  In this paper, we consider a challenging but realistic continual learning
(CL) problem, Few-Shot Continual Active Learning (FoCAL), where a CL agent is
provided with unlabeled data for a new or a previously learned task in each
increment and the agent only has limited labeling budget available. Towards
this, we build on the continual learning and active learning literature and
develop a framework that can allow a CL agent to continually learn new object
classes from a few labeled training examples. Our framework represents each
object class using a uniform Gaussian mixture model (GMM) and uses
pseudo-rehearsal to mitigate catastrophic forgetting. The framework also uses
uncertainty measures on the Gaussian representations of the previously learned
classes to find the most informative samples to be labeled in an increment. We
evaluate our approach on the CORe-50 dataset and on a real humanoid robot for
the object classification task. The results show that our approach not only
produces state-of-the-art results on the dataset but also allows a real robot
to continually learn unseen objects in a real environment with limited labeling
supervision provided by its user.
