IFRR Colloquium on Autonomous Driving

This presentation introduces the probabilistic approach to robotics, which provides a rigorous statistical methodology to solve the state estimation problem.

Speaker: Wolfram Burgard
Toyota Research Institute

Moderator: Henrik I. Christensen
University of California San Diego

Front Row Participants: Marcelo Ang, Andrea Censi, Evangelos Theodorou, David Paz

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, Wolfram Burgard will introduce the probabilistic approach to robotics, which provides a rigorous statistical methodology to solve the state estimation problem. He will furthermore discuss with an expert panel 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. In this context, he will, in particular, focus on the data advantage that the Toyota Research Institute is planning to leverage in combination with self-semi-supervised methods for machine learning to speed up the process of developing self-driving cars.

More details may be found on the event page: http://ifrr.org/autonomous-driving