Autonomous Color Learning on a Mobile Robot (2005)
Color segmentation is a challenging subtask in computer vision. Most popular approaches are computationally expensive, involve an extensive off-line training phase and/or rely on a stationary camera. This paper presents an approach for color learning on-board a legged robot with limited computational and memory resources. A key defining feature of the approach is that it works without any labeled training data. Rather, it trains autonomously from a color-coded model of its environment. The process is fully implemented, completely autonomous, and provides high degree of segmentation accuracy.
In Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005.

Mohan Sridharan Ph.D. Alumni mhnsrdhrn [at] gmail com
Peter Stone Faculty pstone [at] cs utexas edu