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Journal papers

Willem Mathys de Graaf, Tom Cornelis Theodorus van Riet, Jan de Lange, and Jens Kober. A Multiclass Classification Model for Tooth Removal Procedures. Journal of Dental Research, 2022. [bibtex]

Rodrigo Pérez-Dattari, Bruno Brito, Oscar de Groot, Jens Kober, and Javier Alonso-Mora. Visually-Guided Motion Planning for Autonomous Driving from Interactive Demonstrations. Engineering Applications of Artificial Intelligence, 116:105277, 2022. [bibtex] [url] [doi] [code]

Padmaja Kulkarni, Jens Kober, Robert Babuška, and Cosimo Della Santina. Learning Assembly Tasks in a Few Minutes by Combining Impedance Control and Residual Recurrent Reinforcement Learning. Advanced Intelligent Systems, 4(1):2100095, 2022. [bibtex] [file] [doi] [video]

Jihong Zhu, Michael Gienger, and Jens Kober. Learning Task-Parameterized Skills from Few Demonstrations. IEEE Robotics and Automation Letters, 2022. The contents of this paper were also selected by ICRA'22 Program Committee for presentation at the Conference. [bibtex] [pdf] [url] [doi] [code] [video]

Jihong Zhu, Andrea Cherubini, Claire Dune, David Navarro-Alarcon, Farshid Alambeigi, Dmitry Berenson, Fanny Ficuciello, Kensuke Harada, Jens Kober, Xiang Li, Jia Pan, Wenzhen Yuan, and Michael Gienger. Challenges and Outlook in Robotic Manipulation of Deformable Objects. IEEE Robotics & Automation Magazine, :2-12, 2022. [bibtex] [pdf] [url] [doi]

Anna Mészáros, Giovanni Franzese, and Jens Kober. Learning to Pick at Non-Zero-Velocity From Interactive Demonstrations. IEEE Robotics and Automation Letters, 7(3):6052-6059, 2022. [bibtex] [pdf] [url] [doi] [video]

Tom C.T. van Riet, Kevin T.H. Chin Jen Sem, Jean-Pierre T.F. Ho, René Spijker, Jens Kober, and Jan de Lange. Robot Technology in Dentistry, Part Two of a Systematic Review: An Overview of Initiatives. Dental Materials, 37(8):1227–1236, 2021. [bibtex] [url] [doi]

Tom C.T. van Riet, Kevin T.H. Chin Jen Sem, Jean-Pierre T.F. Ho, René Spijker, Jens Kober, and Jan de Lange. Robot Technology in Dentistry, Part One of a Systematic Review: Literature Characteristics. Dental Materials, 37(8):1217–1226, 2021. [bibtex] [url] [doi]

Osama Mazhar, Robert Babuška, and Jens Kober. GEM: Glare or Gloom, I Can Still See You – End-to-End Multimodal Object Detector. IEEE Robotics and Automation Letters, 6(4):6321–6328, 2021. The contents of this paper were also selected by IROS'21 Program Committee for presentation at the Conference. [bibtex] [pdf] [doi]

X. Chen, X. Tan, G. Berselli, X. Chen, G. Clayton, S. Jeon, H. R. Karimi, S. Katsura, J. Kober, C.-C. Lan, A. Leonessa, Z. Li, G. Liu, D. Oetomo, K. Oldham, Y.-J. Pan, T. Shimono, T. Sun, M. Tavakoli, J. Ueda, H. Vallery, Q. Xu, J. Yi, L. Zhang, and L. Zuo. Guest Editorial: Focused Section on Inaugural Edition of TMECH/AIM Emerging Topics. IEEE/ASME Transactions on Mechatronics, 25(4):1695-1697, 2020. [bibtex] [file] [doi]

Simon Manschitz, Michael Gienger, Jens Kober, and Jan Peters. Learning Sequential Force Interaction Skills. Robotics, 9(2):45, 2020. [bibtex] [pdf] [doi]

Rodrigo Pérez-Dattari, Carlos E. Celemin, Giovanni Franzese, Javier Ruiz-del-Solar, and Jens Kober. Interactive Learning of Temporal Features for Control: Shaping Policies and State Representations From Human Feedback. IEEE Robotics & Automation Magazine, 27(2):46–54, 2020. [bibtex] [pdf] [doi] [code] [video]

Fabio Bonsignorio, David Hsu, Matthew Johnson-Roberson, and Jens Kober. Deep Learning and Machine Learning in Robotics [From the Guest Editors]. IEEE Robotics & Automation Magazine, 27(2):20–21, 2020. [bibtex] [url] [doi]

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] [video]

Carlos E. Celemin, Javier Ruiz-del-Solar, and Jens Kober. A Fast Hybrid Reinforcement Learning Framework with Human Corrective Feedback. Autonomous Robots, 43(5):1173–1186, 2019. [bibtex] [pdf] [doi] [video]

Carlos E. Celemin, Guilherme Maeda, Javier Ruiz-del-Solar, Jan Peters, and Jens Kober. Reinforcement Learning of Motor Skills using Policy Search and Human Corrective Advice. International Journal of Robotics Research, 38(14):1560–1580, 2019. [bibtex] [pdf] [doi] [video]

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]

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] [html] [code] [video]

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] [video]

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]

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] [video]

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

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]

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] [video]

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

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

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

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. Reinforcement Learning für Motor-Primitive. Künstliche Intelligenz, 9(3):38–40, 2009. [bibtex] [pdf] [html]

Conference and workshop papers

Nikolaos Passalis, Stefania Pedrazzi, Robert Babuška, Wolfram Burgard, Daniel Dias, Francesco Ferro, Moncef Gabbouj, Green Ole, Alexandros Iosifidis, Erdal Kayacan, Jens Kober, Olivier Michel, Nikolaos Nikolaidis, Paraskevi Nousi, Roel S. Pieters, Maria Tzelepi, Abhinav Valada, and Anastasios Tefas. OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022. [bibtex] [pdf]

Maaike Geertruida Beuling, Tom Cornelis Theodorus van Riet, Jan van Frankenhuyzen, Reinier van Antwerpen, Bas de Blocq van Scheltinga, Arnout Harm Hendrik Dourleijn, Dzan Ireiz, Sander Streefkerk, Jonathan C. van Zanten, Jan de Lange, Jens Kober, and Dimitra Dodou. Development and Testing of a Prototype of a Dental Extraction Trainer with Real-Time Feedback on Forces, Torques, and Angular Velocity. In 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022. [bibtex] [pdf]

Linda van der Spaa, Giovanni Franzese, Jens Kober, and Michael Gienger. Disagreement-Aware Variable Impedance Control for Online Learning of Physical Human-Robot Cooperation Tasks. In ICRA 2022 full day workshop - Shared Autonomy in Physical Human-Robot Interaction: Adaptability and Trust, 2022. [bibtex] [pdf] [code] [video]

Evelyn D'Elia, Jean-Baptiste Mouret, Jens Kober, and Serena Ivaldi. Automatic Tuning and Selection of Whole-Body Controllers. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022. [bibtex]

Antonin Raffin, Jens Kober, and Freek Stulp. Smooth Exploration for Robotic Reinforcement Learning. In 5th Conference on Robot Learning (CoRL) (Aleksandra Faust, David Hsu, Gerhard Neumann, eds.), PMLR, vol. 164 of Proceedings of Machine Learning Research, pp. 1634–1644, 2022. [bibtex] [pdf] [html] [video]

Snehal Jauhri, Carlos E. Celemin, and Jens Kober. Interactive Imitation Learning in State-Space. In 2020 Conference on Robot Learning (CoRL) (Jens Kober, Fabio Ramos, Claire Tomlin, eds.), PMLR, vol. 155 of Proceedings of Machine Learning Research, pp. 682–692, 2021. [bibtex] [pdf] [html] [code] [video]

Peter Valletta, Rodrigo Pérez-Dattari, and Jens Kober. Imitation Learning with Inconsistent Demonstrations through Uncertainty-based Data Manipulation. In IEEE International Conference on Robotics and Automation (ICRA), pp. 3655–3661, 2021. [bibtex] [pdf] [doi] [video]

Padmaja Kulkarni, Robert Babuška, and Jens Kober. Tactile-based Self-supervised Pose Estimation for Robust Grasping. In 17th International Symposium on Experimental Robotics (ISER) (Bruno Siciliano, Cecilia Laschi, Oussama Khatib, eds.), Springer International Publishing, Cham, pp. 277–284, 2021. [bibtex] [pdf] [doi]

Giovanni Franzese, Carlos E. Celemin, and Jens Kober. Learning Interactively to Resolve Ambiguity in Reference Frame Selection. In 2020 Conference on Robot Learning (CoRL) (Jens Kober, Fabio Ramos, Claire Tomlin, eds.), PMLR, vol. 155 of Proceedings of Machine Learning Research, pp. 1298–1311, 2021. [bibtex] [pdf] [html] [code] [video]

Giovanni Franzese, Anna Mészáros, Luka Peternel, and Jens Kober. ILoSA: Interactive Learning of Stiffness and Attractors. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7778–7785, 2021. [bibtex] [pdf] [url] [doi] [code] [video]

Evelyn D'Elia, Jean-Baptiste Mouret, Jens Kober, and Serena Ivaldi. Learning Task Controllers on a Humanoid Robot using Multi-objective Optimization. In ICRA 2021: 5th Full-Day Workshop on Legged Robots (Virtual), 2021. [bibtex] [url]

Carlos E. Celemin and Jens Kober. Uncertainties Based Queries for Interactive Policy Learning with Evaluations and Corrections. In Companion Publication of the 2021 International Conference on Multimodal Interaction, pp. 192–193, 2021. [bibtex] [pdf] [doi]

Bas van der Heijden, Laura Ferranti, Jens Kober, and Robert Babuška. DeepKoCo: Efficient Latent Planning with an Invariant Koopman Representation. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 183–189, 2021. [bibtex] [pdf] [url] [doi]

Bart Bootsma, Giovanni Franzese, and Jens Kober. Interactive Learning of Sensor Policy Fusion. In 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), pp. 665-670, 2021. [bibtex] [pdf] [doi]

Tom Cornelis Theodorus van Riet, Willem Mathys de Graaf, Reinier van Antwerpen, Jan van Frankenhuyzen, Jan de Lange, and Jens Kober. Robot Technology in Analyzing Tooth Removal – a Proof of Concept. In 42nd Annual International Conferences of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4721–4727, 2020. [bibtex] [pdf] [doi]

Tim de Bruin, Jens Kober, Karl Tuyls, and Robert Babuška. Fine-tuning Deep RL with Gradient-Free Optimization. In 21th IFAC World Congress, pp. 8049-8056, 2020. [bibtex] [pdf] [doi] [code]

Linda F. van der Spaa, Tamas Bates, Michael Gienger, and Jens Kober. Predicting and Optimizing Ergonomics in Physical Human-Robot Cooperation Tasks. In IEEE International Conference on Robotics and Automation (ICRA), pp. 1799–1805, 2020. [bibtex] [pdf] [doi] [video]

Rodrigo Pérez-Dattari, Carlos E. 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), pp. 7611–7617, 2019. [bibtex] [pdf] [doi] [code] [video]

Nikolaos Moustakis, Sebastiaan Paul Mulders, Jens Kober, and Jan-Willem van Wingerden. A Practical Bayesian Optimization Approach for the Optimal Estimation of the Rotor Effective Wind Speed. In American Control Conference (ACC), pp. 4179–4185, 2019. [bibtex] [pdf] [doi]

Jan Scholten, Daan Wout, Carlos E. Celemin, and Jens Kober. Deep Reinforcement Learning with Feedback-based Exploration. In IEEE Conference on Decision and Control (CDC), pp. 803–808, 2019. [bibtex] [pdf] [doi] [code]

Carlos E. Celemin and Jens Kober. Simultaneous Learning of Objective Function and Policy from Interactive Teaching with Corrective Feedback. In IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 726–732, 2019. [bibtex] [pdf] [doi] [video]

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]

Rodrigo Pérez-Dattari, Carlos E. 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) (Jing Xiao, Torsten Kröger, Oussama Khatib, eds.), Springer International Publishing, pp. 353–363, 2018. [bibtex] [pdf] [doi] [code] [video]

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] [video]

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] [html] [video]

Carlos E. 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, 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]

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] [video]

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]

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]

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]

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] [video]

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]

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] [video]

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] [video]

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]

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, 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]

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

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] [video]

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] [video]

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] [video]

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, 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]

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] [video]

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.), PMLR, vol. 15 of Proceedings of Machine Learning Research, Fort Lauderdale, FL, USA, pp. 182–189, 2011. [bibtex] [pdf] [url]

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] [video]

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] [video]

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] [code] [video]

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] [html] [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] [video]

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] [video]

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]

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

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] [code] [video]

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]

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]

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] [video]

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

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]

Preprints etc.

Mariano Ramirez Montero, Giovanni Franzese, Jeroen Zwanepol, and Jens Kober. Solving Robot Assembly Tasks by Combining Interactive Teaching and Self-Exploration. arXiv:2209.11530 [cs.RO], 2022. [bibtex] [pdf] [url] [code]

Antonin Raffin, Daniel Seidel, Jens Kober, Alin Albu-Schäffer, João Silvério, and Freek Stulp. Learning to Exploit Elastic Actuators for Quadruped Locomotion. arXiv:2209.07171 [cs.RO], 2022. [bibtex] [pdf] [url]

Tom Cornelis Theodorus van Riet, Willem Mathys de Graaf, Reinier van Antwerpen, Jan van Frankenhuyzen, Jan de Lange, and Jens Kober. Robot Technology in Analyzing Tooth Removal - A Proof of Concept. abstract: 25th Congress of the European Association for Cranio Maxillo Facial Surgery, 2021. [bibtex]

Osama Mazhar and Jens Kober. Random Shadows and Highlights: A New Data Augmentation Method for Extreme Lighting Conditions. arXiv:2101.05361 [cs.CV], 2021. [bibtex] [pdf] [url] [code]

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

Jens Kober and Jan Peters. Learning Prioritized Control of Motor Primitives. arXiv:1209.0488 [cs.RO], 2012. [bibtex] [pdf] [url]

Books, book chapters, and theses

Jens Kober. Encyclopedia of Systems and Control (John Baillieul, Tariq Samad, eds.), chapter Robot Learning, Springer London, pp. 1–9, 2019. [bibtex] [doi]

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, 2016. [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 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. Learning Motor Skills: From Algorithms to Robot Experiments. PhD thesis, Technische Universität Darmstadt, 2012. [bibtex] [pdf] [url]

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] [video]

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


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