  Children learn continually by asking questions about the concepts they are
most curious about. With robots becoming an integral part of our society, they
must also learn unknown concepts continually by asking humans questions. This
paper presents a novel framework for curiosity-driven online learning of
objects. The paper utilizes a recent state-of-the-art approach for continual
learning and adapts it for online learning of objects. The paper further
develops a self-supervised technique to find most of the uncertain objects in
an environment by utilizing an internal representation of previously learned
classes. We test our approach on a benchmark dataset for continual learning on
robots. Our results show that our curiosity-driven online learning approach
beats random sampling and softmax-based uncertainty sampling in terms of
classification accuracy and the total number of classes learned.
