Gregor Gebhardt

Quick Info

Research Interests

Machine Learning, Robotics, Imitation Learning, Reinforcement Learning, Human-Robot Interaction

More Information

Curriculum Vitae Google Citations

Contact Information

Mail. Gregor Gebhardt
TU Darmstadt, FG CLAS,
Hochschulstr. 10, 64289 Darmstadt
Office. Room E327, Building S2|02

Gregor Gebhardt joined the Intelligent Autonomous System (IAS) lab as a PhD student in January, 2015. His research interests are in the areas of imitation learning, reinforcement learning and human-robot-interaction, as well as leveraging methods of these fields by embedding them into reproducing kernel Hilbert spaces.

Gregor started his studies in computer science at the Freie Universität Berlin, where he completed his Bachelor's degree with a thesis written under the supervision of Prof. Dr. Marc Toussaint and Dr. Tobias Lang. For his Master's studies he came to the Technische Universität Darmstadt, where the specialized Master's program "Autonomous Systems" gave him the opportunity to focus on the most interesting fields of computer science: machine learning, robotics, computer vision and control theory. He completed his Master's degree by a thesis entitled “Embedding Kalman Filters into Reproducing Kernel Hilbert Spaces", supervised by Prof. Dr. Gerhard Neumann and Prof. Dr. Jan Peters.

For his research as a PhD student, Gregor wants to focus on the interaction and collaboration of humans and robots, and particularly on how robots can learn from human experts. He believes that the daily and work routines of all of us will become more and more affected by intelligent machines and, thus, the transfer of human knowledge and proficiency to robots and machines is an important challenge addressed by the ongoing and future research. During his PhD studies, Gregor wants to contribute to the methods of imitation learning and reinforcement learning.

Research Interests

Machine Learning, Robotics, Imitation Learning, Reinforcement Learning, Human-Robot Interaction

Key References

  1. Gebhardt, G.H.W.; Kupcsik, A.G.; Neumann, G. (2017). The Kernel Kalman Rule - Efficient Nonparametric Inference with Recursive Least Squares, Proceedings of the National Conference on Artificial Intelligence (AAAI).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  2. Gebhardt, G.H.W.; Daun, K.; Schnaubelt, M.; Hendrich, A.; Kauth, D.; Neumann, G. (2017). Learning to Assemble Objects with a Robot Swarm, Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems (AAMAS 17), pp.1547--1549, International Foundation for Autonomous Agents and Multiagent Systems.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  3. Gebhardt, G.H.W.; Kupcsik, A.; Neumann, G. (2015). Learning Subspace Conditional Embedding Operators, Large-Scale Kernel Learning Workshop at ICML 2015.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]


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