Predictable Robots for Autistic Children — Variance in Robot Behaviour, Idiosyncrasies in Autistic Children’s Characteristics, and Child–Robot Engagement

Bob Rinse Schadenberg*, Dennis Reidsma, Vanessa Evers, Daniel Patrick Davison, Jamy Jue Li, Dirk K.J. Heylen, Carlos Neves, Paulo Alvito, Jie Shen, Maja Pantic, Björn Schuller, Nicholas Cummins, Vlad Olaru, Cristian Sminchisescu, Snezana Babovic Dimitrijevic, Suncica Petrovic, Aurélie Baranger, Alria Williams, Alyssa M. Alcorn, Elizabeth Pellicano

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Predictability is important to autistic individuals, and robots have been suggested to meet this need as they can be programmed to be predictable, as well as elicit social interaction. The effectiveness of robot-assisted interventions designed for social skill learning presumably depends on the interplay between robot predictability, engagement in learning, and the individual differences between different autistic children. To better understand this interplay, we report on a study where 24 autistic children participated in a robot-assisted intervention. We manipulated the variance in the robot’s behaviour as a way to vary predictability, and measured the children’s behavioural engagement, visual attention, as well as their individual factors. We found that the children will continue engaging in the activity behaviourally, but may start to pay less visual attention over time to activity-relevant locations when the robot is less predictable. Instead, they increasingly start to look away from the activity. Ultimately, this could negatively influence learning, in particular for tasks with a visual component. Furthermore, severity of autistic features and expressive language ability had a significant impact on behavioural engagement. We consider our results as preliminary evidence that robot predictability is an important factor for keeping children in a state where learning can occur.
Original languageEnglish
Article number3468849
Number of pages42
JournalACM Transactions on Computer-Human Interaction
Volume28
Issue number5
DOIs
Publication statusPublished - 21 Aug 2021

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