  Vision systems mounted on home robots need to interact with unseen classes in
changing environments. Robots have limited computational resources, labelled
data and storage capability. These requirements pose some unique challenges:
models should adapt without forgetting past knowledge in a data- and
parameter-efficient way. We characterize the problem as few-shot (FS) online
continual learning (OCL), where robotic agents learn from a non-repeated stream
of few-shot data updating only a few model parameters. Additionally, such
models experience variable conditions at test time, where objects may appear in
different poses (e.g., horizontal or vertical) and environments (e.g., day or
night). To improve robustness of CL agents, we propose RobOCLe, which; 1)
constructs an enriched feature space computing high order statistical moments
from the embedded features of samples; and 2) computes similarity between high
order statistics of the samples on the enriched feature space, and predicts
their class labels. We evaluate robustness of CL models to train/test
augmentations in various cases. We show that different moments allow RobOCLe to
capture different properties of deformations, providing higher robustness with
no decrease of inference speed.
