Probabilistic and Machine Learning Approaches for Autonomous Robots and Automated Driving
Abstract: For autonomous robots and automated driving, the capability to robustly perceive 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 deal with 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 able to serve us in our every-day lives.
Bio: Wolfram Burgard is VP for Automated Driving Technology at the Toyota Research Institute in Los Altos, USA and holds a Professorship for Autonomous Intelligent Systems at the University of Freiburg, Germany. His interests lie in AI and Robotics. He has made substantial contributions to several relevant problems in robotics including state estimation, navigation, localization, SLAM and mobile manipulation. For his work, Wolfram Burgard received the Gottfried Wilhelm Leibniz Prize from the Deutsche Forschungsgemeinschaft, the most prestigious German research award, and an Advanced Grant from the European Research Council. He is fellow of the AAAI, the EurAI as well as the IEEE. He currently also serves as President of the IEEE Robotics and Automation Society.