Thomas Schön

Thomas Schön, Professor of Automatic Control at Uppsala University. Photo: Mikael Wallerstedt

Our aim is to automate the extraction of knowledge and understanding from data. Allowing machines (and humans) to understand what is happening and to acquire new skills and learn new things. We achieve this by developing new probabilistic models and deriving algorithms capable of learnings these models from data. The systematic use of probability in representing and manipulating these models is key. It allows us to represent not only what we know, but to some extent also what we do not know. We take a particular interest in dynamical phenomena evolving over time.

Our research is multi-disciplinary and it sits somewhere on the intersection of the areas of Machine learning and statistics, signal processing, automatic control and computer vision. We pursue both basic and applied research, which explains our tight collaboration with various companies. A slightly more detailed overview of our research is available here.

Recent research results/news

September 11, 2019 [New fast algorithm for IMUs accepted for IEEE Signal Processing Letters] Our new algorithm for online, real-time orientation estimation integrates gyroscope data and corrects the resulting orientation estimate for integration drift using accelerometer and magnetometer data. This correction is computed, at each time instance, using a single gradient descent step with fixed step length. This fixed step length results in robustness against model errors. Our new algorithm reduces the computational complexity by approximately 1/3 with respect to the state of the art. It also improves the quality of the resulting estimates, specifically when the orientation corrections are large.

Manon Kok and Thomas B. Schön. A fast and robust algorithm for orientation estimation using inertial sensorsIEEE Signal Processing Letters, 2019. (accepted)

September 4, 2019 [Spotlight paper at NeurIPS 2019] Our new developments concerning the problem of learning control policies for an unknown linear dynamical system to minimize a quadratic cost function are accepted for NeurIPS 2019 with a spotlight presentation. We present a method, based on convex optimization, that accomplishes this task robustly: i.e., we minimize the worst-case cost, accounting for system uncertainty given the observed data. The method balances exploitation and exploration, exciting the system in such a way so as to reduce uncertainty in the model parameters to which the worst-case cost is most sensitive.

Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön, Håkan Hjalmarsson. Robust exploration in linear quadratic reinforcement learningIn Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2019. (spotlight presentation)  [arXiv]

August 30, 2019 [SMC course this week] This week we once again gave our PhD level course on SMC methods. This year we had 85 participants, representing 23 different universities/companies. Thank you all for participating!

July 19, 2019 [4 papers accepted for CDC 2019] The use of deep learning for solving sequence learning problems is highly interesting and a very active research topic. We have investigated the use of deep learning for the specific task of nonlinear system identification and it is very clear that deep learning offers interesting possibilities also for this area. This initial paper (we will continue working on this promising topic) is mostly an initial experimental study where we play with this new technology on some benchmark dataset. The second paper propose a new method for Bayesian identification of nonlinear state-space models driven by our new debiasing technique for nonlinear state estimation in nonlinear state space models. The third and fourth papers are jointly published with the IEEE Control Systems Letters and are already reported below.

Carl Andersson, Antonio H. Ribeiro, Koen Tiels, Niklas Wahlström and Thomas B. Schön. Deep convolutional networks are useful in system identification. In Proceedings of the IEEE 58th IEEE Conference on Decision and Control (CDC), Nice, France, December, 2019.

Jack Umenberger, Thomas B. Schön and Fredrik Lindsten. Bayesian identification of state-space models via adaptive thermostats.  In Proceedings of the IEEE 58th IEEE Conference on Decision and Control (CDC), Nice, France, December, 2019.

Jack Umenberger and Thomas B. Schön. Nonlinear input design as optimal control of a Hamiltonian systemIEEE Control System Letters, 4(1):85-90, 2020. Jointly published at the 58th IEEE Conference on Decision and Control (CDC), Nice, France, December, 2019. [arXiv] [IEEE]

Mina Ferizbegovic, Jack Umenberger, Håkan Hjalmarsson and Thomas B. Schön. Learning robust LQ-controllers using application oriented explorationIEEE Control System Letters, 4(1):19-24, 2020. Jointly published at the  58th IEEE Conference on Decision and Control (CDC), Nice, France, December, 2019. [IEEE]

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 © Thomas Schön 2019