  For robots to assist users with household tasks, they must first learn about
the tasks from the users. Further, performing the same task every day, in the
same way, can become boring for the robot's user(s), therefore, assistive
robots must find creative ways to perform tasks in the household. In this
paper, we present a cognitive architecture for a household assistive robot that
can learn personalized breakfast options from its users and then use the
learned knowledge to set up a table for breakfast. The architecture can also
use the learned knowledge to create new breakfast options over a longer period
of time. The proposed cognitive architecture combines state-of-the-art
perceptual learning algorithms, computational implementation of cognitive
models of memory encoding and learning, a task planner for picking and placing
objects in the household, a graphical user interface (GUI) to interact with the
user and a novel approach for creating new breakfast options using the learned
knowledge. The architecture is integrated with the Fetch mobile manipulator
robot and validated, as a proof-of-concept system evaluation in a large indoor
environment with multiple kitchen objects. Experimental results demonstrate the
effectiveness of our architecture to learn personalized breakfast options from
the user and generate new breakfast options never learned by the robot.
