| 2023 | ICLR | Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning. | Daniel Palenicek, Michael Lutter, Joao Carvalho, Jan Peters |
| 2021 | ICML | Value Iteration in Continuous Actions, States and Time. | Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg |
| 2021 | ICRA | Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning. | Michael Lutter, Johannes Silberbauer, Joe Watson, Jan Peters |
| 2020 | CoRL | High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards. | Kai Ploeger, Michael Lutter, Jan Peters |
| 2019 | CoRL | HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints. | Michael Lutter, Boris Belousov, Kim Listmann, Debora Clever, Jan Peters |
| 2019 | ICLR | Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning. | Michael Lutter, Christian Ritter, Jan Peters |
| 2019 | IROS | Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems. | Michael Lutter, Kim Listmann, Jan Peters |