A Personalized Household Assistive Robot that Learns and Creates New Breakfast Options through Human-Robot Interaction
Ali Ayub, Chrystopher L. Nehaniv, Kerstin Dautenhahn

TL;DR
This paper introduces a cognitive architecture for a household assistive robot that learns personalized breakfast options through human interaction and creatively generates new options, enhancing household task assistance.
Contribution
The paper presents a novel cognitive architecture integrating perceptual learning, memory models, and a task planner, enabling a robot to learn and create new breakfast options over time.
Findings
Robot successfully learned personalized breakfast options.
Robot generated new breakfast options not previously learned.
Architecture proved effective in a real indoor kitchen environment.
Abstract
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…
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Taxonomy
TopicsRobotics and Automated Systems · Urban Planning and Valuation · Cognitive Science and Mapping
