Modulating vision with motor plans: A biologically-inspired efficient allocation of visual resources

This paper presents a novel, biologically-inspired, approach for an efficient management of computational resources for visual processing. In particular, we modulate a visual “attentional landscape” with the motor plans of a robot. The attentional landscape is a more recent, general and a more complex concept of an arrangement of spatial attention than a simple “attentional spotlight” or a “zoom-lens” model of attention. A higher attention priority for visual processing must be given to manipulation-relevant parts of the visual field, in contrast with other, manipulation-irrelevant, parts. Hence, in our model visual attention is not exclusively defined in terms of visual saliency in color, texture or intensity cues, it is rather modulated by motor (manipulation) programs. This computational model is supported by recent experimental findings in visual neuroscience and physiology. We show how this approach can be used to efficiently distribute limited computational resources devoted to visual processing, which is very often the computational bottleneck in a robot system. The model offers a view on the well-known concept of visual saliency that has not been tackled so far, thus this approach can offer interesting alternative prospects not only for robotics, but also for computer vision, physiology and neuroscience. The proposed model is validated in a series of experiments conducted with the iCub robot, both using the simulator and with the real robot.