This work adds the concept of object to an existent low-level attention system of the humanoid robot iCub. The objects are defined as clusters of SIFT visual features. When the robot first encounters an unknown object, found to be within a certain (small) distance from its eyes, it stores a cluster of the features present within an interval about that distance, using depth perception. Whenever a previously stored object crosses the robot's field of view again, it is recognized, mapped into an egocentrical frame of reference, and gazed at. This mapping is persistent, in the sense that its identification and position are kept even if not visible by the robot. Features are stored and recognized in a bottom-up way. Experimental results on the humanoid robot iCub validate this approach. This work creates the foundation for a way of linking the bottom-up attention system with top-down, object-oriented information provided by humans.

This work presents a multimodal bottom-up attention system for the humanoid robot iCub where the robot's decisions to move eyes and neck are based on visual and acoustic saliency maps. We introduce a modular and distributed software architecture which is capable of fusing visual and acoustic saliency maps into one egocentric frame of reference. This system endows the iCub with an emergent exploratory behavior reacting to combined visual and auditory saliency. The developed software modules provide a flexible foundation for the open iCub platform and for further experiments and developments, including higher levels of attention and representation of the peripersonal space.