Links

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http://orcid.org/0000-0001-7257-5434
http://www.researcherid.com/rid/I-9119-2017
ResearchGate

Journal papers

Yudha Prawira Pane, Subramanya Prasad Nageshrao, Jens Kober, and Robert Babuška. Reinforcement Learning Based Compensation Methods for Robot Manipulators. Engineering Applications of Artificial Intelligence, 78:236–247, 2019. [bibtex] [pdf] [doi]

Carlos E. Celemin, Javier Ruiz-del-Solar, and Jens Kober. A Fast Hybrid Reinforcement Learning Framework with Human Corrective Feedback. Autonomous Robots, First Online, 2018. [bibtex] [pdf] [doi]

Lucian Buşoniu, Tim de Bruin, Domagoj Tolić, Jens Kober, and Ivana Palunko. Reinforcement Learning for Control: Performance, Stability, and Deep Approximators. Annual Reviews in Control, 46:8–28, 2018. [bibtex] [pdf] [doi]

Simon Manschitz, Michael Gienger, Jens Kober, and Jan Peters. Mixture of Attractors: A Novel Movement Primitive Representation for Learning Motor Skills From Demonstrations. IEEE Robotics and Automation Letters, 3(2):926–933, 2018. [bibtex] [pdf] [doi]

Tim de Bruin, Jens Kober, Karl Tuyls, and Robert Babuška. Experience Selection in Deep Reinforcement Learning for Control. Journal of Machine Learning Research, 19(9):1–56, 2018. [bibtex] [pdf] [url]

Tim de Bruin, Jens Kober, Karl Tuyls, and Robert Babuška. Integrating State Representation Learning into Deep Reinforcement Learning. IEEE Robotics and Automation Letters, 3(3):1394–1401, 2018. The contents of this paper were also selected by ICRA'18 Program Committee for presentation at the Conference. [bibtex] [pdf] [doi]

Tom Cornelis Theodorus van Riet, Jens Kober, and Jan de Lange. Robottechnologie, is er een toekomst voor in de tandheelkunde? Quality Practice Tandheelkunde 12(5):30–35, 2017. [bibtex] [url]

Simon Manschitz, Jens Kober, Michael Gienger, and Jan Peters. Learning Movement Primitive Attractor Goals and Sequential Skills from Kinesthetic Demonstrations. Robotics and Autonomous Systems, 74(Part A):97–107, 2015. [bibtex] [pdf] [doi]

Jens Kober. Learning Motor Skills: From Algorithms to Robot Experiments. it - Information Technology, 56(3):141–146, 2014. [bibtex] [doi]

Jens Kober, J. Andrew Bagnell, and Jan Peters. Reinforcement Learning in Robotics: A Survey. International Journal of Robotics Research, 32(11):1238–1274, 2013. [bibtex] [pdf] [doi]

Katharina Muelling, Jens Kober, Oliver Kroemer, and Jan Peters. Learning to Select and Generalize Striking Movements in Robot Table Tennis. International Journal of Robotics Research, 32(3):263–279, 2013. [bibtex] [pdf] [doi]

Jens Kober, Andreas Wilhelm, Erhan Oztop, and Jan Peters. Reinforcement Learning to Adjust Parametrized Motor Primitives to New Situations. Autonomous Robots, 33(4):361–379, 2012. [bibtex] [pdf] [doi]

Jens Kober and Jan Peters. Policy Search for Motor Primitives in Robotics. Machine Learning, 84(1-2):171–203, 2011. [bibtex] [pdf] [doi]

Katharina Muelling, Jens Kober, and Jan Peters. A Biomimetic Approach to Robot Table Tennis. Adaptive Behavior, 19(5):359–376, 2011. [bibtex] [pdf] [doi]

Jan Peters, Jens Kober, and Stefan Schaal. Policy Learning Algorithmis for Motor Learning (Algorithmen zum automatischen Erlernen von Motorfähigkigkeiten). at - Automatisierungstechnik, 58(12):688–694, 2010. [bibtex] [pdf] [doi]

Jens Kober and Jan Peters. Imitation and Reinforcement Learning - Practical Algorithms for Motor Primitive Learning in Robotics. IEEE Robotics and Automation Magazine, 17(2):55–62, 2010. [bibtex] [pdf] [doi]

Jens Kober and Jan Peters. Reinforcement Learning für Motor-Primitive. Künstliche Intelligenz, 9(3):38–40, 2009. [bibtex] [pdf] [url]

Conference and workshop papers

Nikolaos Moustakis, Jens Kober, and Jan-Willem van Wingerden. A Practical Bayesian Optimization Approach for the Optimal Estimation of the Rotor Effective Wind Speed Speed. In American Control Conference (ACC), 2019. [bibtex]

Rodrigo Pérez Dattari, Carlos Celemin, Javier Ruiz Del Solar, and Jens Kober. Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach. In IEEE International Conference on Robotics and Automation (ICRA), 2019. [bibtex] [pdf]

Divyam Rastogi, Ivan Koryakovskiy, and Jens Kober. Sample-efficient Reinforcement Learning via Difference Models. In Third Machine Learning in Planning and Control of Robot Motion Workshop at IEEE International Conference on Robotics and Automation (ICRA), 2018. [bibtex] [pdf] [url]

Michael Gienger, Dirk Ruiken, Tamas Bates, Mohamed Regaieg, Michael Meißner, Jens Kober, Philipp Seiwald, and Arne-Christoph Hildebrandt. Human-Robot Cooperative Object Manipulation with Contact Changes. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1354–1360, 2018. [bibtex] [pdf] [doi]

Rodrigo Pérez Dattari, Carlos Celemin, Javier Ruiz Del Solar, and Jens Kober. Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks. In International Symposium on Experimental Robotics (ISER), 2018. [bibtex] [pdf]

Tamas Bates, Jens Kober, and Michael Gienger. Head-tracked off-axis perspective projection improves gaze readability of 3D virtual avatars. In SIGGRAPH Asia Technical Briefs, pp. 29:1–29:4, 2018. [bibtex] [pdf] [doi]

Carlos Celemin, Guilherme Maeda, Jens Kober, and Javier Ruiz-del-Solar. Human Corrective Advice in the Policy Search Loop. In Workshop Human-in-the-loop Robotic Manipulation: On the Influence of the Human Role, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017. [bibtex] [pdf]

Tom Cornelis Theodorus van Riet, Jens Kober, Xiang Zhang, Maarten Griffioen, Piet-Hein van Twisk, Jan de Lange, and Robert Babuška. Robot-assistance in Understanding and Education of Tooth Removal: Setup and Preliminary Results. In 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017. [bibtex]

Denise S. Feirstein, Ivan Koryakovskiy, Jens Kober, and Heike Vallery. Reinforcement Learning of Potential Fields to achieve Limit-Cycle Walking. In IFAC International Workshop on Periodic Control Systems (PSYCO), pp. 113–118, 2016. IFAC-PapersOnLine. [bibtex] [pdf] [doi]

Jelle Munk, Jens Kober, and Robert Babuška. Learning State Representation for Deep Actor-Critic Control. In IEEE Conference on Decision and Control (CDC), pp. 4667–4673, 2016. [bibtex] [pdf] [doi]

Simon Manschitz, Michael Gienger, Jens Kober, and Jan Peters. Probabilistic Decomposition of Sequential Force Interaction Tasks into Movement Primitives. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3920–3927, 2016. [bibtex] [pdf] [doi]

Tim de Bruin, Jens Kober, Karl Tuyls, and Robert Babuška. Off Policy Experience Retention for Deep Actor Critic Learning. In Deep Reinforcement Learning Workshop, Advances in Neural Information Processing Systems (NIPS), 2016. [bibtex] [pdf]

Tim de Bruin, Jens Kober, Karl Tuyls, and Robert Babuška. Improved Deep Reinforcement Learning for Robotics Through Distribution-based Experience Retention. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3947–3952, 2016. [bibtex] [pdf] [doi]

Tom Cornelis Theodorus van Riet, Jens Kober, Maarten Griffioen, Xiang Zhang, Piet-Hein van Twisk, Robert Babuška, and Jan de Lange. Op zoek naar toepassingen van robottechnologie in de MKA-chirurgie. In Annual Scientific Meeting of the Dutch Association of Oral and Maxillofacial Surgery, 2016. [bibtex]

Jens Kober, Michael Gienger, and Jochen J. Steil. Learning Movement Primitives for Force Interaction Tasks. In IEEE International Conference on Robotics and Automation (ICRA), pp. 3192–3199, 2015. [bibtex] [pdf] [doi]

Simon Manschitz, Jens Kober, Michael Gienger, and Jan Peters. Probabilistic Progress Prediction and Sequencing of Concurrent Movement Primitives. In IEEE/RSJ International Conference on Robot Systems (IROS), pp. 449–455, 2015. [bibtex] [pdf] [doi]

Tim de Bruin, Jens Kober, Karl Tuyls, and Robert Babuška. The Importance of Experience Replay Database Composition in Deep Reinforcement Learning. In Deep Reinforcement Learning Workshop, Advances in Neural Information Processing Systems (NIPS), 2015. [bibtex] [pdf]

Simon Manschitz, Jens Kober, Michael Gienger, and Jan Peters. Learning to Unscrew a Light Bulb from Demonstrations. In 41st International Symposium on Robotics (ISR/ROBOTIK), pp. 264–270, 2014. [bibtex] [pdf] [url]

Simon Manschitz, Jens Kober, Michael Gienger, and Jan Peters. Learning to Sequence Movement Primitives from Demonstrations. In IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), pp. 4414–4421, 2014. [bibtex] [pdf] [doi]

Jan Peters, Jens Kober, Katharina Muelling, Duy Nguyen-Tuong, and Oliver Kroemer. Learning Skills with Motor Primitives. In 16th Yale Learning Workshop, 2013. [bibtex]

Jan Peters, Jens Kober, Katharina Muelling, Oliver Kroemer, and Gerhard Neumann. Towards Robot Skill Learning: From Simple Skills to Table Tennis. In European Conference on Machine Learning (ECML), Nectar Track, pp. 627–631, 2013. [bibtex] [pdf] [doi]

Jens Kober. Lernen Motorischer Fähigkeiten: Von Algorithmen zu Roboter-Experimenten. In Ausgezeichnete Informatikdissertationen 2012, Gesellschaft für Informatik e.V. (GI), pp. 181–190, 2013. [bibtex]

Jan Peters, Jens Kober, Katharina Muelling, Duy Nguyen-Tuong, and Oliver Kroemer. Robot Skill Learning. In European Conference on Artificial Intelligence (ECAI), pp. 40–45, 2012. [bibtex] [pdf] [doi]

Jens Kober, Katharina Muelling, and Jan Peters. Learning Throwing and Catching Skills. In IEEE/RSJ International Conference on Robot Systems (IROS), Video Track, pp. 5167–5168, 2012. [bibtex] [pdf] [doi]

Jens Kober, Matthew Glisson, and Michael Mistry. Playing Catch and Juggling with a Humanoid Robot. In IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS), pp. 875–881, 2012. [bibtex] [pdf] [doi]

Katharina Muelling, Jens Kober, Oliver Kroemer, and Jan Peters. Learning to Select and Generalize Striking Movements in Robot Table Tennis. In AAAI Fall Symposium on Robots that Learn Interactively from Human Teachers, pp. 263–279, 2012. [bibtex] [pdf] [url]

Abdeslam Boularias, Jens Kober, and Jan Peters. Relative Entropy Inverse Reinforcement Learning. In 14th International Conference on Artificial Intelligence and Statistics (AISTATS) (Geoffrey Gordon, David Dunson, Miroslav Dudík, eds.), vol. 15 of Proceedings of Machine Learning Research, pp. 182–189, 2011. [bibtex] [pdf] [url]

Jens Kober and Jan Peters. Learning Elementary Movements Jointly with a Higher Level Task. In IEEE/RSJ International Conference on Intelligent Robot Systems (IROS), pp. 338–343, 2011. [bibtex] [pdf] [doi]

Jens Kober, Erhan Oztop, and Jan Peters. Reinforcement Learning to adjust Robot Movements to New Situations. In International Joint Conference on Artificial Intelligence (IJCAI), Best Paper Track, pp. 2650–2655, 2011. [bibtex] [pdf] [doi]

Jan Peters, Katharina Muelling, and Jens Kober. Experiments with Motor Primitives to learn Table Tennis. In 12th International Symposium on Experimental Robotics (ISER) (Oussama Khatib, Vijay Kumar, Gaurav Sukhatme, eds.), pp. 347–359, 2010. [bibtex] [url] [doi]

Jens Kober, Erhan Oztop, and Jan Peters. Reinforcement Learning to adjust Robot Movements to New Situations. In Robotics: Science and Systems (R:SS), 2010. [bibtex] [pdf] [url] [doi]

Jens Kober, Katharina Muelling, Oliver Kroemer, Christoph H. Lampert, Bernhard Schölkopf, and Jan Peters. Movement Templates for Learning of Hitting and Batting. In IEEE International Conference on Robotics and Automation (ICRA), pp. 69–82, 2010. [bibtex] [pdf] [doi]

Katharina Muelling, Jens Kober, and Jan Peters. Learning Table Tennis with a Mixture of Motor Primitives. In 10th IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS), pp. 411–416, 2010. [bibtex] [pdf] [doi]

Katharina Muelling, Jens Kober, and Jan Peters. Simulating Human Table Tennis with a Biomimetic Robot Setup. In From Animals to Animats 11, International Conference on the Simulation of Adaptive Behavior (SAB), pp. 273–282, 2010. [bibtex] [pdf] [doi]

Katharina Muelling, Jens Kober, and Jan Peters. A Biomimetic Approach to Robot Table Tennis. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1921–1926, 2010. [bibtex] [pdf] [doi]

Jan Peters and Jens Kober. Using Reward-Weighted Imitation for Robot Reinforcement Learning. In IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning (ADPRL), pp. 226–232, 2009. [bibtex] [pdf] [doi]

Jan Peters, Jens Kober, Katharina Muelling, Duy Nguyen-Tuong, and Oliver Kroemer. Towards Motor Skill Learning for Robotics. In International Symposium on Robotics Research (ISRR), Invited Paper, pp. 469–482, 2009. [bibtex] [pdf] [doi]

Jens Kober and Jan Peters. Learning Motor Primitives for Robotics. In IEEE International Conference on Robotics and Automation (ICRA), pp. 2112–2118, 2009. [bibtex] [pdf] [doi]

Jens Kober and Jan Peters. Learning new basic Movements for Robotics. In Autonome Mobile Systeme (AMS) (Rüdiger Dillmann, Jürgen Beyerer, Christoph Stiller, J. Marius Zöllner, Tobias Gindele, eds.), Springer Berlin Heidelberg, pp. 105–112, 2009. [bibtex] [pdf] [doi]

Jens Kober and Jan Peters. Policy Search for Motor Primitives in Robotics. In Advances in Neural Information Processing Systems 21 (NIPS 2008) (D. Koller, D. Schuurmans, Y. Bengio, L. Bottou, eds.), Curran Associates, Inc., pp. 849–856, 2009. [bibtex] [pdf] [url]

Manuel Gomez-Rodriguez, Jens Kober, and Bernhard Schölkopf. Denoising Photographs Using Dark Frames Optimized by Quadratic Programming. In 1st IEEE International Conference on Computational Photography (ICCP), pp. 1–9, 2009. [bibtex] [pdf] [doi]

Silvia Chiappa, Jens Kober, and Jan Peters. Using Bayesian Dynamical Systems for Motion Template Libraries. In Advances in Neural Information Processing Systems 21 (NIPS 2008) (D. Koller, D. Schuurmans, Y. Bengio, L. Bottou, eds.), Curran Associates, Inc., pp. 297–304, 2009. [bibtex] [pdf] [url]

Jan Peters, Jens Kober, and Duy Nguyen-Tuong. Policy Learning - A Unified Perspective with Applications in Robotics. In European Workshop on Reinforcement Learning (EWRL), pp. 220–228, 2008. [bibtex] [pdf] [doi]

Jens Kober and Jan Peters. Reinforcement Learning of Perceptual Coupling for Motor Primitives. In European Workshop on Reinforcement Learning (EWRL), 2008. [bibtex]

Jens Kober, Betty Mohler, and Jan Peters. Learning Perceptual Coupling for Motor Primitives. In IEEE/RSJ International Conference on Intelligent Robot Systems (IROS), pp. 834–839, 2008. [bibtex] [pdf] [doi]

Preprints

Daan Wout, Jan Scholten, Carlos Celemin, and Jens Kober. Learning Gaussian Policies from Corrective Human Feedback. arXiv:1903.05216 [cs.LG], 2019. [bibtex] [pdf] [url]

Jan Scholten, Daan Wout, Carlos Celemin, and Jens Kober. Deep Reinforcement Learning with Feedback-based Exploration. arXiv:1903.06151 [cs.LG], 2019. [bibtex] [pdf] [url]

Books, book chapters and theses

Jan Peters, Daniel D. Lee, Jens Kober, Duy Nguyen-Tuong, Drew Bagnell, and Stefan Schaal. Springer Handbook of Robotics, 2nd Edition (Bruno Siciliano, Oussama Khatib, eds.), chapter Robot Learning, Springer International Publishing, pp. 357–394, 2017. [bibtex] [url] [doi]

Jens Kober and Jan Peters. Learning Motor Skills - From Algorithms to Robot Experiments. Springer, vol. 97 of Springer Tracts in Advanced Robotics (STAR Series), 2014. [bibtex] [url] [doi]

Jens Kober. Learning Motor Skills: From Algorithms to Robot Experiments. PhD thesis, Technische Universität Darmstadt, 2012. [bibtex] [pdf] [url]

Jens Kober and Jan Peters. Reinforcement Learning - State-of-the-Art (Marco Wiering, Martijn van Otterlo, eds.), chapter Reinforcement Learning in Robotics: A Survey, Springer, vol. 12 of Adaptation, Learning, and Optimization, pp. 579–610, 2012. [bibtex] [url] [doi]

Jens Kober, Betty Mohler, and Jan Peters. From Motor Learning to Interaction Learning in Robots (Olivier Sigaud, Jan Peters, eds.), chapter Imitation and Reinforcement Learning for Motor Primitives with Perceptual Coupling, Springer Verlag, vol. 264 of Studies in Computational Intelligence, pp. 209–225, 2010. [bibtex] [pdf] [doi]

Jens Kober. Reinforcement Learning for Motor Primitives. Master's thesis, University of Stuttgart, 2008. [bibtex] [pdf]


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