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

Author:

Schadenberg Bob R.1,Reidsma Dennis1,Evers Vanessa2,Davison Daniel P.1,Li Jamy J.1,Heylen Dirk K. J.1,Neves Carlos3,Alvito Paulo3,Shen Jie4,Pantić Maja4,Schuller Björn W.5,Cummins Nicholas6,Olaru Vlad7,Sminchisescu Cristian7,Dimitrijević Snežana Babović8,Petrović Sunčica8,Baranger Aurélie9,Williams Alria10,Alcorn Alyssa M.10,Pellicano Elizabeth11

Affiliation:

1. University of Twente, Enschede, The Netherlands

2. University of Twente and Nanyang Technological University, Singapore, Singapore

3. IDMind, Lisbon, Portugal

4. Imperial College London, London, United Kingdom

5. University of Augsburg and Imperial College London, London, United Kingdom

6. University of Augsburg and King’s College London, London, United Kingdom

7. Institute of Mathematics of the Romanian Academy, Bucharest, Romania

8. Serbian Society of Autism, Belgrade, Serbia

9. Autism Europe, Brussels, Belgium

10. University College London, London, United Kingdom

11. University College London and Macquarie University,Macquarie Park, Australia

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.

Funder

European Union’s Horizon 2020 research and innovation program

Publisher

Association for Computing Machinery (ACM)

Subject

Human-Computer Interaction

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