Probabilistic and Machine Learning Approaches for Autonomous Robots and Automated Driving

Speaker: Dr. Wolfram Burgard, Professor of Computer Science and head of the Research Lab for Autonomous Intelligent Systems, University of Freiburg and Vice President, Automated Driving Technology, Toyota Research Institute Abstract: The capability to robustly perceive their environments and to execute their actions is the ultimate goal in the areas of autonomous robots and automated driving. The key challenge is that there are no sensors and no actuators that are perfect, which means that robots and cars need the ability to properly deal with the resulting uncertainty. In this presentation, I will provide an introduction to the probabilistic approach to robotics, which provides a rigorous statistical methodology to deal with the perception and planning. 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 are able to serve us in our every-day life. Thumbnail