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Joint research on Machine Learning and Robotics has received increasingly more attention recently. There are two reasons for this trend: First, robots that cannot learn lack one of the most interesting aspects of intelligence. Much of classical robotics focussed on reasoning, optimal control and sensor processing given models of the robot and its environment. While this approach is successful for many industrial applications, it falls behind the more ambitious goal of Robotics as a test platform for our understanding of artificial and natural intelligence. Learning therefore has become a central topic in modern Robotics research. Second, Machine Learning has proven very successful on many applications of statistical data analysis, like speech, vision, text, genetics, etc. However, although Machine Learning methods largely outperform humans in extracting statistical models from abstract data sets, our understanding of learning in natural environments---and learning what is relevant for behavior in natural environments---is limited. Therefore, robotics research motivates new and interesting kinds of challenges for Machine Learning. This tutorial targets at Machine Learning researchers interested in the challenges of Robotics. It will introduce---in ML lingo---basics of Robotics and discuss which kinds of ML research are particularly promising to advance the field of Learning in Robotics.

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