About Me

Michael Lutter
Technical Manager & Research Scientist
at Boston Dynamics

Contact: mail(at)mlutter.eu
Twitter: _mlutter

Research Interests:
Learning Dynamics Models,
High-Speed Robot Control,
Reinforcement Learning,
Legged Locomotion
Robust Control

Bio: Michael Lutter is a technical manager and research scientist at Boston Dynamics. Within the Atlas team, his team works on applying reinforcement learning to dexterous manipulation with humanoid robots. Previously he worked on reactive quadruped locomotion over slippery terrain with Spot. He completed his Ph.D. supervised by Jan Peters at the Institute for Intelligent Autonomous Systems (IAS) TU Darmstadt in November 2021. During his Ph.D., he researched inductive biases for robot learning. Michael also worked with ABB Corporate Research on a joint research project to evaluate the application of robot learning to industrial applications. He completed a research internship at DeepMind, NVIDIA Research and received multiple awards for his research including the George Giralt Ph.D. Award (2022) for the best robotics Ph.D. thesis in Europe and the AI newcomer award (2019) of the German computer science foundation.

From 2016 to 2017, Michael held a researcher position at the Technical University of Munich (TUM) for bio-inspired learning for robotics. During this time he worked for the Neurorobotics subproject of the Human Brain Project, a European H2020 FET flagship project. In addition, he taught the classes "Deep Learning for Autonomous Systems” and “Fundamentals of Computer Science for Neuroengineering” within the Elite Master Programm Neuroengineering and participated in teaching Think. Make. Start., a two-week prototyping course. His educational background covers a Bachelors in Engineering Management from University of Duisburg Essen and a Masters in Electrical Engineering from the Technical University of Munich. During his undergraduate studies he also spent one semester at the Massachusetts Institute of Technology studying electrical engineering and computer science. In addition to his studies, Michael worked for ThyssenKrupp, Siemens and General Electric and received multiple scholarships for academic excellence.

News

(07.Feb 25) - Invited Talk at the MIT Sloan AI Conference on RL for robotics
(29.Dec 24) - New preprint on the diminishing returns of value expansion [Arxiv]
(09.Nov 24) - Invited Talk at CoRL WS on Learning for Locomotion
(15.Jul 24) - Invited Talk at RSS WS on Embodiement-Aware Robot Learning
(15.Jul 24) - Invited Talk at RSS WS on Structural Priors for Robot Learning
(15.Jul 24) - Invited Talk at the Freiburg Robotics and Biology conference
(01.Oct 23) - Invited Talk at IROS WS on Reinforcement Learning
(01.Aug 23) - Published my Ph.D. thesis in the Springer STAR series [Springer]
(19.Apr 23) - Accepted IJRR Journal Paper on Deep Lagrangian Networks [IJRR][Arxiv]
(22.Jan 23) - Accepted ICLR Paper Diminishing Return of Value Expansion [Arxiv]
(14.Dec 22) - Invited Talk CoRL WS on Inductive Bias in Robot Learning
(19.Oct 22) - Accepted TPAMI Journal Paper on Value Iteration [IEEE][Arxiv]
(29.Jun 22) - Received the George Giralt Award for best robotics Ph.D. thesis in Europe
(23.May 22) - Joined the Boston Dynamics Atlas team to work on RL for locomotion
(19.Nov 21) - Defended my Ph.D. on Robot Learning [Thesis]
(29.Sep 21) - New pre-print on learning dynamics models for MPC [Arxiv]
(10.May 21) - Accepted RSS Paper Robust Value Iteration for Continuous Control [Arxiv]
(08.May 21) - Accepted ICML Paper Value Iteration in continuous space and time [Arxiv]
(01.Apr 21) - Accepted ICRA Paper Model-Learning for offline RL [Arxiv]
(14.Jan 21) - Started my Research Internship with the DeepMind Robotics Team
(11.Dec 20) - Organizing NeurIPS WS on Inductive Biases and Physically Structured Learning
(25.Oct 20) - Invited Talk IROS WS on Trends and Advances in ML and Automated Reasoning
(14.Oct 20) - Robot Juggling paper accepted at CoRL 2020 [Arxiv]
(01.Oct 20) - My Homepage is finally live :)