Future computer programs will contain a growing part of 'intelligent' software modules that are not conventionally programmed, but that are learned either from data provided by the user or from data that the program autonomously collects during its use.
In this spirit, the Machine Learning Lab deals with research on Machine Learning techniques and the integration of learning modules into larger software systems, aiming at their effective application in complex real-world problems. Application areas are robotics, control, forecasting and disposition systems, scheduling and related fields.
Research Areas: Efficient Reinforcement Learning Algorithms, Intelligent Robot Control Architectures, Learning in Multiagent Systems, (Un-)Supervised Learning, Autonomous Robots, Industrial Applications
News
Open Source RoboCup Mid-Size Robot
Open Source RoboCup Mid-Size Robot 20.4.2012
We are proud to introduce the new Website for openTribot, our Open Source RoboCup Mid-Size Robotic Platform. The DFG Transfer Projects Goal was the creation of a robust, easy to use, performant Robot that can be used in RoboCup Mid-Size Soccer. Our Prototype Robots are complete and can be programmed with the ROS Robotic Operating System.
A neural racing car driver
New EXIST-grant awarded to Group Member 7.11.2011
We congratulate Sascha and Patrick to their recently awarded “EXIST-Gründerstipendium”. This grant will allow them to work full-time on their idea of founding a new, highly-innovative games company here in Freiburg (see http://www.5dlab.com). Their next product will be a location-aware strategy-MMO.
A neural racing car driver
A Neural Racing Car Driver 27.05.2011
Learning to drive a virtual racing car - this idea was realized in the bachelor thesis written by Thorsten Engesser. A neural network based driver learns to drive fast but save, controlling the car by steering, breaking and accelerating. The learning car is realized in the Torcs-framework, showing highly competitive driving behaviour, finally learned from the experience of success and failure (see video). Our data-efficient batch-RL scheme NFQ was used to realize the learning. More learning videos can be found in this list.
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