Projects
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I work on inductive biases for robot learning. Within this field I focus on whether inductive biases are preferable for learning algorithms and how inductive biases can be incorporated in generic deep learning algorithms. I showed that (1) inductive biases can drastically improve sample efficiency for robot learning and (2) that generic inductive biases can be incorporated in deep learning models to obtain interpretable and physically plausible models.
A full list of all publications can be found at Google Scholar
Deep Lagrangian Networks (DeLaN)
Paper:

Lutter, M.; Ritter, C.; Peters, J. (2019). Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning, International Conference on Learning Representations (ICLR). [Arxiv]

Lutter, M.; Peters, J. (2019). Deep Lagrangian Networks for endtoend learning of energybased control for underactuated systems, International Conference on Intelligent Robots and Systems (IROS). [Arxiv] [Code]
Continuous Time Optimal Feedback Control
Paper:
 Lutter, M.; Belousov, B.; Listmann, K.; Clever, D.; Peters, J. (2019). HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints, Conference on Robot Learning (CoRL) [Arxiv]
Differentiable NewtonEulerAlgorithms
Paper:

Lutter, M.*; Silberbauer, J.*; Watson, J.; Peters, J. (2020). A Differentiable Newton Euler Algorithm for Multibody Model Learning, ICML Workshop on Inductive Biases, Invariances and Generalization in RL [Arxiv]

Lutter, M.*; Silberbauer, J.*; Watson, J.; Peters, J. (2020). Differentiable Physics Models for Realworld Offline Modelbased Reinforcement Learning, [Arxiv] [Videos]
Robot Juggling
Paper: