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.

家用机器人上安装的视觉系统需要与不可见的类别在多变的环境中进行交互。我们提出了 RobOCLe 作为一种少样本在线持续学习模型，通过构建富化特征空间和计算样本的高阶统计矩来改善连续学习模型的鲁棒性。