  Despite the impressive progress brought by deep network in visual object
recognition, robot vision is still far from being a solved problem. The most
successful convolutional architectures are developed starting from ImageNet, a
large scale collection of images of object categories downloaded from the Web.
This kind of images is very different from the situated and embodied visual
experience of robots deployed in unconstrained settings. To reduce the gap
between these two visual experiences, this paper proposes a simple yet
effective data augmentation layer that zooms on the object of interest and
simulates the object detection outcome of a robot vision system. The layer,
that can be used with any convolutional deep architecture, brings to an
increase in object recognition performance of up to 7\%, in experiments
performed over three different benchmark databases. Upon acceptance of the
paper, our robot data augmentation layer will be made publicly available.
