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Jan Peters

Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt and, at the same time, he is the dept head of the research department on Systems AI for Robot Learning (SAIROL) at the German Research Center for Artificial Intelligence (Deutsches Forschungszentrum für Künstliche Intelligenz, DFKI). He is also is a founding research faculty member of The Hessian Center for Artificial Intelligence.

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. From 2010 until 2021, he was also an adjunct senior research scientist at the Max-Planck Institute for Intelligent Systems, where he used to head 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.

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). He has also been program co-chair at the Conference on Robot Learning in 2018 and 2022 and at IEEE-RAS International Conference on Humanoid Robots (Humanoids 2018).

Jan Peters cares deeply about student and postdoc supervision and has been blessed by some of the most outstanding students. His more than twenty-five graduated Ph.D. students and more than a dozen former postdocs have done outstandingly well in terms of quality and quantity of research results, awards (including both winners and runner-ups of Georges Giralt PhD Award for the best Robotics PhD thesis in Europe), publications ... and job offers! His alumni have started professorships at prestigious universities in the USA (Carnegie Mellon University, Rutgers University, University of Utah, University of Texas at Austin, ...), in Asia (Kyushu Tech, RIKEN AIP, University of Tokyo, Chinese Academy of Science) and in Europe (KIT, TU Delft, University of Amsterdam, Aalto University, Lübeck University, Leoben University, Imperial College, University College London, ...) as well as major IT companies (Amazon, Facebook, Google, ...) as well as many major AI labs at more general companies (Bosch Centre for AI, Honda Research Institute, Porsche Motor Sports Lab, Volkswagen AI Lab). Even his Master's and Bachelor's thesis students have gotten Ph.D. positions offered at many prestigious places including MIT, CMU, UW, UC Berkeley, Brown University, Georgia Tech, Oxford, KTH, Max Planck Institutes, etc. He hopes all current and future students will do equally well.

Recent Publications

Buechler, D.; Calandra, R.; Peters, J. (2023). Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots, Robotics and Autonomous Systems, 159, 104230.   Download Article [PDF]   BibTeX Reference [BibTex]

Lutter, M.; Peters, J. (2023). Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models, International Journal of Robotics Research (IJRR), 42, 3.   Download Article [PDF]   BibTeX Reference [BibTex]

Look, A.; Kandemir, M.; Rakitsch, B.; Peters, J. (2023). A Deterministic Approximation to Neural SDEs, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 45, 4, pp.4023-4037.   BibTeX Reference [BibTex]

Liu, Y.; Belousov, B.; Funk, N.; Chalvatzaki, G.; Peters, J.; Tessman, O. (2023). Auto(mated)nomous Assembly, International Conference on Trends on Construction in the Post-Digital Era, pp.167-181, Springer, Cham.   Download Article [PDF]   BibTeX Reference [BibTex]

Liu, P.; Zhang, K.; Tateo, D.; Jauhri, S.; Hu, Z.; Peters, J. Chalvatzaki, G. (2023). Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction, 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE.   Download Article [PDF]   BibTeX Reference [BibTex]

Zelch, C.; Peters, J.; von Stryk, C. (2023). Start State Selection for Control Policy Learning from Optimal Trajectories, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).   Download Article [PDF]   BibTeX Reference [BibTex]

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).   Download Article [PDF]   BibTeX Reference [BibTex]

Hansel, K.; Urain, J.; Peters, J.; Chalvatzaki, G. (2023). Hierarchical Policy Blending as Inference for Reactive Robot Control, 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE.   Download Article [PDF]   BibTeX Reference [BibTex]

Le, A. T.; Hansel, K.; Peters, J.; Chalvatzaki, G. (2023). Hierarchical Policy Blending As Optimal Transport, 5th Annual Learning for Dynamics & Control Conference (L4DC), PMLR.   Download Article [PDF]   BibTeX Reference [BibTex]

Luis, C.; Bottero, A.G.; Vinogradska, J.; Berkenkamp, F.; Peters, J. (2023). Model-Based Uncertainty in Value Functions, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).   BibTeX Reference [BibTex]

Al-Hafez, F.; Tateo, D.; Arenz, O.; Zhao, G.; Peters, J. (2023). LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning, International Conference on Learning Representations (ICLR).   Download Article [PDF]   BibTeX Reference [BibTex]

Palenicek, D.; Lutter, M.; Carvalho, J.; Peters, J. (2023). Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning, International Conference on Learning Representations (ICLR).   Download Article [PDF]   BibTeX Reference [BibTex]

Buechler, D.; Guist, S.; Calandra, R.; Berenz, V.; Schoelkopf, B.; Peters, J. (2023). Learning to Play Table Tennis From Scratch using Muscular Robots, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE T-TRo Track.   Download Article [PDF]   BibTeX Reference [BibTex]

Urain, J.; Tateo, D.; Peters, J. (2023). Learning Stable Vector Fields on Lie Groups, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE R-AL Track.   BibTeX Reference [BibTex]

Bjelonic, F.; Lee, J.; Arm, P.; Sako, D.; Tateo, D.; Peters, J.; Hutter, M. (2023). Learning-Based Design and Control for Quadrupedal Robots With Parallel-Elastic Actuators, IEEE Robotics and Automation Letters, 8, 3, pp.1611-1618.   Download Article [PDF]   BibTeX Reference [BibTex]

Bethge, J.; Pfefferkorn, M.; Rose, A.; Peters, J.; Findeisen, R. (2023). Model predictive control with Gaussian-process-supported dynamical constraints for autonomous vehicles, Proceedings of the 22nd World Congress of the International Federation of Automatic Control.   Download Article [PDF]   BibTeX Reference [BibTex]

Parisi, S.; Tateo, D.; Hensel, M.; D'Eramo, C.; Peters, J.; Pajarinen, J. (2022). Long-Term Visitation Value for Deep Exploration in Sparse Reward Reinforcement Learning, Algorithms, 15, 3, pp.81.   Download Article [PDF]   BibTeX Reference [BibTex]

Akrour, R.; Tateo, D.; Peters, J. (2022). Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 44, 10, pp.6795-6806.   Download Article [PDF]   BibTeX Reference [BibTex]

Loeckel, S.; Kretschi, A.; van Vliet, P.; Peters, J. (2022). Identification and modelling of race driving styles, Vehicle System Dynamics, 60, 8, pp.2890--2918.   Download Article [PDF]   BibTeX Reference [BibTex]

Tosatto, S.; Carvalho, J.; Peters, J. (2022). Batch Reinforcement Learning with a Nonparametric Off-Policy Policy Gradient, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 44, 10, pp.5996--6010.   Download Article [PDF]   BibTeX Reference [BibTex]

Belousov, B.; Wibranek, B.; Schneider, J.; Schneider, T.; Chalvatzaki, G.; Peters, J.; Tessmann, O. (2022). Robotic Architectural Assembly with Tactile Skills: Simulation and Optimization, Automation in Construction, 133, pp.104006.   Download Article [PDF]   BibTeX Reference [BibTex]

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, 7, 1, pp.478-485.   Download Article [PDF]   BibTeX Reference [BibTex]

Muratore, F.; Ramos, F.; Turk, G.; Yu, W.; Gienger, M.; Peters, J. (2022). Robot Learning from Randomized Simulations: A Review, Frontiers in Robotics and AI, 9.   Download Article [PDF]   BibTeX Reference [BibTex]

You, B.; Arenz, O.; Chen, Y.; Peters, J. (2022). Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from Images, Neurocomputing.   Download Article [PDF]   BibTeX Reference [BibTex]

Klink, P.; D`Eramo, C.; Peters, J.; Pajarinen, J. (2022). Boosted Curriculum Reinforcement Learning, International Conference on Learning Representations (ICLR).   Download Article [PDF]   BibTeX Reference [BibTex]

Memmel, M.; Liu, P.; Tateo, D.; Peters, J. (2022). Dimensionality Reduction and Prioritized Exploration for Policy Search, 25th International Conference on Artificial Intelligence and Statistics (AISTATS).   Download Article [PDF]   BibTeX Reference [BibTex]

Flynn, H.; Reeb, D.; Kandemir, M.; Peters, J. (2022). PAC-Bayesian Lifelong Learning For Multi-Armed Bandits, Data Mining and Knowledge Discovery, 36, 2, pp.841-876.   Download Article [PDF]   BibTeX Reference [BibTex]

Prasad, V.; Stock-Homburg, R.; Peters, J. (2022). Human-Robot Handshaking: A Review, International Journal of Social Robotics (IJSR), 14, 1, pp.277-293.   Download Article [PDF]   BibTeX Reference [BibTex]

Zheng, Y.; Veiga, F.F.; Peters, J.; Santos, V.J. (2022). Autonomous Learning of Page Flipping Movements via Tactile Feedback, IEEE Transactions on Robotics (T-Ro), 38, 5, pp.2734 - 2749.   BibTeX Reference [BibTex]

Palenicek, D.; Lutter, M., Peters, J. (2022). Revisiting Model-based Value Expansion, Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM).   Download Article [PDF]   BibTeX Reference [BibTex]

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