Niklas Funk

I have graduated in December 2025. For more up-to-date information see my personal website: https://niklasfunk.com/.

Niklas Funk joined the Institute for Intelligent Autonomous Systems (IAS) at TU Darmstadt in September 2020 as a Ph.D. student.

Previously, Niklas received his bachelor‘s degree in Electrical Engineering and Information Technology, as well as his master’s degree in Robotics, Systems and Control from ETH Zurich. During his studies, he focused on the intersection between machine learning and control and completed several applied projects. He finished off his masters with the thesis – „Learning Event-triggered Control from Data through Joint Optimization“, which was conducted at the Max-Planck Institute for Intelligent Systems under the supervision of Dominik Baumann and Sebastian Trimpe, and has been awarded with the ETH medal. Niklas interned at Bosch, NVIDIA, and Toyota Research Institute.

Niklas researches ways to advance robotic manipulation through vision, touch, and spatially grounded representations. His work focuses on algorithmic breakthroughs that enable long-horizon manipulation through combinatorial reasoning and generalization, on novel, geometrically grounded, and graph-based policy representations, and on integrating advanced sensing capabilities, such as event-based optical tactile sensing. Together, these components aim to facilitate the learning of dexterous skills and unlock more capable robotic manipulation.

Key References

Reinforcement Learning and Combinatorial Optimization for Robotic Assembly

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    Funk, N.; Chalvatzaki, G.; Belousov, B.; Peters, J. (2021). Learn2Assemble with Structured Representations and Search for Robotic Architectural Construction, Conference on Robot Learning (CoRL).
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    Funk, N.; Menzenbach, S.; Chalvatzaki, G.; Peters, J. (2022). Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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    Wibranek, B.; Liu, Y.; Funk, N.; Belousov, B.; Peters, J.; Tessmann, O. (2021). Reinforcement Learning for Sequential Assembly of SL-Blocks: Self-Interlocking Combinatorial Design Based on Machine Learning, Proceedings of the 39th eCAADe Conference.

Dexterous Manipulation / Grasp and Motion Planning

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    Funk, N.; Urain, J.; Carvalho, J.; Prasad, V.; Chalvatzaki, G.; Peters, J. (submitted). ActionFlow: Equivariant, Accurate, and Efficient Policies with Spatially Symmetric Flow Matching.
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    Urain, J.; Funk, N.; Peters, J.; Chalvatzaki G (2023). SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion, International Conference on Robotics and Automation (ICRA).
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    Funk, N.; Schaff, C.; Madan, R.; Yoneda, T.; Urain, J.; Watson, J.; Gordon, E.; Widmaier, F; Bauer, S.; Srinivasa, S.; Bhattacharjee, T.; Walter, M.; Peters, J. (2022). Benchmarking Structured Policies and Policy Optimization for Real-World Dexterous Object Manipulation, IEEE Robotics and Automation Letters (R-AL).
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    Bauer, S.; Wüthrich, W.; Widmaier, F.; Buchholz, A.; Stark, S.; Goyal, A.; Steinbrenner, T.; Akpo, J.; Joshi, S.; Berenz, V.; Agrawal, V.; Funk, N.; Urain, J.; Peters, J.; Watson, J.; Et, A.L.l (2021). Real Robot Challenge: A Robotics Competition in the Cloud, Advances in Neural Information Processing Systems (NIPS / NeurIPS).

Tactile Sensing

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    Funk, N.; Helmut, E.; Chalvatzaki, G.; Calandra, R.; Peters, J. (2024). Evetac: An Event-based Optical Tactile Sensor for Robotic Manipulation, IEEE Transactions on Robotics (T-RO), 40, pp.3812-3832.
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    Funk, N.; Chen, C.; Schneider, T.; Chalvatzaki, G.; Calandra, R.; Peters, J. (2025). On the Importance of Tactile Sensing for Imitation Learning: A Case Study on Robotic Match Lighting, ICRA 2025 Workshop on “Towards Human Level Intelligence Vision and Tactile Sensing”.
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    Lach, L.; Funk, N.; Haschke, R.; Lemaignan, S.; Ritter, H.; Peters, J.; Chalvatzaki, G. (2023). Placing by Touching: An empirical study on the importance of tactile sensing for precise object placing, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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    Funk, N.; Mueller, P.-O.; Belousov, B.; Savchenko, A.; Findeisen, R.; Peters, J. (2023). High-Resolution Pixelwise Contact Area and Normal Force Estimation for the GelSight Mini Visuotactile Sensor Using Neural Networks, Embracing Contacts-Workshop at ICRA 2023.
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    Helmut, E.; Dziarski, L.; Funk, N.; Belousov, B.; Peters, J. (2025). Learning Force Distribution Estimation for the GelSight Mini Optical Tactile Sensor Based on Finite Element Analysis, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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    Helmut, E.; Dziarski, L.; Funk, N.; Belousov, B.; Peters, J. (2024). Learning Force Distribution Estimation for the GelSight Mini Optical Tactile Sensor Based on Finite Element Analysis, 2nd NeurIPS Workshop on Touch Processing: From Data to Knowledge.
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    Helmut, E.; Funk, N.; Schneider, T.; de Farias, C.; Peters, J. (2026). Tactile-Conditioned Diffusion Policy for Force-Aware Robotic Manipulation, IEEE International Conference on Robotics and Automation (ICRA).
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    Afzal, N.; Funk, N.; Helmut, E.; Peters, J.; Ward-Cherrier, B. (submitted). Neuromorphic BrailleNet: Accurate and Generalizable Braille Reading Beyond Single Characters through Event-Based Optical Tactile Sensing, Submitted to the IEEE Robotics and Automation Letters (R-AL).

Other Publications

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    Liu, P.; Guenster, J.; Funk, N.; Groeger, S.; Chen, D.; Bou Ammar, H.; Jankowski, J.; Maric, A.; Calinon, S.; et, al.; Lioutikov, R.; Neumann, G.; Likmeta, A.; Zhalehmehrabi, A.; Bonenfant, T.; Restelli, M.; Tateo, D.; Liu, Z.; Peters, R. (2024). A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics, Advances in Neural Information Processing Systems.
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    Funk, N.; Baumann, D.; Berenz, V.; Trimpe, S. (2021). Learning event-triggered control from data through joint optimization, IFAC Journal of Systems and Control, 16, pp.100144.
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    Moeller, C.; Funk, N.; Peters, J. (2025). Particle-based 6D Object Pose Estimation from Point Clouds using Diffusion Models, 3rd Workshop on Generative Models for Computer Vision at CVPR 2025.