- Al-Hafez, F.; Tateo, D.; Arenz, O.; Zhao, G.; Peters, J. (2023). LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning, International Conference on Learning Representations (ICLR).
- Ju, S.; van Vliet, P.; Arenz, O.; Peters, J. (2023). Digital Twin of a Driver-in-the-Loop Race Car Simulation with Contextual Reinforcement Learning, IEEE Robotics and Automation Letters (RA-L), 8, 7, pp.4107-4114.
- Arenz, O.; Dahlinger, P.; Ye, Z.; Volpp, M.; Neumann, G. (2023). A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models, Transactions on Machine Learning Research (TMLR).
- Al-Hafez, F.; Tateo, D.; Arenz, O.; Zhao, G.; Peters, J. (2023). Least Squares Inverse Q-Learning, European Workshop on Reinforcement Learning (EWRL).
- You, B.; Arenz, O.; Chen, Y.; Peters, J. (2022). Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from Images, Neurocomputing.
- Arenz, O.; Zhong, M.; Neumann G. (2020). Trust-Region Variational Inference with Gaussian Mixture Models, Journal of Machine Learning Research (JMLR).
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- Becker, P.; Arenz, O.; Neumann, G. (2020). Expected Information Maximization: Using the I-Projection for Mixture Density Estimation, International Conference on Learning Representations (ICLR).
- Pajarinen, J.; Arenz, O.; Peters, J.; Neumann, N. (2020). Probabilistic approach to physical object disentangling, IEEE Robotics and Automation Letters (RA-L).
- Laux, M.; Arenz, O.; Pajarinen, J.; Peters, J. (2020). Deep Adversarial Reinforcement Learning for Object Disentangling, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020).
- Abi Farraj, F.; Pacchierotti, C.; Arenz, O.; Neumann, G.; Giordano, P. (2019). A Haptic Shared-Control Architecture for Guided Multi-Target Robotic Grasping, IEEE Transactions on Haptics.
- Ewerton, M.; Arenz, O.; Maeda, G.; Koert, D.; Kolev, Z.; Takahashi, M.; Peters, J. (2019). Learning Trajectory Distributions for Assisted Teleoperation and Path Planning, Frontiers in Robotics and AI.
- Arenz, O.; Zhong, M.; Neumann, G. (2018). Efficient Gradient-Free Variational Inference using Policy Search, in: Dy, Jennifer and Krause, Andreas (eds.), Proceedings of the International Conference on Machine Learning (ICML), 80, pp.234--243, PMLR.
- Abdulsamad, H.; Arenz, O.; Peters, J.; Neumann, G. (2017). State-Regularized Policy Search for Linearized Dynamical Systems, Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS).
- Arenz, O.; Abdulsamad, H.; Neumann, G. (2016). Optimal Control and Inverse Optimal Control by Distribution Matching, Proceedings of the International Conference on Intelligent Robots and Systems (IROS), IEEE.
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