| A haptic shared-control architecture for guided multi-target robotic grasping F Abi-Farraj, C Pacchierotti, O Arenz, G Neumann, PR Giordano IEEE transactions on haptics 13 (2), 270-285, 2019 | 71 | 2019 |
| Efficient gradient-free variational inference using policy search O Arenz, M Zhong, G Neumann International Conference on Machine Learning, 2018 | 43 | 2018 |
| Ls-iq: Implicit reward regularization for inverse reinforcement learning F Al-Hafez, D Tateo, O Arenz, G Zhao, J Peters arXiv preprint arXiv:2303.00599, 2023 | 29 | 2023 |
| Assisted teleoperation in changing environments with a mixture of virtual guides M Ewerton, O Arenz, J Peters Advanced Robotics 34 (18), 1157-1170, 2020 | 21 | 2020 |
| Learning trajectory distributions for assisted teleoperation and path planning M Ewerton, O Arenz, G Maeda, D Koert, Z Kolev, M Takahashi, J Peters Frontiers in Robotics and AI 6, 89, 2019 | 21 | 2019 |
| Trust-region variational inference with gaussian mixture models O Arenz, M Zhong, G Neumann Journal of Machine Learning Research 21 (163), 1-60, 2020 | 19 | 2020 |
| Integrating contrastive learning with dynamic models for reinforcement learning from images B You, O Arenz, Y Chen, J Peters Neurocomputing 476, 102-114, 2022 | 17 | 2022 |
| Inverse reinforcement learning of bird flocking behavior R Pinsler, M Maag, O Arenz, G Neumann ICRA swarms workshop, 2018 | 17 | 2018 |
| A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models O Arenz, P Dahlinger, Z Ye, M Volpp, G Neumann Transactions on Machine Learning Research (TMLR), 2835-8856, 2023 | 16 | 2023 |
| Expected information maximization: Using the i-projection for mixture density estimation P Becker, O Arenz, G Neumann arXiv preprint arXiv:2001.08682, 2020 | 14 | 2020 |
| Monte carlo chess O Arenz Technische Universität Darmstadt, 2012 | 14* | 2012 |
| Non-adversarial imitation learning and its connections to adversarial methods O Arenz, G Neumann arXiv preprint arXiv:2008.03525, 2020 | 12 | 2020 |
| Digital twin of a driver-in-the-loop race car simulation with contextual reinforcement learning S Ju, P van Vliet, O Arenz, J Peters IEEE Robotics and Automation Letters 8 (7), 4107-4114, 2023 | 8 | 2023 |
| Optimal Control and Inverse Optimal Control by Distribution Matching O Arenz, H Abdulsamad, G Neumann 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016 | 8 | 2016 |
| State-regularized policy search for linearized dynamical systems H Abdulsamad, O Arenz, J Peters, G Neumann Proceedings of the International Conference on Automated Planning and …, 2017 | 5 | 2017 |
| Machine learning with physics knowledge for prediction: A survey J Watson, C Song, O Weeger, T Gruner, AT Le, K Pompetzki, A Hendawy, ... arXiv preprint arXiv:2408.09840, 2024 | 4 | 2024 |
| Probabilistic approach to physical object disentangling J Pajarinen, O Arenz, J Peters, G Neumann IEEE Robotics and Automation Letters 5 (4), 5510-5517, 2020 | 4 | 2020 |
| Deep Adversarial Reinforcement Learning for Object Disentangling M Laux, O Arenz, J Peters, J Pajarinen IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 | 4 | 2020 |
| Self-supervised sequential information bottleneck for robust exploration in deep reinforcement learning B You, J Xie, Y Chen, J Peters, O Arenz arXiv preprint arXiv:2209.05333, 2022 | 3 | 2022 |
| Maximum Total Correlation Reinforcement Learning B You, P Liu, H Liu, J Peters, O Arenz arXiv preprint arXiv:2505.16734, 2025 | | 2025 |
Jan PetersProfessor for Intelligent Autonomous Systems/TU Darmstadt, Dept. Head/German AI Research Center DFKIVerified email at ias.tu-darmstadt.de