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For robots to assist users with household tasks, they must first learn aboutthe tasks from the users. Further, performing the same task every day, in thesame way, can become boring for the robot's user(s), therefore, assistiverobots must find creative ways to perform tasks in the household. In thispaper, we present a cognitive architecture for a household assistive robot thatcan learn personalized breakfast options from its users and then use thelearned knowledge to set up a table for breakfast. The architecture can alsouse the learned knowledge to create new breakfast options over a longer periodof time. The proposed cognitive architecture combines state-of-the-artperceptual learning algorithms, computational implementation of cognitivemodels of memory encoding and learning, a task planner for picking and placingobjects in the household, a graphical user interface (GUI) to interact with theuser and a novel approach for creating new breakfast options using the learnedknowledge. The architecture is integrated with the Fetch mobile manipulatorrobot and validated, as a proof-of-concept system evaluation in a large indoorenvironment with multiple kitchen objects. Experimental results demonstrate theeffectiveness of our architecture to learn personalized breakfast options fromthe user and generate new breakfast options never learned by the robot.

A Personalized Household Assistive Robot that Learns and Creates New Breakfast Options through Human-Robot Interaction