Online Learning of an Open-Ended Skill Library for Collaborative Tasks. - Dorothea Koert; Susanne Trick; Marco Ewerton; Michael Lutter; Jan Peters 001 (Humanoids, 2018)

HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints. - Michael Lutter; Boris Belousov; Kim Listmann; Debora Clever; Jan Peters 001 (CoRL, 2019)

Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning. - Michael Lutter; Christian Ritter; Jan Peters 001 (ICLR (Poster), 2019)

Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems. - Michael Lutter; Kim Listmann; Jan Peters 001 (IROS, 2019)

High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards. - Kai Ploeger; Michael Lutter; Jan Peters 001 (CoRL, 2020)

A Differentiable Newton Euler Algorithm for Multi-body Model Learning. - Michael Lutter; Johannes Silberbauer; Joe Watson; Jan Peters 001 (CoRR, 2020)

Incremental Learning of an Open-Ended Collaborative Skill Library. - Dorothea Koert; Susanne Trick; Marco Ewerton; Michael Lutter; Jan Peters 001 (Int. J. Humanoid Robotics, 2020)

Trajectory Optimization of Energy Consumption and Expected Service Life of a Robotic System. - Florian Stuhlenmiller; Debora Clever; Stephan Rinderknecht; Michael Lutter; Jan Peters 001 (AIM, 2021)

Building Skill Learning Systems for Robotics. - Michael Lutter; Debora Clever; René Kirsten; Kim Listmann; Jan Peters 001 (CASE, 2021)

Value Iteration in Continuous Actions, States and Time. - Michael Lutter; Shie Mannor; Jan Peters 001; Dieter Fox; Animesh Garg (ICML, 2021)

Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning. - Michael Lutter; Johannes Silberbauer; Joe Watson; Jan Peters 001 (ICRA, 2021)

Robust Value Iteration for Continuous Control Tasks. - Michael Lutter; Shie Mannor; Jan Peters 001; Dieter Fox; Animesh Garg (Robotics: Science and Systems, 2021)

Learning Dynamics Models for Model Predictive Agents. - Michael Lutter; Leonard Hasenclever; Arunkumar Byravan; Gabriel Dulac-Arnold; Piotr Trochim; Nicolas Heess; Josh Merel; Yuval Tassa (CoRR, 2021)

A Differentiable Newton-Euler Algorithm for Real-World Robotics. - Michael Lutter; Johannes Silberbauer; Joe Watson; Jan Peters 001 (CoRR, 2021)

Inductive Biases in Machine Learning for Robotics and Control - Michael Lutter (2021)

Revisiting Model-based Value Expansion. - Daniel Palenicek; Michael Lutter; Jan Peters 001 (CoRR, 2022)

Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning. - Daniel Palenicek; Michael Lutter; Joao Carvalho; Jan Peters 001 (ICLR, 2023)

Inductive Biases in Machine Learning for Robotics and Control - Michael Lutter (Springer, 2023)

Combining physics and deep learning to learn continuous-time dynamics models. - Michael Lutter; Jan Peters 001 (Int. J. Robotics Res., 2023)

Continuous-Time Fitted Value Iteration for Robust Policies. - Michael Lutter; Boris Belousov; Shie Mannor; Dieter Fox; Animesh Garg; Jan Peters 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Diminishing Return of Value Expansion Methods. - Daniel Palenicek; Michael Lutter; João Carvalho; Daniel Dennert; Faran Ahmad; Jan Peters 001 (CoRR, 2024)