Job, Scholarship & Internships Applications: Submit your application materials through our application website. Please do not send applications by email (You may not receive an answer due to the many spam applications; in some cases, we are legally bound to not consider it, and, potentially, flooding inboxes may bias us during future applications. Also, we know that you have not visited my homepage and read this text, which does not start us off on the right food). I will not even see copy & paste applications with megabytes of PDFs as my spam filter removes them, please do not expect an answer. If we have open positions, they will be listed at here. Scholarship applications (including DAAD WISE, CSC or other) and internships (we only host interns who bring their own funding) will also only be considered if send through our application website.
Prüfungspläne für Computational Engineering: Die Prüfungspläne bitte immer direkt per email an Bitte nicht per email an meine Adresse, da gehen sie bei 500 Emails pro Tag einfach unter!
Administration: Alle administrativen Angelegenheiten (Studienbüro, Personal, etc) bitte immer *DIREKT* an Ich kriege 500 Emails pro Tag und mehr als 100 Emails pro Tag bleiben daher unbearbeitet.
Aggressive Spam Filtering: Make sure to really address the email to me. My spam filter flags emails starting with Dear Sir... as spam. Please forgive me if I never get them...

Jan Peters

Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt. Until 2021, he was also an adjunct senior research scientist at the Max-Planck Institute for Intelligent Systems, where he heads the interdepartmental Robot Learning Group between the departments of Empirical Inference and Autonomous Motion. Jan Peters has received a few awards, most notably, he has received the Dick Volz Best US PhD Thesis Runner Up Award, the Robotics: Science & Systems - Early Career Spotlight, the IEEE Robotics & Automation Society's Early Career Award, and the International Neural Networks Society's Young Investigator Award.

Quick Info

Research Interests

Motor Control & Learning, Robotics, Machine Learning, Biomimetic Systems.

More Information

Curriculum Vitae Short Bio Publications Google Scholar DBLP ORCID LinkedIn Xing

Contact Information

Mail. TU Darmstadt, FB-Informatik, FG-IAS, Hochschulstr. 10, 64289 Darmstadt
Office. Room E314, Robert-Piloty-Gebaeude S2|02

To get an answer, see and follow my email rules!

Jan Peters graduated from the University of Hagen in 2000 with a Diplom-Informatiker (German M.Sc. in Computer Science) with a focus on artificial intelligence and from Munich University of Technology (TU Muenchen) in 2001 with a Diplom-Ingenieur Elektrotechnik (German M.Eng. in Electrical Engineering), majoring in automation & control. In 2000-2001, he spent two semesters as visiting student at National University of Singapore. Subsequently, he moved to University of Southern California (USC) where he completed another M.Sc. in Computer Science with a focus on Machine Learning and a M.Sc. in Aerospace and Mechanical Engineering with a major in nonlinear dynamics. During his studies, Jan Peters has been a visiting research student at the Department of Robotics at the German Aerospace Research Center in Germany, at Siemens Advanced Engineering in Singapore and at the Department of Humanoid Robotics and Computational Neuroscience at the Advanced Telecommunication Research (ATR) Center in Japan.

From 2001 to 2007, he was a graduate research assistant at the Computational Learning and Motor Control Lab at the Department of Computer Science at the University of Southern California (USC) in Los Angeles, USA, where Jan has been working with Stefan Schaal, Sethu Vijayakumar (now at U. Edinburgh, UK), and Firdaus Udwadia (Department of Mechanical Engineering). Jan Peters received his Ph.D. in Computer Science from the University of Southern California (USC). Chris Atkeson (Robotics Institute at CMU) and Gaurav Sukhatme also guided him to his thesis. In 2011, Jan Peters' PhD Thesis received the Dick Volz Best 2007 US PhD Thesis Runner Up Award based on thesis quality and thesis impact. Jan remains affiliated with the CLMC Lab as an invited researcher. After graduating from USC, Jan Peters became a full-time Senior Research Scientist and Robot Learning Group Leader in the Empirical Inference Department of Bernhard Schoelkopf at the Max-Planck Institute for Biological Cybernetics in 2007-2011 and at the Max-Planck Institute for Intelligent Systems since 2011. In 2011, Jan Peters joined the Technische Universitaet Damstadt as a full professor (W3) founding the Intelligent Autonomous Systems lab.

Short Bio


Topics. Computer Science (Dipl-Inform, MSc, PhD), Electrical Engineering (Dipl-Ing), Aerospace & Mechanical Engineering (MSc)
Universities. Technische Universität München, National University of Singapore (NUS), FernUni Hagen, University of Southern California (USC)

Research Experience

Deutsches Luft- & Raumfahrtzentrum (1997-2000)
ATR Research Center (2000,2003), Japan
University of Southern California (2001-2007), USA
Max Planck Institute for Biological Cybernetics (2007-2010)
Max Planck Institute for Intelligent Systems (2011-now)
Technische Universität Darmstadt (2011-now)

Notable Awards

Dick Volz Best 2007 US PhD Thesis Runner Up Award
ICINCO 2010 Best Paper Awards and Honors
Robotics:Science & Systems 2012 Early Career Spotlight
IROS Best Cognitive Robotics Paper Award 2012
2013 IEEE Robotics & Automation Early Career Award
INNS Young Investigator Award 2013
ICRA Best Cognitive Robotics Paper Award 2014
ERC Starting Grant 2014
IEEE Brain Initiative Best Paper Award 2016
IEEE Fellow 2019

Jan Peters cares deeply about student supervision and has been blessed by some of the most outstanding students. His first Ph.D. graduates, Duy Nguyen-Tuong, Jens Kober, Katharina Muelling and Zhikun Wang have been extremely successful during their doctoral studies as well as after their post-graduation. Jens Kober has even won the Georges Giralt PhD Award for the best Robotics PhD thesis in Europe in 2013. His Master's thesis students have gotten Ph.D. positions offered at many prestigious places including MIT, CMU, UW, UC Berkeley, Brown University, Oxford, KTH, Max Planck Institutes, etc. He hopes all current and future students will do equally well.

As Jan Peters' research lies at the intersection between two fields, i.e., machine learning and robotics, he has always keen to bring members of both fields together. To do so, he has organized three NIPS workshops (Towards a New Reinforcement Learning!, Robotics Challenges for Machine Learning and Probabilistic Approaches for Robotics and Control), three R:SS Workshops (Learning for Locomotion, Bridging the gap between high-level discrete representations and low-level continuous behaviors and Towards Closing the Loop: Active Learning for Robotics), three IROS workshops (From motor to interaction learning in robots, Robotics Challenges for Machine Learning II, and Beyond Robot Grasping - Modern Approaches for Learning Dynamic Manipulation), two ICRA workshops (Approaches to Sensorimotor Learning on Humanoid Robots, Novel Methods for Learning and Optimization of Control Policies and Trajectories for Robotics) and one ECAI workshop (The 6th International Cognitive Robotics Workshop). His Co-Organizers included Pieter Abbeel (U. Berkeley), Drew Bagnell (CMU), Andreas Krause (CalTech), Dana Kulic (U. Waterloo), Ruben Martinez-Cantin (IST Lisbon/U.Zaragoza), Jun Morimoto (ATR), Ashutosh Saxena (Cornell), Nick Roy (MIT), Stefan Schaal (USC), Olivier Sigaud (U.Paris 6), Russ Tedrake (MIT), Marc Toussaint (TU Berlin), Sethu Vijayakumar (U.Edingburgh), Gerhard Lakemeyer (RWTH Aachen, Germany), Yves Lesperance (York University, Canada), Fiora Pirri (University of Rome "La Sapienza", Italy), Ales Ude (Josef Stefan Institute, Slovenia), Tamim Asfour (U.Karlsruhe).

Together with Nick Roy (MIT), Russ Tedrake (MIT), Jun Morimoto (ATR), Jan Peters founded the IEEE Robotics and Automation Society's Technical Committee on Robot Learning which won the Most Active Technical Committee Award. Jan Peters and Andrew Y. Ng (Stanford) have edited a Special Issue on Robot Learning in the Autonomous Robots journal. Jan Peters has been an Area Chair at Robotics: Science & Systems (R:SS), at the European Conference on Machine Learning (ECML), at the International Conference on Neural Networks (ICANN), at International Conference on Artificial Intelligence & Statistics (AIStats) and at Advances in Neural Information Processing Systems (NIPS). Jan Peters has been an Associate Editor for the IEEE Transactions on Robotics and Jan Peters is an action editor for the Journal of Machine Learning Research (JMLR) since 2013.

Key References

  1. Peters, J.; Muelling, K.; Altun, Y. (2010). Relative Entropy Policy Search, Proceedings of the Twenty-Fourth National Conference on Artificial Intelligence (AAAI), Physically Grounded AI Track.   Download Article [PDF]   BibTeX Reference [BibTex]
  2. Peters, J.; Schaal, S. (2008). Reinforcement learning of motor skills with policy gradients, Neural Networks, 21, 4, pp.682-97.   Download Article [PDF]   BibTeX Reference [BibTex]
  3. Peters, J.; Schaal, S. (2008). Natural actor critic, Neurocomputing, 71, 7-9, pp.1180-1190.   Download Article [PDF]   BibTeX Reference [BibTex]
  4. Peters, J.;Schaal, S. (2007). Reinforcement learning by reward-weighted regression for operational space control, Proceedings of the International Conference on Machine Learning (ICML2007).   Download Article [PDF]   BibTeX Reference [BibTex] where a longer version appeared as Peters, J.; Schaal, S. (2008). Learning to control in operational space, International Journal of Robotics Research (IJRR), 27, pp.197-212.   Download Article [PDF]   BibTeX Reference [BibTex]
  5. Kober, J.; Peters, J. (2009). Policy Search for Motor Primitives in Robotics, Advances in Neural Information Processing Systems 22 (NIPS/NeurIPS), Cambridge, MA: MIT Press.   Download Article [PDF]   BibTeX Reference [BibTex] where a longer version appeared as Kober, J.; Peters, J. (2011). Policy Search for Motor Primitives in Robotics, Machine Learning (MLJ), 84, 1-2, pp.171-203.   Download Article [PDF]   BibTeX Reference [BibTex]
  6. Peters, J.;Mistry, M.;Udwadia, F. E.;Nakanishi, J.;Schaal, S. (2008). A unifying framework for robot control with redundant DOFs, Autonomous Robots (AURO), 24, 1, pp.1-12.   Download Article [PDF]   BibTeX Reference [BibTex]
  7. Nguyen Tuong, D.; Seeger, M.; Peters, J. (2009). Local Gaussian Process Regression for Real Time Online Model Learning and Control, Advances in Neural Information Processing Systems 22 (NIPS/NeurIPS), Cambridge, MA: MIT Press.   Download Article [PDF]   BibTeX Reference [BibTex] where a longer version appeared as Nguyen Tuong, D.; Seeger, M.; Peters, J. (2009). Model Learning with Local Gaussian Process Regression, Advanced Robotics, 23, 15, pp.2015-2034.   Download Article [PDF]   BibTeX Reference [BibTex]
  8. Kober, J.; Oztop, E.; Peters, J. (2011). Reinforcement Learning to adjust Robot Movements to New Situations, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Best Paper Track.   Download Article [PDF]   BibTeX Reference [BibTex] where a longer version appeared as Kober, J.; Wilhelm, A.; Oztop, E.; Peters, J. (2012). Reinforcement Learning to Adjust Parametrized Motor Primitives to New Situations, Autonomous Robots (AURO), 33, 4, pp.361-379, Springer US.   Download Article [PDF]   BibTeX Reference [BibTex]
  9. Muelling, K.; Kober, J.; Peters, J. (2011). A Biomimetic Approach to Robot Table Tennis, Adaptive Behavior Journal, 19, 5.   Download Article [PDF]   BibTeX Reference [BibTex]
  10. Muelling, K.; Kober, J.; Kroemer, O.; Peters, J. (2013). Learning to Select and Generalize Striking Movements in Robot Table Tennis, International Journal of Robotics Research (IJRR), 32, 3, pp.263-279.   Download Article [PDF]   BibTeX Reference [BibTex]

Privately, Jan Peters enjoys Adventures, Sailing, and Skiing.


zum Seitenanfang