A Model-Free Affective Reinforcement Learning Approach to Personalization of an Autonomous Social Robot Companion for Early Literacy Education
Name
AAAI2019.pdf
Description
Accepted version
Size
4.72 MB
Format
Adobe PDF
Checksum (MD5)
060224f153f02d4d441a2b80529164be
Author(s)
Park, Hae Won
Grover, Ishaan
Spaulding, Samuel
Gomez, Louis
Breazeal, Cynthia
Date Issued
2019
Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Citation
Park, Hae Won, Grover, Ishaan, Spaulding, Samuel, Gomez, Louis and Breazeal, Cynthia. 2019. "A Model-Free Affective Reinforcement Learning Approach to Personalization of an Autonomous Social Robot Companion for Early Literacy Education." Proceedings of the AAAI Conference on Artificial Intelligence, 33.
Version
Author's final manuscript
Abstract
© 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Personalized education technologies capable of delivering adaptive interventions could play an important role in addressing the needs of diverse young learners at a critical time of school readiness. We present an innovative personalized social robot learning companion system that utilizes children's verbal and nonverbal affective cues to modulate their engagement and maximize their long-term learning gains. We propose an affective reinforcement learning approach to train a personalized policy for each student during an educational activity where a child and a robot tell stories to each other. Using the personalized policy, the robot selects stories that are optimized for each child's engagement and linguistic skill progression. We recruited 67 bilingual and English language learners between the ages of 4-6 years old to participate in a between-subjects study to evaluate our system. Over a three-month deployment in schools, a unique storytelling policy was trained to deliver a personalized story curriculum for each child in the Personalized group. We compared their engagement and learning outcomes to a Non-personalized group with a fixed curriculum robot, and a baseline group that had no robot intervention. In the Personalization condition, our results show that the affective policy successfully personalized to each child to boost their engagement and outcomes with respect to learning and retaining more target words as well as using more target syntax structures as compared to children in the other groups.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1609/AAAI.V33I01.3301687
