A Model-free Affective Reinforcement Learning Approach to Personalization of a Social Robot Companion for Early Literacy Education

Jan. 29, 2019


Hae Won Park, Ishaan Grover, Samuel Spaulding, Louis Gomez, and Cynthia Breazeal. “A Model- free Affective Reinforcement Learning Approach to Personalization of a Social Robot Companion for Early Literacy Education” In Association for the Advancement of Artificial Intelligence (AAAI), 2019.


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. 

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