  A commercial robot, trained by its manufacturer to recognize a predefined
number and type of objects, might be used in many settings, that will in
general differ in their illumination conditions, background, type and degree of
clutter, and so on. Recent computer vision works tackle this generalization
issue through domain adaptation methods, assuming as source the visual domain
where the system is trained and as target the domain of deployment. All
approaches assume to have access to images from all classes of the target
during training, an unrealistic condition in robotics applications. We address
this issue proposing an algorithm that takes into account the specific needs of
robot vision. Our intuition is that the nature of the domain shift experienced
mostly in robotics is local. We exploit this through the learning of maps that
spatially ground the domain and quantify the degree of shift, embedded into an
end-to-end deep domain adaptation architecture. By explicitly localizing the
roots of the domain shift we significantly reduce the number of parameters of
the architecture to tune, we gain the flexibility necessary to deal with subset
of categories in the target domain at training time, and we provide a clear
feedback on the rationale behind any classification decision, which can be
exploited in human-robot interactions. Experiments on two different settings of
the iCub World database confirm the suitability of our method for robot vision.
