Interactive Continual Learning Architecture for Long-Term
  Personalization of Home Service Robots
Ali Ayub Chrystopher Nehaniv Kerstin Dautenhahn
For robots to perform assistive tasks in unstructured home environments, they
must learn and reason on the semantic knowledge of the environments. Despite a
resurgence in the development of semantic reasoning architectures, these
methods assume that all the training data is available a priori. However, each
user's environment is unique and can continue to change over time, which makes
these methods unsuitable for personalized home service robots. Although
research in continual learning develops methods that can learn and adapt over
time, most of these methods are tested in the narrow context of object
classification on static image datasets. In this paper, we combine ideas from
continual learning, semantic reasoning, and interactive machine learning
literature and develop a novel interactive continual learning architecture for
continual learning of semantic knowledge in a home environment through
human-robot interaction. The architecture builds on core cognitive principles
of learning and memory for efficient and real-time learning of new knowledge
from humans. We integrate our architecture with a physical mobile manipulator
robot and perform extensive system evaluations in a laboratory environment over
two months. Our results demonstrate the effectiveness of our architecture to
allow a physical robot to continually adapt to the changes in the environment
from limited data provided by the users (experimenters), and use the learned
knowledge to perform object fetching tasks.