Michael Muehlebach
Research Group Leader
Max Planck Ring 4
72076 Tuebingen
Germany
Michael Muehlebach leads the independent research group Learning and Dynamical Systems.
Michael studied mechanical engineering at ETH Zurich and specialized in robotics, systems, and control during his master's degree. He received the B.Sc. and the M.Sc. in 2010 and 2013, respectively, before joining the Institute for Dynamic Systems and Control for his Ph.D. He graduated under the supervision of Prof. R. D'Andrea in 2018. He then joined the group of Prof. M. I. Jordan at the University of California, Berkeley as a postdoctoral researcher.
His research lies at the intersection between machine learning, dynamical systems, and mathematical optimization. During his PhD, he worked on approximations of the constrained linear quadratic regulator problem with applications to model predictive control (see for example here). He also designed control, estimation, and learning algorithms for a balancing robot and a flying machine. His postdoctoral research aimed at analyzing gradient-based optimization algorithms and rigorously characterizing the mechanisms leading to accelerated convergence (see for example here).
He received the Outstanding D-MAVT Bachelor Award, the Willi-Studer prize for the best master's degree, and the ETH Medal and the HILTI prize for his doctoral thesis. He is a Branco Weiss Fellow since 2018 and was awarded the Emmy Noether Fellowship in 2020.
Flying Platform
The Flying Platform was designed to study ducted fan actuation. It was also used for benchmarking novel control strategies that account for actuation limits. Control algorithms explicitly accounting for these limitation can provide larger stability margins and other performance enhancements. A summary of some of the results can be found here.
The Loop
I supervised Julien Kohler's Master thesis, where we designed control, estimation, and learning algorithms for aggressive quadrotor maneuvers.
The Cubli: a cube that can jump up, balance, and 'walk'
The Cubli is a balancing robot that can balance on its corner and jump up. We investigated the dynamics, and implemented and tested a nonlinear controller. We also designed the learning algorithm that enables the system to adapt to a changing environment. A summary of the results can be found here.