Jan Peters

Research Group Leader

Alumni

Empirical Inference

http://www.ias.informatik.tu-dar...

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 a senior research scientist and group leader at the Max-Planck Institute for Intelligent Systems, where he heads the interdepartmental Robot Learning Group. Jan Peters has received the Dick Volz Best 2007 US PhD Thesis Runner-Up Award, the 2012 Robotics: Science & Systems - Early Career Spotlight, the 2013 INNS Young Investigator Award, and the IEEE Robotics & Automation Society‘s 2013 Early Career Award. In 2015, he was awarded an ERC Starting Grant. In 2019, he was appointed IEEE Fellow.

Jan Peters has studied Computer Science, Electrical, Mechanical and Control Engineering at TU Munich and FernUni Hagen in Germany, at the National University of Singapore (NUS) and the University of Southern California (USC), and been an visiting researcher at the ATR Telecomunications Research Center in Japan. He has received four Master‘s degrees in these disciplines as well as a Computer Science Ph.D. from USC.

If you are interested in working with me, please either apply through http://www.ausy.tu-darmstadt.de/Main/OpenPositions or send an official application to Ms. Lidia Pavel (email: lpavel@tuebingen.mpg.de, tel:+49 7071 601 1741). PLEASE REFRAIN FROM SENDING ME YOUR APPLICATION BY EMAIL! You will not get a reply as they will get lost in my aggressive spam filter.

Robot Learning Reinforcement Learning Machine Learning Robotics Biomimetic Systems

I am interested in many topics. Among them are

For more information see my private homepage and on my group's homepage.

Jan Peters is a full professor at Darmstadt University of Technology (TU Darmstadt) heading the intelligent systems group while still being affliated with the MPI heading the robot learning lab.

Jan Peters joined the Max-Planck Institute for Biological Cybernetics in April 2007 as a research scientist. Before joining MPI, Jan Peters studied Electrical Engineering, Computer Science and Mechanical Engineering. He graduated from the University of Hagen in 2000 with a Diplom-Informatiker (German M.Sc. in Computer Science) and from Munich University of Technology in 2001 with a Diplom-Ingenieur in Electrical Engineering (German M.Sc. in Electrical Engineering). In 2000-2001, he spent two semesters as visiting student at National University of Singapore. In 2002, he completed a M.Sc. in Computer Science and, in 2005, a M.S. in Mechanical Engineering both from USC. He joined USC's Computer Science Ph.D. program and the CLMC Lab in Fall 2001. 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 Advanded Telecommunication Research (ATR) Center in Japan. Jan Peters graduated from University of Southern California with a Ph.D. in Computer Science in March 2007. He remains affiliated with the CLMC Lab as an adjunct researcher.

More information on Jan Peters can be found on his private homepage.

Learning for Control

Figure: Muscle-based robotic arm serving as a testbed for Learning for Control. While it offers unique possibilities in terms of high accelerat...

Jan Peters Dieter Büchler Simon Guist Shane Gu Okan Koc

Robotics

Creating autonomous robots that can learn to assist humans in situations of daily life is a fascinating challenge for machine learning. While this aim has been a long-standing vision of artificial intelligence, we have yet to create robots that can learn to accomplish many different tasks triggered by environmental context or higher...
Jan Peters Sebastian Gomez-Gonzalez Dieter Büchler Okan Koc Yassine Nemmour Simon Guist Ge Li

377 results (View BibTeX file of all listed publications)

2020

Bayesian Online Prediction of Change Points

Agudelo-España, D., Gomez-Gonzalez, S., Bauer, S., Schölkopf, B., Peters, J.
Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), August 2020 (conference) Accepted

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[BibTex]

2020

ei Agudelo-España, D., Gomez-Gonzalez, S., Bauer, S., Schölkopf, B., Peters, J. Bayesian Online Prediction of Change Points Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), August 2020 (conference) Accepted

[BibTex]

Adaptation and Robust Learning of Probabilistic Movement Primitives

Gomez-Gonzalez, S., Neumann, G., Schölkopf, B., Peters, J.
IEEE Transactions on Robotics, 36(2):366-379, IEEE, March 2020 (article)

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arXiv DOI Project Page [BibTex]

ei Gomez-Gonzalez, S., Neumann, G., Schölkopf, B., Peters, J. Adaptation and Robust Learning of Probabilistic Movement Primitives IEEE Transactions on Robotics, 36(2):366-379, IEEE, March 2020 (article)

arXiv DOI Project Page [BibTex]

Real Time Trajectory Prediction Using Deep Conditional Generative Models

Gomez-Gonzalez, S., Prokudin, S., Schölkopf, B., Peters, J.
IEEE Robotics and Automation Letters, 5(2):970-976, IEEE, January 2020 (article)

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arXiv DOI [BibTex]

ei ps Gomez-Gonzalez, S., Prokudin, S., Schölkopf, B., Peters, J. Real Time Trajectory Prediction Using Deep Conditional Generative Models IEEE Robotics and Automation Letters, 5(2):970-976, IEEE, January 2020 (article)

arXiv DOI [BibTex]

Learning to Play Table Tennis From Scratch using Muscular Robots

Büchler, D., Guist, S., Calandra, R., Berenz, V., Schölkopf, B., Peters, J.
2020 (article) Submitted

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arXiv Project Page Video [BibTex]

ei sg am Büchler, D., Guist, S., Calandra, R., Berenz, V., Schölkopf, B., Peters, J. Learning to Play Table Tennis From Scratch using Muscular Robots 2020 (article) Submitted

arXiv Project Page Video [BibTex]

2019

Neural Signatures of Motor Skill in the Resting Brain

Ozdenizci, O., Meyer, T., Wichmann, F., Peters, J., Schölkopf, B., Cetin, M., Grosse-Wentrup, M.
Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2019), pages: 4387-4394, IEEE, October 2019 (conference)

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DOI [BibTex]

2019

ei Ozdenizci, O., Meyer, T., Wichmann, F., Peters, J., Schölkopf, B., Cetin, M., Grosse-Wentrup, M. Neural Signatures of Motor Skill in the Resting Brain Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2019), pages: 4387-4394, IEEE, October 2019 (conference)

DOI [BibTex]

Projections for Approximate Policy Iteration Algorithms

Akrour, R., Pajarinen, J., Peters, J., Neumann, G.
Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 181-190, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

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link (url) [BibTex]

ei Akrour, R., Pajarinen, J., Peters, J., Neumann, G. Projections for Approximate Policy Iteration Algorithms Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 181-190, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

link (url) [BibTex]

Switching Linear Dynamics for Variational Bayes Filtering

Becker-Ehmck, P., Peters, J., van der Smagt, P.
Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 553-562, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

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link (url) [BibTex]

ei Becker-Ehmck, P., Peters, J., van der Smagt, P. Switching Linear Dynamics for Variational Bayes Filtering Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 553-562, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

link (url) [BibTex]

Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

Lutter, M., Ritter, C., Peters, J.
7th International Conference on Learning Representations (ICLR), May 2019 (conference)

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link (url) [BibTex]

ei Lutter, M., Ritter, C., Peters, J. Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning 7th International Conference on Learning Representations (ICLR), May 2019 (conference)

link (url) [BibTex]

TD-regularized actor-critic methods

Parisi, S., Tangkaratt, V., Peters, J., Khan, M. E.
Machine Learning, 108(8):1467-1501, (Editors: Karsten Borgwardt, Po-Ling Loh, Evimaria Terzi, and Antti Ukkonen), 2019 (article)

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DOI [BibTex]

ei Parisi, S., Tangkaratt, V., Peters, J., Khan, M. E. TD-regularized actor-critic methods Machine Learning, 108(8):1467-1501, (Editors: Karsten Borgwardt, Po-Ling Loh, Evimaria Terzi, and Antti Ukkonen), 2019 (article)

DOI [BibTex]

Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots

Büchler, D., Calandra, R., Peters, J.
2019 (article) Submitted

Abstract

High-speed and high-acceleration movements are inherently hard to control. Applying learning to the control of such motions on anthropomorphic robot arms can improve the accuracy of the control but might damage the system. The inherent exploration of learning approaches can lead to instabilities and the robot reaching joint limits at high speeds. Having hardware that enables safe exploration of high-speed and high-acceleration movements is therefore desirable. To address this issue, we propose to use robots actuated by Pneumatic Artificial Muscles (PAMs). In this paper, we present a four degrees of freedom (DoFs) robot arm that reaches high joint angle accelerations of up to 28000 °/s^2 while avoiding dangerous joint limits thanks to the antagonistic actuation and limits on the air pressure ranges. With this robot arm, we are able to tune control parameters using Bayesian optimization directly on the hardware without additional safety considerations. The achieved tracking performance on a fast trajectory exceeds previous results on comparable PAM-driven robots. We also show that our system can be controlled well on slow trajectories with PID controllers due to careful construction considerations such as minimal bending of cables, lightweight kinematics and minimal contact between PAMs and PAMs with the links. Finally, we propose a novel technique to control the the co-contraction of antagonistic muscle pairs. Experimental results illustrate that choosing the optimal co-contraction level is vital to reach better tracking performance. Through the use of PAM-driven robots and learning, we do a small step towards the future development of robots capable of more human-like motions.

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Arxiv Video [BibTex]

ei Büchler, D., Calandra, R., Peters, J. Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots 2019 (article) Submitted

Arxiv Video [BibTex]

Entropic Regularization of Markov Decision Processes

Belousov, B., Peters, J.
Entropy, 21(7):674, 2019 (article)

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link (url) DOI [BibTex]

ei Belousov, B., Peters, J. Entropic Regularization of Markov Decision Processes Entropy, 21(7):674, 2019 (article)

link (url) DOI [BibTex]

Learning Intention Aware Online Adaptation of Movement Primitives

Koert, D., Pajarinen, J., Schotschneider, A., Trick, S., Rothkopf, C., Peters, J.
IEEE Robotics and Automation Letters, 4(4):3719-3726, 2019 (article)

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DOI [BibTex]

ei Koert, D., Pajarinen, J., Schotschneider, A., Trick, S., Rothkopf, C., Peters, J. Learning Intention Aware Online Adaptation of Movement Primitives IEEE Robotics and Automation Letters, 4(4):3719-3726, 2019 (article)

DOI [BibTex]

How Cognitive Models of Human Body Experience Might Push Robotics

Schürmann, T., Mohler, B. J., Peters, J., Beckerle, P.
Frontiers in Neurorobotics, 13(14), 2019 (article)

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DOI [BibTex]

ei Schürmann, T., Mohler, B. J., Peters, J., Beckerle, P. How Cognitive Models of Human Body Experience Might Push Robotics Frontiers in Neurorobotics, 13(14), 2019 (article)

DOI [BibTex]

Reliable Real-Time Ball Tracking for Robot Table Tennis

Gomez-Gonzalez, S., Nemmour, Y., Schölkopf, B., Peters, J.
Robotics, 8(4):90, 2019 (article)

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link (url) DOI [BibTex]

ei Gomez-Gonzalez, S., Nemmour, Y., Schölkopf, B., Peters, J. Reliable Real-Time Ball Tracking for Robot Table Tennis Robotics, 8(4):90, 2019 (article)

link (url) DOI [BibTex]

Learning Trajectory Distributions for Assisted Teleoperation and Path Planning

Ewerton, M., Arenz, O., Maeda, G., Koert, D., Kolev, Z., Takahashi, M., Peters, J.
Frontiers in Robotics and AI, 6, pages: 89, 2019 (article)

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DOI [BibTex]

ei Ewerton, M., Arenz, O., Maeda, G., Koert, D., Kolev, Z., Takahashi, M., Peters, J. Learning Trajectory Distributions for Assisted Teleoperation and Path Planning Frontiers in Robotics and AI, 6, pages: 89, 2019 (article)

DOI [BibTex]

Optimizing the Execution of Dynamic Robot Movements With Learning Control

Koc, O., Maeda, G., Peters, J.
IEEE Transactions on Robotics, pages: 1-16, 2019 (article)

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arXiv link (url) DOI Project Page [BibTex]

ei Koc, O., Maeda, G., Peters, J. Optimizing the Execution of Dynamic Robot Movements With Learning Control IEEE Transactions on Robotics, pages: 1-16, 2019 (article)

arXiv link (url) DOI Project Page [BibTex]

Learning to Serve: An Experimental Study for a New Learning From Demonstrations Framework

Koc, O., Peters, J.
IEEE Robotics and Automation Letters, 4(2):1784-1791, 2019 (article)

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link (url) DOI Project Page [BibTex]

ei Koc, O., Peters, J. Learning to Serve: An Experimental Study for a New Learning From Demonstrations Framework IEEE Robotics and Automation Letters, 4(2):1784-1791, 2019 (article)

link (url) DOI Project Page [BibTex]

Compatible natural gradient policy search

Pajarinen, J., Thai, H. L., Akrour, R., Peters, J., Neumann, G.
Machine Learning, 108(8):1443-1466, (Editors: Karsten Borgwardt, Po-Ling Loh, Evimaria Terzi, and Antti Ukkonen), 2019 (article)

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DOI [BibTex]

ei Pajarinen, J., Thai, H. L., Akrour, R., Peters, J., Neumann, G. Compatible natural gradient policy search Machine Learning, 108(8):1443-1466, (Editors: Karsten Borgwardt, Po-Ling Loh, Evimaria Terzi, and Antti Ukkonen), 2019 (article)

DOI [BibTex]

2018

Regularizing Reinforcement Learning with State Abstraction

Akrour, R., Veiga, F., Peters, J., Neuman, G.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2018 (conference) Accepted

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link (url) Project Page [BibTex]

2018

ei Akrour, R., Veiga, F., Peters, J., Neuman, G. Regularizing Reinforcement Learning with State Abstraction Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2018 (conference) Accepted

link (url) Project Page [BibTex]

Learning to Categorize Bug Reports with LSTM Networks

Gondaliya, K., Peters, J., Rueckert, E.
Proceedings of the 10th International Conference on Advances in System Testing and Validation Lifecycle (VALID), pages: 7-12, October 2018 (conference)

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link (url) [BibTex]

ei Gondaliya, K., Peters, J., Rueckert, E. Learning to Categorize Bug Reports with LSTM Networks Proceedings of the 10th International Conference on Advances in System Testing and Validation Lifecycle (VALID), pages: 7-12, October 2018 (conference)

link (url) [BibTex]

Domain Randomization for Simulation-Based Policy Optimization with Transferability Assessment

Muratore, F., Treede, F., Gienger, M., Peters, J.
2nd Annual Conference on Robot Learning (CoRL), 87, pages: 700-713, Proceedings of Machine Learning Research, PMLR, October 2018 (conference)

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link (url) Project Page [BibTex]

ei Muratore, F., Treede, F., Gienger, M., Peters, J. Domain Randomization for Simulation-Based Policy Optimization with Transferability Assessment 2nd Annual Conference on Robot Learning (CoRL), 87, pages: 700-713, Proceedings of Machine Learning Research, PMLR, October 2018 (conference)

link (url) Project Page [BibTex]

Control of Musculoskeletal Systems using Learned Dynamics Models

Büchler, D., Calandra, R., Schölkopf, B., Peters, J.
IEEE Robotics and Automation Letters, Robotics and Automation Letters, 3(4):3161-3168, IEEE, 2018 (article)

Abstract

Controlling musculoskeletal systems, especially robots actuated by pneumatic artificial muscles, is a challenging task due to nonlinearities, hysteresis effects, massive actuator de- lay and unobservable dependencies such as temperature. Despite such difficulties, muscular systems offer many beneficial prop- erties to achieve human-comparable performance in uncertain and fast-changing tasks. For example, muscles are backdrivable and provide variable stiffness while offering high forces to reach high accelerations. In addition, the embodied intelligence deriving from the compliance might reduce the control demands for specific tasks. In this paper, we address the problem of how to accurately control musculoskeletal robots. To address this issue, we propose to learn probabilistic forward dynamics models using Gaussian processes and, subsequently, to employ these models for control. However, Gaussian processes dynamics models cannot be set-up for our musculoskeletal robot as for traditional motor- driven robots because of unclear state composition etc. We hence empirically study and discuss in detail how to tune these approaches to complex musculoskeletal robots and their specific challenges. Moreover, we show that our model can be used to accurately control an antagonistic pair of pneumatic artificial muscles for a trajectory tracking task while considering only one- step-ahead predictions of the forward model and incorporating model uncertainty.

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RAL18final link (url) DOI Project Page [BibTex]

ei Büchler, D., Calandra, R., Schölkopf, B., Peters, J. Control of Musculoskeletal Systems using Learned Dynamics Models IEEE Robotics and Automation Letters, Robotics and Automation Letters, 3(4):3161-3168, IEEE, 2018 (article)

RAL18final link (url) DOI Project Page [BibTex]

Constraint-Space Projection Direct Policy Search

Akrour, R., Peters, J., Neuman, G.
14th European Workshop on Reinforcement Learning (EWRL), October 2018 (conference)

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link (url) Project Page [BibTex]

ei Akrour, R., Peters, J., Neuman, G. Constraint-Space Projection Direct Policy Search 14th European Workshop on Reinforcement Learning (EWRL), October 2018 (conference)

link (url) Project Page [BibTex]

PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos

Parmas, P., Rasmussen, C., Peters, J., Doya, K.
Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 4065-4074, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)

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link (url) Project Page [BibTex]

ei Parmas, P., Rasmussen, C., Peters, J., Doya, K. PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 4065-4074, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)

link (url) Project Page [BibTex]

Inducing Probabilistic Context-Free Grammars for the Sequencing of Movement Primitives

Lioutikov, R., Maeda, G., Veiga, F., Kersting, K., Peters, J.
IEEE International Conference on Robotics and Automation, (ICRA), pages: 1-8, IEEE, May 2018 (conference)

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DOI Project Page [BibTex]

ei Lioutikov, R., Maeda, G., Veiga, F., Kersting, K., Peters, J. Inducing Probabilistic Context-Free Grammars for the Sequencing of Movement Primitives IEEE International Conference on Robotics and Automation, (ICRA), pages: 1-8, IEEE, May 2018 (conference)

DOI Project Page [BibTex]

Assisting Movement Training and Execution With Visual and Haptic Feedback

Ewerton, M., Rother, D., Weimar, J., Kollegger, G., Wiemeyer, J., Peters, J., Maeda, G.
Frontiers in Neurorobotics, 12, May 2018 (article)

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DOI Project Page [BibTex]

ei Ewerton, M., Rother, D., Weimar, J., Kollegger, G., Wiemeyer, J., Peters, J., Maeda, G. Assisting Movement Training and Execution With Visual and Haptic Feedback Frontiers in Neurorobotics, 12, May 2018 (article)

DOI Project Page [BibTex]

Learning Coupled Forward-Inverse Models with Combined Prediction Errors

Koert, D., Maeda, G., Neumann, G., Peters, J.
IEEE International Conference on Robotics and Automation, (ICRA), pages: 2433-2439, IEEE, May 2018 (conference)

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DOI Project Page [BibTex]

ei Koert, D., Maeda, G., Neumann, G., Peters, J. Learning Coupled Forward-Inverse Models with Combined Prediction Errors IEEE International Conference on Robotics and Automation, (ICRA), pages: 2433-2439, IEEE, May 2018 (conference)

DOI Project Page [BibTex]

Sample and Feedback Efficient Hierarchical Reinforcement Learning from Human Preferences

Pinsler, R., Akrour, R., Osa, T., Peters, J., Neumann, G.
IEEE International Conference on Robotics and Automation, (ICRA), pages: 596-601, IEEE, May 2018 (conference)

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DOI Project Page [BibTex]

ei Pinsler, R., Akrour, R., Osa, T., Peters, J., Neumann, G. Sample and Feedback Efficient Hierarchical Reinforcement Learning from Human Preferences IEEE International Conference on Robotics and Automation, (ICRA), pages: 596-601, IEEE, May 2018 (conference)

DOI Project Page [BibTex]

Mixture of Attractors: A Novel Movement Primitive Representation for Learning Motor Skills From Demonstrations

Manschitz, S., Gienger, M., Kober, J., Peters, J.
IEEE Robotics and Automation Letters, 3(2):926-933, April 2018 (article)

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DOI Project Page [BibTex]

ei Manschitz, S., Gienger, M., Kober, J., Peters, J. Mixture of Attractors: A Novel Movement Primitive Representation for Learning Motor Skills From Demonstrations IEEE Robotics and Automation Letters, 3(2):926-933, April 2018 (article)

DOI Project Page [BibTex]

Probabilistic movement primitives under unknown system dynamics

Paraschos, A., Rueckert, E., Peters, J., Neumann, G.
Advanced Robotics, 32(6):297-310, April 2018 (article)

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DOI Project Page [BibTex]

ei Paraschos, A., Rueckert, E., Peters, J., Neumann, G. Probabilistic movement primitives under unknown system dynamics Advanced Robotics, 32(6):297-310, April 2018 (article)

DOI Project Page [BibTex]

An Algorithmic Perspective on Imitation Learning

Osa, T., Pajarinen, J., Neumann, G., Bagnell, J., Abbeel, P., Peters, J.
Foundations and Trends in Robotics, 7(1-2):1-179, March 2018 (article)

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DOI Project Page [BibTex]

ei Osa, T., Pajarinen, J., Neumann, G., Bagnell, J., Abbeel, P., Peters, J. An Algorithmic Perspective on Imitation Learning Foundations and Trends in Robotics, 7(1-2):1-179, March 2018 (article)

DOI Project Page [BibTex]

Using Probabilistic Movement Primitives in Robotics

Paraschos, A., Daniel, C., Peters, J., Neumann, G.
Autonomous Robots, 42(3):529-551, March 2018 (article)

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DOI Project Page [BibTex]

ei Paraschos, A., Daniel, C., Peters, J., Neumann, G. Using Probabilistic Movement Primitives in Robotics Autonomous Robots, 42(3):529-551, March 2018 (article)

DOI Project Page [BibTex]

A kernel-based approach to learning contact distributions for robot manipulation tasks

Kroemer, O., Leischnig, S., Luettgen, S., Peters, J.
Autonomous Robots, 42(3):581-600, March 2018 (article)

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DOI Project Page [BibTex]

ei Kroemer, O., Leischnig, S., Luettgen, S., Peters, J. A kernel-based approach to learning contact distributions for robot manipulation tasks Autonomous Robots, 42(3):581-600, March 2018 (article)

DOI Project Page [BibTex]

Approximate Value Iteration Based on Numerical Quadrature

Vinogradska, J., Bischoff, B., Peters, J.
IEEE Robotics and Automation Letters, 3(2):1330-1337, January 2018 (article)

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DOI Project Page [BibTex]

ei Vinogradska, J., Bischoff, B., Peters, J. Approximate Value Iteration Based on Numerical Quadrature IEEE Robotics and Automation Letters, 3(2):1330-1337, January 2018 (article)

DOI Project Page [BibTex]

Biomimetic Tactile Sensors and Signal Processing with Spike Trains: A Review

Yi, Z., Zhang, Y., Peters, J.
Sensors and Actuators A: Physical, 269, pages: 41-52, January 2018 (article)

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DOI Project Page [BibTex]

ei Yi, Z., Zhang, Y., Peters, J. Biomimetic Tactile Sensors and Signal Processing with Spike Trains: A Review Sensors and Actuators A: Physical, 269, pages: 41-52, January 2018 (article)

DOI Project Page [BibTex]

Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks

Tanneberg, D., Peters, J., Rueckert, E.
Neural Networks, 109, pages: 67-80, 2018 (article)

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link (url) DOI Project Page [BibTex]

ei Tanneberg, D., Peters, J., Rueckert, E. Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks Neural Networks, 109, pages: 67-80, 2018 (article)

link (url) DOI Project Page [BibTex]

Numerical Quadrature for Probabilistic Policy Search

Vinogradska, J., Bischoff, B., Achterhold, J., Koller, T., Peters, J.
IEEE Transactions on Pattern Analysis and Machine Intelligence, pages: 1-1, 2018 (article)

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DOI [BibTex]

ev Vinogradska, J., Bischoff, B., Achterhold, J., Koller, T., Peters, J. Numerical Quadrature for Probabilistic Policy Search IEEE Transactions on Pattern Analysis and Machine Intelligence, pages: 1-1, 2018 (article)

DOI [BibTex]

Online optimal trajectory generation for robot table tennis

Koc, O., Maeda, G., Peters, J.
Robotics and Autonomous Systems, 105, pages: 121-137, 2018 (article)

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PDF link (url) DOI Project Page [BibTex]

ei Koc, O., Maeda, G., Peters, J. Online optimal trajectory generation for robot table tennis Robotics and Autonomous Systems, 105, pages: 121-137, 2018 (article)

PDF link (url) DOI Project Page [BibTex]

Hierarchical Reinforcement Learning of Multiple Grasping Strategies with Human Instructions

Osa, T., Peters, J., Neumann, G.
Advanced Robotics, 32(18):955-968, 2018 (article)

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DOI Project Page [BibTex]

ei Osa, T., Peters, J., Neumann, G. Hierarchical Reinforcement Learning of Multiple Grasping Strategies with Human Instructions Advanced Robotics, 32(18):955-968, 2018 (article)

DOI Project Page [BibTex]

Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling

Šošić, A., Rueckert, E., Peters, J., Zoubir, A., Koeppl, H.
Journal of Machine Learning Research, 19(69):1-45, 2018 (article)

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link (url) Project Page [BibTex]

ei Šošić, A., Rueckert, E., Peters, J., Zoubir, A., Koeppl, H. Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling Journal of Machine Learning Research, 19(69):1-45, 2018 (article)

link (url) Project Page [BibTex]

Grip Stabilization of Novel Objects using Slip Prediction

Veiga, F., Peters, J., Hermans, T.
IEEE Transactions on Haptics, 2018 (article) In press

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DOI Project Page [BibTex]

ei Veiga, F., Peters, J., Hermans, T. Grip Stabilization of Novel Objects using Slip Prediction IEEE Transactions on Haptics, 2018 (article) In press

DOI Project Page [BibTex]

f-Divergence constrained policy improvement

Belousov, B., Peters, J.
Journal of Machine Learning Research, 2018 (article) Submitted

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Project Page [BibTex]

ei Belousov, B., Peters, J. f-Divergence constrained policy improvement Journal of Machine Learning Research, 2018 (article) Submitted

Project Page [BibTex]

In-Hand Object Stabilization by Independent Finger Control

Veiga, F. F., Edin, B. B., Peters, J.
IEEE Transactions on Robotics, 2018 (article) Submitted

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Project Page [BibTex]

ei Veiga, F. F., Edin, B. B., Peters, J. In-Hand Object Stabilization by Independent Finger Control IEEE Transactions on Robotics, 2018 (article) Submitted

Project Page [BibTex]

Transfer Learning for BCIs

Jayaram, V., Fiebig, K., Peters, J., Grosse-Wentrup, M.
In Brain–Computer Interfaces Handbook, pages: 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (incollection)

ei

Project Page [BibTex]

ei Jayaram, V., Fiebig, K., Peters, J., Grosse-Wentrup, M. Transfer Learning for BCIs In Brain–Computer Interfaces Handbook, pages: 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (incollection)

Project Page [BibTex]

2017

Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals

Tanneberg, D., Peters, J., Rueckert, E.
Proceedings of the 1st Annual Conference on Robot Learning (CoRL), pages: 167-174, Proceedings of Machine Learning Research, (Editors: Sergey Levine, Vincent Vanhoucke and Ken Goldberg), PMLR, November 2017 (conference)

ei

link (url) Project Page [BibTex]

2017

ei Tanneberg, D., Peters, J., Rueckert, E. Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals Proceedings of the 1st Annual Conference on Robot Learning (CoRL), pages: 167-174, Proceedings of Machine Learning Research, (Editors: Sergey Levine, Vincent Vanhoucke and Ken Goldberg), PMLR, November 2017 (conference)

link (url) Project Page [BibTex]

Efficient Online Adaptation with Stochastic Recurrent Neural Networks

Tanneberg, D., Peters, J., Rueckert, E.
IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pages: 198-204, IEEE, November 2017 (conference)

ei

DOI Project Page [BibTex]

ei Tanneberg, D., Peters, J., Rueckert, E. Efficient Online Adaptation with Stochastic Recurrent Neural Networks IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pages: 198-204, IEEE, November 2017 (conference)

DOI Project Page [BibTex]

Learning inverse dynamics models in O(n) time with LSTM networks

Rueckert, E., Nakatenus, M., Tosatto, S., Peters, J.
IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pages: 811-816, IEEE, November 2017 (conference)

ei

DOI Project Page [BibTex]

ei Rueckert, E., Nakatenus, M., Tosatto, S., Peters, J. Learning inverse dynamics models in O(n) time with LSTM networks IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pages: 811-816, IEEE, November 2017 (conference)

DOI Project Page [BibTex]

A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries

Stark, S., Peters, J., Rueckert, E.
IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pages: 624-630, IEEE, November 2017 (conference)

ei

DOI Project Page [BibTex]

ei Stark, S., Peters, J., Rueckert, E. A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pages: 624-630, IEEE, November 2017 (conference)

DOI Project Page [BibTex]

Simulation of the underactuated Sake Robotics Gripper in V-REP

Thiem, S., Stark, S., Tanneberg, D., Peters, J., Rueckert, E.
Workshop at the International Conference on Humanoid Robots (HUMANOIDS), November 2017 (conference)

ei

link (url) [BibTex]

ei Thiem, S., Stark, S., Tanneberg, D., Peters, J., Rueckert, E. Simulation of the underactuated Sake Robotics Gripper in V-REP Workshop at the International Conference on Humanoid Robots (HUMANOIDS), November 2017 (conference)

link (url) [BibTex]

Active Incremental Learning of Robot Movement Primitives

Maeda, G., Ewerton, M., Osa, T., Busch, B., Peters, J.
Proceedings of the 1st Annual Conference on Robot Learning (CoRL), 78, pages: 37-46, Proceedings of Machine Learning Research, (Editors: Sergey Levine, Vincent Vanhoucke and Ken Goldberg), PMLR, November 2017 (conference)

ei

link (url) Project Page [BibTex]

ei Maeda, G., Ewerton, M., Osa, T., Busch, B., Peters, J. Active Incremental Learning of Robot Movement Primitives Proceedings of the 1st Annual Conference on Robot Learning (CoRL), 78, pages: 37-46, Proceedings of Machine Learning Research, (Editors: Sergey Levine, Vincent Vanhoucke and Ken Goldberg), PMLR, November 2017 (conference)

link (url) Project Page [BibTex]

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