Peripersonal space and object recognition for humanoids

This work is concerned with a framework for visual object recognition in real world tasks. Our approach is motivated by biological findings of the representation of space around the body, the so-called peripersonal space. We show that the principles behind those findings can lead to a natural structuring of object recognition tasks in artificial systems. We demonstrate this by the supervised learning and recognition of 20 complex-shaped objects from unsegmented visual input