| 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 | 57 | 2019 |
| Efficient gradient-free variational inference using policy search O Arenz, M Zhong, G Neumann International Conference on Machine Learning, 2018 | 31 | 2018 |
| 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 | 18 | 2019 |
| Assisted teleoperation in changing environments with a mixture of virtual guides M Ewerton, O Arenz, J Peters Advanced Robotics 34 (18), 1157-1170, 2020 | 14 | 2020 |
| Trust-region variational inference with gaussian mixture models O Arenz, M Zhong, G Neumann Journal of Machine Learning Research 21 (163), 1-60, 2020 | 14 | 2020 |
| Monte carlo chess O Arenz Technische Universität Darmstadt, 2012 | 13* | 2012 |
| 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 | 12 | 2023 |
| Integrating contrastive learning with dynamic models for reinforcement learning from images B You, O Arenz, Y Chen, J Peters Neurocomputing 476, 102-114, 2022 | 12 | 2022 |
| Expected information maximization: Using the i-projection for mixture density estimation P Becker, O Arenz, G Neumann arXiv preprint arXiv:2001.08682, 2020 | 12 | 2020 |
| Inverse reinforcement learning of bird flocking behavior R Pinsler, M Maag, O Arenz, G Neumann ICRA Swarms Workshop, 2018 | 9 | 2018 |
| Non-adversarial imitation learning and its connections to adversarial methods O Arenz, G Neumann arXiv preprint arXiv:2008.03525, 2020 | 8 | 2020 |
| 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 | 7 | 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 |
| 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 |
| 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 | 3 | 2023 |
| 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, 2023 | 1 | 2023 |
| 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 | 1 | 2022 |
| Least Squares Inverse Q-Learning F Al-Hafez, D Tateo, O Arenz, G Zhao, J Peters Sixteenth European Workshop on Reinforcement Learning, 2023 | | 2023 |
| A First-Order Method for Estimating Natural Gradients for Variational Inference with Gaussians and Gaussian Mixture Models O Arenz, Z Ye, P Dahlinger, G Neumann | | 2021 |
Gerhard NeumannProfessor, Karlsruhe Institute of Technology (KIT)Verified email at robot-learning.de
