Niklas Funk

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.

Currently, Niklas is working in the broad field of autonomous robotic assembly. On the one hand, he is exploring algorithms enabling combinatorial reasoning and generalization. In particular, his recent research focuses on combining learned graph-based representations with powerful inductive biases and model-based search. On the other hand, he is also interested in developing fine, precise, and dexterous robotic assembly skills, eventually adding tactile feedback.

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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    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).

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.

Event-triggered Control

• •       Bib
    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.