Intelligent Autonomous Systems Lab

Welcome to the Intelligent Autonomous Systems Group of the Computer Science Department of the Technische Universitaet Darmstadt. Our research centers around the goal of bringing advanced motor skills to robotics using techniques from machine learning and control. Please check out our research or contact any of our lab members. As we originated out of the RObot Learning Lab in the Department for Empirical Inference and Machine Learning at the Max-Planck Institute of Intelligent Systems, we also have a few members in Tuebingen.

Our agenda: Creating autonomous robots that can learn to assist humans in situations of daily life is a fascinating challenge for machine learning. While this aim has been a long-standing vision of artificial intelligence and the cognitive sciences, we have yet to achieve the first step of creating robots that can learn to accomplish many different tasks triggered by environmental context or higher-level instruction. The goal of our robot learning laboratory is the investigation of the ingredients for such a general approach to motor skill learning, to get closer towards human-like performance in robotics. We thus focus on the solution of basic problems in robotics while developing domain- appropriate machine-learning methods.

For doing so, we develop methods for learning models and control policy in real time, see e.g., learning models for control and learning operational space control. We are particularly interested in reinforcement learning where we try push the state-of-the-art further on and received a tremendous support by the RL community. Much of our research relies upon learning motor primitives that can be used to learn both elementary tasks as well as complex applications such as grasping or sports.

In case that you are searching for our address or for directions on how to get to our lab, look at our contact information.

We always have thesis opportunities for excellent Masters/Bachelors students (please contact Jan Peters instead). Check out the currently offered abschlussarbeiten or suggest one yourself, drop us a line by email or simply drop by! We also have open Ph.D. or Post-Doc positions please apply.

Some more information on us fore the general public can be found in an article by Max Planck Research and on the IEEE Blog on Robotics.

News

• Oliver Kroemer has a full NIPS-2011 plenary presentation on Kroemer, O.; Peters, J. (2011). A Non-Parametric Approach to Dynamic Programming, Advances in Neural Information Processing Systems 25 (NIPS 2011), Cambridge, MA: MIT Press.  download [PDF]
• Christian Daniel has gotten an AIStats paper accepted on Daniel, C.; Neumann, G.; Peters, J. (2012). Hierarchical Relative Entropy Policy Search, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS 2012)
• We had two ICRA papers: (i) Bocsi, B.; Hennig, P.; Csato, L.; Peters, J. (2012). Learning Tracking Control with Forward Models, Proceedings of the International Conference on Robotics and Automation (ICRA) and (ii) Kroemer, O. ; Ugur, E.; Oztop, E. ; Peters, J. (2012). A Kernel-based Approach to Direct Action Perception, Proceedings of the International Conference on Robotics and Automation (ICRA)