Purposeful perception by attention-steered robots

Designers of autonomous systems, embodied in an uncertain environment, have the tendency to build up a world model from everything that can be perceived. In contrast to this view, psychological researchers find for humans a selective interpretation of a scene, with phenomena like inattentional blindness. Objects remain unseen if they are not central to the current behavior, even while they are clearly within view. Previous research in the Dutch Aibo Team has proven that also for robots behavior-specific image processing can be very beneficial. In this article we design an experiment where we can not only indicate the appropriate moments to limit the perception to the objects relevant to the task, but also indicate the appropriate moments to release those limitations and to increase the overall situation awareness.