CBCL-PR: A Cognitively Inspired Model for Class-Incremental Learning in
  Robotics
Ali Ayub Alan R. Wagner
For most real-world applications, robots need to adapt and learn continually
with limited data in their environments. In this paper, we consider the problem
of Few-Shot class Incremental Learning (FSIL), in which an AI agent is required
to learn incrementally from a few data samples without forgetting the data it
has previously learned. To solve this problem, we present a novel framework
inspired by theories of concept learning in the hippocampus and the neocortex.
Our framework represents object classes in the form of sets of clusters and
stores them in memory. The framework replays data generated by the clusters of
the old classes, to avoid forgetting when learning new classes. Our approach is
evaluated on two object classification datasets resulting in state-of-the-art
(SOTA) performance for class-incremental learning and FSIL. We also evaluate
our framework for FSIL on a robot demonstrating that the robot can continually
learn to classify a large set of household objects with limited human
assistance.