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.

本文提出了一种智能家居机器人的认知结构体系，用于学习用户的偏好从而个性化准备早餐，并且可以结合任务计划、图形用户界面等技术生成并创新新的早餐。针对 Fetch 移动机器人进行了验证，实验结果表明该认知结构体系能够高效地学习用户个性化的早餐偏好，并生成一些机器人从未学习过的新的早餐组合方式。