  The ability to evolve is fundamental for any valuable autonomous agent whose
knowledge cannot remain limited to that injected by the manufacturer. Consider
for example a home assistant robot: it should be able to incrementally learn
new object categories when requested, but also to recognize the same objects in
different environments (rooms) and poses (hand-held/on the floor/above
furniture), while rejecting unknown ones. Despite its importance, this scenario
has started to raise interest in the robotic community only recently and the
related research is still in its infancy, with existing experimental testbeds
but no tailored methods. With this work, we propose the first learning approach
that deals with all the previously mentioned challenges at once by exploiting a
single contrastive objective. We show how it learns a feature space perfectly
suitable to incrementally include new classes and is able to capture knowledge
which generalizes across a variety of visual domains. Our method is endowed
with a tailored effective stopping criterion for each learning episode and
exploits a self-paced thresholding strategy that provides the classifier with a
reliable rejection option. Both these novel contributions are based on the
observation of the data statistics and do not need manual tuning. An extensive
experimental analysis confirms the effectiveness of the proposed approach in
establishing the new state-of-the-art. The code is available at
https://github.com/FrancescoCappio/Contrastive_Open_World.
