  Robotic vision is a field where continual learning can play a significant
role. An embodied agent operating in a complex environment subject to frequent
and unpredictable changes is required to learn and adapt continuously. In the
context of object recognition, for example, a robot should be able to learn
(without forgetting) objects of never before seen classes as well as improving
its recognition capabilities as new instances of already known classes are
discovered. Ideally, continual learning should be triggered by the availability
of short videos of single objects and performed on-line on on-board hardware
with fine-grained updates. In this paper, we introduce a novel continual
learning protocol based on the CORe50 benchmark and propose two rehearsal-free
continual learning techniques, CWR* and AR1*, that can learn effectively even
in the challenging case of nearly 400 small non-i.i.d. incremental batches. In
particular, our experiments show that AR1* can outperform other
state-of-the-art rehearsal-free techniques by more than 15% accuracy in some
cases, with a very light and constant computational and memory overhead across
training batches.
