stanford.seal64.gif (1,768 bytes)

Broad Area Colloquium for Artificial Intelligence,
Geometry, Graphics, Robotics and Vision

What Do Mobile Robots And Bayesian Statistics Have In Common?

Sebastian Thrun
Carnegie Melon and Stanford

Monday, October 8, 2001, 4:15PM
Gates B01


In recent years, several hard problems in mobile robotics have been solved using probabilistic techniques. These solutions have led to deployed mobile robot systems of unprecedented robustness, in application domains ranging from autonomous underwater robotics to interactive service robots that give tours to kids in museums. The goal of this talk is to introduce the audience to a rich and fascinating body of work on probabilistic robotics. The speaker will present basic estimation and control techniques and show how they lead to new and more robust solutions in problems such as mobile robot localization, mapping, exploration, and people interaction. Special emphasis will be given to the challenges that arise when transitioning from basic statistical theory to physical systems operating in the real world. To illustrate these concepts, the speaker plans to discuss in depth several robot systems that his group recently developed, and that were deployed in places like a Smithsonian museum in Washington, DC, and an elderly care facility near Pittsburgh, PA.

About the Speaker

Sebastian Thrun is an Associate Professor of Computer Science and Robotics at Carnegie Mellon University, presently on sabbatical at Stanford University. Thrun pursues research in artificial intelligence, machine learning, and robotics.


Back to the Colloquium Page