Jan Peters

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Gerhard NeumannProfessor, Karlsruhe Institute of Technology (KIT)Verified email at robot-learning.de
Jens KoberAssociate Professor, CoR, TU DelftVerified email at tudelft.nl
Stefan SchaalRobotics, Machine Learning, AI, NeuroscienceVerified email at google.com
Oliver KroemerCarnegie Mellon University - The Robotics InstituteVerified email at andrew.cmu.edu
Marc DeisenrothUniversity College LondonVerified email at ucl.ac.uk
Duy Nguyen-TuongBosch Center for AI, GermanyVerified email at robot-learning.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
Katharina MuellingCarnegie Mellon University, NRECVerified email at nrec.ri.cmu.edu
Guilherme Jorge MaedaPreferred Networks Inc.Verified email at preferred.jp
Marco EwertonPostdoctoral researcher at Idiap Research Institute, SwitzerlandVerified email at idiap.ch
Rudolf LioutikovAssitant Professor of Practice, University of Texas at AustinVerified email at utexas.edu
Rueckert ElmarAssistant Professor, ROB, Universität zu LübeckVerified email at rob.uni-luebeck.de
Roberto CalandraFacebook AI ResearchVerified email at fb.com
Christian DanielBosch Center for Artificial IntelligenceVerified email at ias.tu-darmstadt.de
Herke van HoofUniversity of Amsterdam (UVA)Verified email at uva.nl
Joni PajarinenAssistant Professor at Aalto University and Research Group Leader at IAS at TU DarmstadtVerified email at aalto.fi
Alexandros ParaschosVolkswagen AGVerified email at argmax.ai
Jun NakanishiMeijo University, JapanVerified email at meijo-u.ac.jp
Michael MistryProfessor of Robotics, University of EdinburghVerified email at ed.ac.uk
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinVerified email at amazon.de

Jan Peters

Professor for Intelligent Autonomous Systems at Technische Universität Darmstadt

Verified email at ias.tu-darmstadt.de - Homepage

Robot Learning Reinforcement Learning Machine Learning Robotics Biomimetic Systems


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Reinforcement learning in robotics: A survey J Kober, JA Bagnell, J Peters The International Journal of Robotics Research 32 (11), 1238-1274, 2013	2045	2013
Reinforcement learning of motor skills with policy gradients J Peters, S Schaal Neural networks 21 (4), 682-697, 2008	905	2008
A survey on policy search for robotics MP Deisenroth, G Neumann, J Peters Foundations and trends in Robotics 2 (1-2), 388-403, 2013	826	2013
Natural actor-critic J Peters, S Schaal Neurocomputing 71 (7-9), 1180-1190, 2008	807	2008
Policy search for motor primitives in robotics J Kober, J Peters Machine Learning 84 (1), 171–203, 2011	704	2011
Policy gradient methods for robotics J Peters, S Schaal Proceedings of the IEEE/RSJ International Conference on Intelligent Robots …, 2006	568	2006
Relative entropy policy search J Peters, K Mülling, Y Altun Proceedings of the Twenty-fourth National Conference on Artificial …, 2010	530	2010
Reinforcement learning for humanoid robotics J Peters, S Vijayakumar, S Schaal Proceedings of the 3rd IEEE-RAS International Conference on Humanoid Robots …, 2003	443	2003
Model learning for robot control: a survey D Nguyen-Tuong, J Peters Cognitive processing 12 (4), 319-340, 2011	429	2011
Learning movement primitives S Schaal, J Peters, J Nakanishi, A Ijspeert Proceedings of the International Symposium on Robotics Research (ISRR), 561-572, 2005	406	2005
Learning to select and generalize striking movements in robot table tennis K Mülling, J Kober, O Kroemer, J Peters The International Journal of Robotics Research 32 (3), 263-279, 2013	365	2013
Operational space control: A theoretical and empirical comparison J Nakanishi, R Cory, M Mistry, J Peters, S Schaal The International Journal of Robotics Research 27 (6), 737-757, 2008	344	2008
Probabilistic movement primitives A Paraschos, C Daniel, J Peters, G Neumann Advances in neural information processing systems, 2013	339	2013
Natural evolution strategies D Wierstra, T Schaul, J Peters, J Schmidhuber 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on …, 2008	300	2008
Natural evolution strategies D Wierstra, T Schaul, T Glasmachers, Y Sun, J Peters, J Schmidhuber The Journal of Machine Learning Research 15 (1), 949-980, 2014	288	2014
Model learning with local gaussian process regression D Nguyen-Tuong, M Seeger, J Peters Advanced Robotics 23 (15), 2015-2034, 2009	279	2009
Relative entropy inverse reinforcement learning A Boularias, J Kober, J Peters 14th International Conference on Artificial Intelligence and Statistics …, 2011	277	2011
Natural actor-critic J Peters, S Vijayakumar, S Schaal European Conference on Machine Learning, 280-291, 2005	272	2005
Gaussian process dynamic programming MP Deisenroth, CE Rasmussen, J Peters Neurocomputing 72 (7-9), 1508-1524, 2009	244	2009
Parameter-exploring policy gradients F Sehnke, C Osendorfer, T Rückstieß, A Graves, J Peters, J Schmidhuber Neural Networks 23 (4), 551-559, 2010	243	2010

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