). Wasserstein-Optimal Bayesian System Identification for Domain Randomization, Master Thesis.
Robot Foundation Models
- Toelle, M.; Gruner, T.; Palenicek, D.; Schneider, T. Guenster, J.; Watson, J.; Tateo, D.; Liu, P.; Peters, J. (2025). Towards Safe Robot Foundation Models using Inductive Biases, SafeVLM Workshop @ IEEE International Conference on Robotics and Automation (ICRA), Spotlight.
- Scherer, C. F.; Tölle, M.; Gruner, T.; Palenicek, D.; Schneider, T.; Schramowski, P.; Belousov, B.; Peters, J. (2025). AllmAN: A German Vision-Language-Action Model, German Robotics Conference (GRC).
Others
- Watson, J.; Song, C.; Weeger, O.; Gruner, T.; Le, A.T.; Hansel, K.; Headway, A.; Arenz, O.; Trojak, W.; Cranmer, M.; D’Eramo, C.; Bülow, F.; Goyal, T.; Peters, J.; Hoffman, M.W.; (2025). Machine Learning with Physics Knowledge for Prediction: A Survey, Transactions on Machine Learning Research (TMLR).
- 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.
- 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.
- 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).
Supervised Projects
| Thesis/Project | Student(s) | Topic | Together with |
| M.Sc. Thesis | Lenz J. | Integration of Vision and Tactile Sensing for Robotic Insertion Tasks using Deep Reinforcement Learning | Tim Schneider & Daniel Palenicek |
| M.Sc. Thesis | Jacobs T. | Developing a Simulation Platform for the Benchmarking of Generalist Robot Policies | Tim Schneider & Maximilian Tölle & Daniel Palenicek |
| B.Sc. Thesis | Scherer C. | Coherent Soft Imitation Learning for Vision-Language-Action models | Daniel Palenicek & Joe Watson |
| RL:IP.SS24 | Scherer C. | Training Large Scale Robot Transformer Models | Tim Schneider & Daniel Palenicek & Maximilian Tölle |
| RL:IP.WS23/24 | Dennert D., Scherer C., Ahmad F. | XXX: eXploring X-Embodiment with RT-X | Tim Schneider & Daniel Palenicek & Maximilian Tölle |
| RL:IP.WS23/24 | Jacobs T. | XXX: eXploring X-Embodiment with RT-X | Tim Schneider & Daniel Palenicek & Maximilian Tölle |
| RL:IP.WS23/24 | Böhm A., Pfenning I., Lenz J. | Unveiling the Unseen: Tactile Perception and Reinforcement Learning in the Real World | Tim Schneider & Theo Gruner |
| RL:IP.WS23/24 | Wang, Y., Li, S. | Benchmarking Sequence Models for Discontinuous Dynamical Systems | Puze Liu |
| RL:IP.SS22 | Klyushina, A., Rath, M. | Black-Box System Identification of the Airhockey Table | Puze Liu |
| RL:IP.SS22 | Krämer, E. | Latent Tactile Representations for Model-based RL | Tim Schneider & Daniel Palenicek |