Optimal control and inverse optimal control by distribution matching. - Oleg Arenz; Hany Abdulsamad; Gerhard Neumann (IROS, 2016)

State-Regularized Policy Search for Linearized Dynamical Systems. - Hany Abdulsamad; Oleg Arenz; Jan Peters 001; Gerhard Neumann (ICAPS, 2017)

Efficient Gradient-Free Variational Inference using Policy Search. - Oleg Arenz; Mingjun Zhong; Gerhard Neumann (ICML, 2018)

Learning Trajectory Distributions for Assisted Teleoperation and Path Planning. - Marco Ewerton; Oleg Arenz; Guilherme Maeda; Dorothea Koert; Zlatko Kolev; Masaki Takahashi; Jan Peters 001 (Frontiers Robotics AI, 2019)

Expected Information Maximization: Using the I-Projection for Mixture Density Estimation. - Philipp Becker; Oleg Arenz; Gerhard Neumann (ICLR, 2020)

Deep Adversarial Reinforcement Learning for Object Disentangling. - Melvin Laux; Oleg Arenz; Jan Peters 001; Joni Pajarinen (IROS, 2020)

Assisted teleoperation in changing environments with a mixture of virtual guides. - Marco Ewerton; Oleg Arenz; Jan Peters 001 (Adv. Robotics, 2020)

Non-Adversarial Imitation Learning and its Connections to Adversarial Methods. - Oleg Arenz; Gerhard Neumann (CoRR, 2020)

Trust-Region Variational Inference with Gaussian Mixture Models. - Oleg Arenz; Mingjun Zhong; Gerhard Neumann (J. Mach. Learn. Res., 2020)

Probabilistic Approach to Physical Object Disentangling. - Joni Pajarinen; Oleg Arenz; Jan Peters 001; Gerhard Neumann (IEEE Robotics Autom. Lett., 2020)

A Haptic Shared-Control Architecture for Guided Multi-Target Robotic Grasping. - Firas Abi-Farraj; Claudio Pacchierotti; Oleg Arenz; Gerhard Neumann; Paolo Robuffo Giordano (IEEE Trans. Haptics, 2020)

Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning. - Bang You; Jingming Xie; Youping Chen; Jan Peters 001; Oleg Arenz (CoRR, 2022)

Integrating contrastive learning with dynamic models for reinforcement learning from images. - Bang You; Oleg Arenz; Youping Chen; Jan Peters 001 (Neurocomputing, 2022)

LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning. - Firas Al-Hafez; Davide Tateo; Oleg Arenz; Guoping Zhao; Jan Peters 001 (ICLR, 2023)

A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models. - Oleg Arenz; Philipp Dahlinger; Zihan Ye; Michael Volpp; Gerhard Neumann (Trans. Mach. Learn. Res., 2023)

Digital Twin of a Driver-in-the-Loop Race Car Simulation With Contextual Reinforcement Learning. - Siwei Ju; Peter van Vliet; Oleg Arenz; Jan Peters 001 (IEEE Robotics Autom. Lett., 2023)

Machine Learning with Physics Knowledge for Prediction: A Survey. - Joe Watson; Chen Song; Oliver Weeger; Theo Gruner; An Thai Le 001; Kay Hansel; Ahmed Hendawy; Oleg Arenz; Will Trojak; Miles D. Cranmer; Carlo D'Eramo; Fabian Bülow; et al. (Trans. Mach. Learn. Res., 2025)

Rollout Total Correlation for Deep Reinforcement Learning. - Bang You; Huaping Liu; Jan Peters 001; Oleg Arenz (Trans. Mach. Learn. Res., 2025)

Learning from Less: Guiding Deep Reinforcement Learning with Differentiable Symbolic Planning. - Zihan Ye; Oleg Arenz; Kristian Kersting (CoRR, 2025)

LODGE: Joint Hierarchical Task Planning and Learning of Domain Models with Grounded Execution. - Claudius Kienle; Benjamin Alt; Oleg Arenz; Jan Peters 001 (CoRR, 2025)

Maximum Total Correlation Reinforcement Learning. - Bang You; Puze Liu; Huaping Liu; Jan Peters 001; Oleg Arenz (CoRR, 2025)

Context-Aware Deep Lagrangian Networks for Model Predictive Control. - Lucas Schulze; Jan Peters 001; Oleg Arenz (CoRR, 2025)