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.

翻译：为了使机器人能够协助用户完成家庭任务，它们必须首先从用户那里了解这些任务。此外，每天以相同方式重复执行同一任务可能会让机器人用户感到乏味，因此辅助机器人需要寻找创造性的方式来执行家庭任务。本文提出了一种家庭辅助机器人的认知架构，该架构能够从用户那里学习个性化的早餐选项，并利用所学知识布置早餐餐桌。该架构还能在较长时间内利用所学知识创造新的早餐选项。所提出的认知架构结合了最先进的感知学习算法、记忆编码与学习的认知模型计算实现、家庭物体拾取与放置的任务规划器、与用户交互的图形用户界面（GUI），以及利用所学知识创造新早餐选项的创新方法。该架构集成于Fetch移动操作机器人，并在一个包含多种厨房物体的大型室内环境中作为概念验证系统进行了评估。实验结果表明了该架构在从用户学习个性化早餐选项以及生成机器人从未学过的新早餐选项方面的有效性。