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MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMSAutonomous Motion Department (Prof. Stefan Schaal)
Master Thesis Project
Gaussian Process Optimization forSelf-Tuning Control
A self-tuning controller can automatically adjust its feedback gains based on experimental data and thusimprove its performance during operation. In previous work1, we have developed a self-tuning LinearQuadratic Regulator (LQR) that accomplishes an automatic improvement by means of a stochasticgradient descent algorithm. This Master Thesis project aims at enhancing the self-tuning LQR by usingGaussian process optimization for adjusting the controller gains. In contrast to the existing algorithm,the improved self-tuner shall make better use of the available data by learning the controller cost functionand optimally choosing the next controller tuning by trading off exploration versus exploitation.
This project falls into the ongoing research on learning control at the Autonomous Motion De-partment (AMD), where we seek to combine techniques from machine learning, control theory, andoptimization to develop intelligent control algorithms for autonomous robots. For experimental valida-tion, we have a variety of state-of-the-art robotic platforms (see photo below for an example). Withinthis project, the developed learning controllers shall be demonstrated on one of these platforms.
The proposed project is research-oriented and theoretically challenging. At the same time, the projectallows the student to gain hands-on experience with sophisticated robotic systems.
[Photo: Wolfram Scheible, left graphic: Rasmussen/Williams, MIT Press, 2006]
Autonomous Motion Department (http://www-amd.is.tuebingen.mpg.de)
The AMD is headed by Prof. Stefan Schaal and part of the Max Planck Institute for Intelligent Systems(MPI-IS) located in Tubingen, Germany (near Stuttgart). This project is offered as a joint projectbetween the AMD and the Learning & Adaptive Systems Group (Prof. Andreas Krause) at ETHZurich. Accommodation at the MPI-IS guest house may be available for the duration of the project,and travel to international conferences can be supported if the project leads to such publications.
Prerequisites
High motivation and excellent theoretical and technical skills. Programming experience (C/C++, Mat-lab). Background in control or machine learning is a plus.
Contact
Do not hesitate to contact us if you are interested in this project. When applying, please include yourCV, current grade transcript, a short motivation statement (why are you interested in the project?),and optionally other documentation helpful to evaluate your background. After initial screening, we willinvite suitable candidates to visit the AMD in Tubingen.
Dr. Sebastian Trimpe, Max Planck Institute for Intelligent Systems, [email protected]. Andreas Krause, ETH Zurich, [email protected]
1http://www-amd.is.tuebingen.mpg.de/pub/uploads/Main/SebastianTrimpe/IFAC14_1455_web.pdf