Wolfram Burgard is a Professor of Robotics and Artificial Intelligence at the University of Technology Nuremberg. Before this, he was a Professor for Autonomous Intelligent Systems at the University of Freiburg and also Vice President for Automated Driving Technology at the Toyota Research. His areas of interest lie in artificial intelligence and mobile robots. His research mainly focuses on the development of robust and adaptive techniques for robot state estimation and control. Over the past years, his group has developed a series of innovative probabilistic and machine learning techniques for robot navigation and perception. Wolfram Burgard has coauthored over 400 publications in robotics and artificial intelligence. He is a Fellow of the European European Association for Artificial Intelligence (EurAI), the Association for the Advancement of Artificial Intelligence (AAAI), and the Institute of Electrical and Electronics Engineers (IEEE). In 2009, he received the Gottfried Wilhelm Leibniz Prize, the most prestigious German research award. In 2010, he earned an Advanced Grant from the European Research Council. He is a member of the Heidelberg Academy of Science and the National Academy of Science Leopoldina. In 2022, he received the Technical Field Award of the IEEE Robotics and Automation Society.
KEYNOTE: Probabilistic and Machine Learning Approaches for Autonomous Robots and Automated Driving
For autonomous robots and automated driving, the capability to robustly perceive their environments and execute their actions is the ultimate goal. The key challenge is that no sensors and actuators are perfect, which means that robots and cars need the ability to properly deal with the resulting uncertainty. In this presentation, I will introduce the probabilistic approach to robotics, which provides a rigorous statistical methodology to solve the state estimation problem. I will furthermore discuss how this approach can be extended using state-of-the-art technology from machine learning to bring us closer to the development of truly robust systems that serve us in our everyday lives.