Journal Papers

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

Lutter, M.; Belousov, B.; Mannor, S.; Fox, D.; Garg, A.; Peters, J. (submitted). Continuous-Time Fitted Value Iteration for Robust Policies, Submitted to the IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI).   Download Article [PDF]   BibTeX Reference [BibTex]

Lutter, M.; Silberbauer, J.; Watson, J.; Peters, J. (Submitted). A Differentiable Newton-Euler Algorithm for Real-World Robotics, Submitted to the IEEE Transaction of Robotics (T-RO).   BibTeX Reference [BibTex]

Koert, D.; Trick, S.; Ewerton, M.; Lutter, M.; Peters, J. (2020). Incremental Learning of an Open-Ended Collaborative Skill Library, International Journal of Humanoid Robotics (IJHR), 17, 1.   BibTeX Reference [BibTex]

Conference and Workshop Papers

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]

Lutter, M.; Silberbauer, J.; Watson, J.; Peters, J. (2021). Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).   Download Article [PDF]   BibTeX Reference [BibTex]

Lutter, M.; Mannor, S.; Peters, J.; Fox, D.; Garg, A. (2021). Value Iteration in Continuous Actions, States and Time, International Conference on Machine Learning (ICML).   Download Article [PDF]   BibTeX Reference [BibTex]

Lutter, M.; Mannor, S.; Peters, J.; Fox, D.; Garg, A. (2021). Robust Value Iteration for Continuous Control Tasks, Robotics: Science and Systems (RSS).   Download Article [PDF]   BibTeX Reference [BibTex]

Lutter, M.; Clever, D.; Kirsten, R.; Listmann, K.; Peters, J. (2021). Building Skill Learning Systems for Robotics, Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE).   BibTeX Reference [BibTex]

Ploeger, K.; Lutter, M.; Peters, J. (2020). High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards, Conference on Robot Learning (CoRL).   Download Article [PDF]   BibTeX Reference [BibTex]

Lutter, M.; Clever, D.; Belousov, B.; Listmann, K.; Peters, J. (2020). Evaluating the Robustness of HJB Optimal Feedback Control, International Symposium on Robotics.   BibTeX Reference [BibTex]

Lutter, M.; Ritter, C.; Peters, J. (2019). Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning, International Conference on Learning Representations (ICLR).   Download Article [PDF]   BibTeX Reference [BibTex]

Lutter, M.; Peters, J. (2019). Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems, International Conference on Intelligent Robots and Systems (IROS).   Download Article [PDF]   BibTeX Reference [BibTex]

Lutter, M.; Peters, J. (2019). Deep Optimal Control: Using the Euler-Lagrange Equation to learn an Optimal Feedback Control Law, Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM).   Download Article [PDF]   BibTeX Reference [BibTex]

Lutter, M.; Belousov, B.; Listmann, K.; Clever, D.; Peters, J. (2019). HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints, Conference on Robot Learning (CoRL).   Download Article [PDF]   BibTeX Reference [BibTex]

Koert, D.; Trick, S.; Ewerton, M.; Lutter, M.; Peters, J. (2018). Online Learning of an Open-Ended Skill Library for Collaborative Tasks, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).   Download Article [PDF]   BibTeX Reference [BibTex]

Books, Book Chapters & Theses

  

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