Seminario: Probabilistic and Deep Learning Techniques for Robot Navigation and Automated Driving

Abstract: For autonomous robots and automated driving, the ability to robustly perceive environments and execute their actions is the ultimate goal. The biggest challenge is that no sensors and actuators are perfect, which means that robots and cars must be able to properly deal with the resulting uncertainty. In this talk, I will introduce the probabilistic approach to robotics, which provides a rigorous statistical methodology for dealing with state estimation problems. In addition, I will discuss how this approach can be extended using state-of-the-art machine learning technologies to deal with complex and changing real-world environments.

Bio: Wolfram Burgard is a professor for computer science at the University of Nuremberg and head of the research lab for Autonomous Intelligent Systems and former head of the research lab for Autonomous Mobile Systems at the University of Freiburg. Over the past years he and his group have developed a series of innovative probabilistic techniques for robot navigation and control. They cover different aspects such as localization, map-building, SLAM, path-planning, exploration, and several other aspects. He has published over 350 papers and articles in robotics and artificial intelligence conferences and journals and several books. He has also obtained numerous awards for his research contributions. In 2008, he became a fellow of the European Coordinating Committee for Artificial Intelligence and he obtained the 2009 Gottfried Wilhelm Leibniz Prize, the most prestigious German research prize.

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