Marchstr. 23, MAR 5-5
10587 Berlin, Germany
- Info for Students @ TU Berlin
- Dear Students, I created an ISIS page for the "Optimization Algorithms" course, as well as for the "LIS Project", both offered this winter. Please register there. Within the next week I'll decide on the actual week days the events will happen and announce it via ISIS. In addition I'll cover the 2nd half of the Bachelor AI (Grundlagen KI) course (the first half is covered by Stefan Fricke, as in previous years).
- Info for Students @ U Stuttgart
- In March 2020 I moved to TU Berlin. Prof. Steffen Staab accepted a professorship for "Analytic Computing", and will cover the ML course in summer, and AI course in the winter 2020. Other profs are currently negotiating for professorships in "Autonomous Systems", and "ML in Simulation". My followup for "ML and Robotics" will be hired later in addition.
- LGP demonstrations
- See this page for multiple videos demonstrating Logic-Geometric Programming on various contexts (doesn't work in Firefox for me). The methods is described in our RSS'18 publication. The bottom of the page contains the RSS demonstrations, the top more recent advances. More to come. See the github repo for code.
- Max Planck Fellow
- From Nov 1st I am Max Plack Fellow with the MPI for Intelligent Systems.
- RSS'18 best paper
- M. Toussaint, K. R. Allen, K. A. Smith, and J. B. Tenenbaum: Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning. In Proc. of Robotics: Science and Systems (R:SS 2018), 2018. [Accompanying Video] [Source Code].
- some video lectures
- Brown Apr 18, 2018 Physical Reasoning and Admissible Symbols
- MIT Sep 19, 2017 Sufficient Symbols to Make Optimization-Based Manipulation Planning Tractable
- Tübingen Sep 5, 2013 Bandits, Active Learning, Bayesian RL and Global Optimization -- understanding the common ground
- Barcelona, Sep 12, 2013 Decision Making and Planning (and model-free RL) as Probabilistic Inference
since 03/20 Full Prof. at TU Berlin; head of the Learning and Intelligent Systems lab. since 11/18 Max Planck Fellow with the MPI for Intelligent Systems 08/17-07/18 Visiting Scholar at CSAIL, MIT (LIS group) 04/17-07/17 Lead of the ML-Robotics lab at Amazon, Berlin 12/12-02/20 Full Prof. at University of Stuttgart; head of the Machine Learning and Robotics Lab. 10/10-11/12 Prof. (W1) at the Department of Math and Computer Science, FU Berlin; head of the Machine Learning and Robotics Lab at FU Berlin 3/07-10/10 head of the Machine Learning and Robotics group (Emmy Noether Programme) at the IDA lab (Klaus-Robert Müller), TU Berlin. 8/06-2/07 guest scientist at the Honda Research Institute, Offenbach. 6/04-6/06 post doc at the Machine Learning group (Chris Williams) and the Statistical Machine Learning and Motor Control group (Sethu Vijayakumar), University of Edinburgh. 4/00-5/04 PhD student (& brief post doc) at the Adaptive Systems group, Institut für Neuroinformatik (Werner von Seelen), Ruhr-Universität-Bochum. 6/98-3/00 student at the Cologne gravity group (Friedrich W. Hehl), Institute for Theoretical Physics, U Cologne.
- current research interests
- Our research focusses on the combination of decision theory and machine learning, motivated by applications in robotics. The goal are learning systems that are able to reason about their own state of knowledge (e.g., in a Bayesian way) and decide which actions might yield the most informative future data, make them learn even better and eventually solve problems. We address this in the form of Reinforcement Learning, Planning and Active Learning in probabilistic relational domains. Further, a growing focus of our lab are real-world robotic systems and joint symbolic and geometric planning, including trajectory optimization and optimal control methods.
- Research in the intersections of modern AI (probabilistic reasoning, learning & planning), robotics and machine learning
- Probabilistic approaches to planning, on symbolic (relational) as well as motion & control level
- (Constrained) Optimization methods for robotics, reinforcement learning and machine learning in general
- Active learning, experimental design and UCB/UCT type methods for autonomous (e.g.\ robot) exploration of complex domains
- general Machine Learning: learning representations, Bayesian networks & graphical models