International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
CM-Pack'01: Fast Legged Robot Walking, Robust Localization, and Team Behaviors
RoboCup 2001: Robot Soccer World Cup V
Appearance-Based Obstacle Detection with Monocular Color Vision
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Autonomous color learning on a mobile robot
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
RoboCup 2007: Robot Soccer World Cup XI
Learning and multiagent reasoning for autonomous agents
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Practical color-based motion capture
SCA '11 Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
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A central goal of robotics and AI is to be able to deploy an agent to act autonomously in the real world over an extended period of time. It is commonly asserted that in order to do so, the agent must be able to learn to deal with unexpected environmental conditions. However an ability to learn is not sufficient. For true extended autonomy, an agent must also be able to recognize when to abandon its current model in favor of learning a new one; and how to learn in its current situation. This paper presents a fully implemented example of such autonomy in the context of color map learning on a vision-based mobile robot for the purpose of image segmentation. Past research established the ability of a robot to learn a color map in a single fixed lighting condition when manually given a "curriculum," an action sequence designed to facilitate learning. This paper introduces algorithms that enable a robot to i) devise its own curriculum; and ii) recognize when the lighting conditions have changed sufficiently to warrant learning a new color map.