[CMU Intelligence Seminar] Robust Autonomous Color Learning on a Mobile Robot

Title : Robust Autonomous Color Learning on a Mobile Robot


Abstract :

The scientific community is slowly but surely working towards the creation of fully autonomous mobile robots capable of interacting with the proverbial real world. To operate in the real world, autonomous robots rely on their sensory information, but the ability to accurately sense the complex world is still missing. Visual input, in the form of color images from a camera, should be an excellent and rich source of such information, considering the significant amount of progress made in machine vision. But color, and images in general, have been used sparingly on mobile robots, where people have mostly focused their attention on other sensors such as tactile sensors, sonar and laser.

This talk presents the challenges raised and solutions introduced in our efforts to create a robust, color-based visual system for the Sony Aibo robot. We enable the robot to learn its color map autonomously and demonstrate a degree of illumination invariance under changing lighting conditions. Our contributions are fully implemented and operate in real time within the limited processing resources available onboard the robot. The system has been deployed in periodic robot soccer competitions, enabling teams of four Aibo robots to play soccer as a part of the international RoboCup initiative.


Bio :

Dr. Peter Stone is an Alfred P. Sloan Research Fellow and Assistant Professor in the Department of Computer Sciences at the University of Texas at Austin. He received his Ph.D in Computer Science in 1998 from Carnegie Mellon University. From 1999 to 2002 he was a Senior Technical Staff Member in the Artificial Intelligence Principles Research Department at AT&T Labs - Research. Peter's research interests include machine learning, multiagent systems, robotics, and e-commerce. In 2003, he won a CAREER award from the National Science Foundation for his research on learning agents in dynamic, collaborative, and adversarial multiagent environments. In 2004, he was named an ONR Young Investigator for his research on machine learning on physical robots. Most recently, he was awarded the prestigious IJCAI 2007 Computers and Thought award.