Color Learning on a Mobile Robot: Towards Full Autonomy under Changing Illumination

Abstract
A central goal of robotics and AI is to be able to de- ploy 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 suf- ficient. For true extended autonomy, an agent must also be able to recognize when to abandon its cur- rent 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 segmenta- tion. Past research established the ability of a robot to learn a color map in a single fixed lighting con- dition when manually given a “curriculum,” an ac- tion 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.

Read the Paper.

Okay, super nerdy. But the investigation into artificial intelligence is fascinating. Color input and the ability to recognize inputs and make decisions about abandoning current models in favor of learning a new one... um, super cool. Autonomy at is purest, more terrifying.