Learning to maintain engagement: No one leaves a sad DragonBot

Goren Gordon, Cynthia Breazeal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Engagement is a key factor in eveiy social interaction. be it between humans or humans and robots. Many studi es were aimed at designing robot behavior in order to sustain human engagement. Infants and children, howe ver, learn how to engage their caregivers to receive more attention. We used a social robot platform. Drago nBot. that learned which of its social behaviors ret ained human engagement. This was achieved by imp lementing a reinforcement learning algorithm, wherein the reward is the proximity and number of people near the robot. The experiment was run in the World Scie nce Festival in New York. where hundreds of people interacted with the robot. After more than two continuo us hours of interaction, the robot learned by itself that making a sad face was the most rewarding expression. Further analysis showed that after a sad face, people's engagement rose for thirty seconds. In other words, the robot learned by itself in two hours that almost no-one leaves a sad DragonBot.

Original languageEnglish
Title of host publicationArtificial Intelligence for Human-Robot Interaction - Papers from the AAAI Fall Symposium, Technical Report
PublisherAI Access Foundation
Number of pages2
ISBN (Electronic)9781577356912
StatePublished - 2014
Externally publishedYes
Event2014 AAAI Fall Symposium - Arlington, United States
Duration: 13 Nov 201415 Nov 2014

Publication series

NameAAAI Fall Symposium - Technical Report


Conference2014 AAAI Fall Symposium
Country/TerritoryUnited States


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