Robot Expressive Behaviour and Autistic Traits Peter E McKenna, Ruth Aylett, Ayan Ghosh, Frank Broz, Ingo Keller and Gnanathusharan Rajendran Heriot-Watt University School of Social Sciences | School of Mathematical & Computer Sciences
Background
Motivation
Experiment Procedure
• In the UK a disproportionate number of adults with autism spectrum disorder (ASD) are currently unemployed, with an estimated 16% holding full-time positions [2].
• Studies of autistic children interacting with robots indicate that interaction can foster positive social behaviours outside of the laboratory.
• Alyx asked participants to offer food, and raised it’s right hand.
• As initial work towards this system, we asked neuro-typical participants to give their interpretation of robot facial expressions, and cross referenced task performance with participant’s autistic traits.
• Once RFID tag read, Alyx lowered hand and produced an expression.
• Although willing to work, many of these adults do not possess the necessary understanding of the social nuances typical to workplace interactions [4]. Unusual low-level processing of “social signals” may help explain to this. • Social signals are the verbal and non-verbal cues that are elicited during communication to help interlocutors establish common ground [3]. Here, we focused specifically on facial expressions that would typically be viewed in the workplace: approval and disapproval.
• Participants had to place item into box marked ‘Like’ or ‘Dislike’ based on interpretation of expression.
Robot Expressive Behaviour
• For adults with ASD, robots could offer a unique opportunity to improve social signal recognition through role play and rehearsal of work-based scenarios. So, designing expressive robots to subtly elicit life-like expressions, and when added to a role-play context, model workplace scenarios for training purposes.
Experiment Design
Results
Data Exploration
β SE β z-value (Intercept) 0.94 0.29 3.19** Upper lid raiser, jaw drop 0.87 0.48 1.80. Chin raise, head down 0.38 0.43 0.86 Eyes closed, head down 1.19 0.52 2.29* Signif.codes : ‘.0 p < 0.1. ∗p < 0.05. ∗∗p < 0.01. ∗∗∗p < 0.001.
• Tested participant’s recognition (N = 57) of four EMYS head expressions (two approval, two disapproval). • Participant’s offered RFID tagged food items to a full-bodied FLASH robot. • RFID reader attached to robot’s hand initiated expression (order counterbalanced).
• Participant’s score on the AQ was used as a continuous and categorical (Low AQ, High AQ) predictor of recognition accuracy and response time (RT).
• Alyx then produced an expression and the participant had to place the food item in the respective box. • To finish, participants completed the Autism-spectrum Quotient [1].
• Binary logistic regression analysis found an effect of expression on accuracy but not AQ or RT.
References [1] Simon Baron-Cohen, Sally Wheelwright, Richard Skinner, Joanne Martin, and Emma Clubley. The Autism-Spectrum Quotient (AQ): Evidence from Asperger Syndrome/HighFunctioning Autism, Males and Females, Scientists and Mathematicians. Journal of Autism and Developmental Disorders, 31(1):5–17, 2001. [2] National Autisitic Society. The autism employment gap: Too Much Information in the workplace. Technical report, National Autistic Society, 2016.
Acknowledgements The presented work was funded by the EPSRC grant EP/N034546/1 and conducted at the Heriot-Watt University / RoboticsLab.
[3] Alessandro Vinciarelli, Maja Pantic, and Hervé Bourlard. Social signal processing: Survey of an emerging domain. Image and Vision Computing, 27(12):1743–1759, 2009. [4] John D. Westbrook, Chad Nye, Carlton J. Fong, Judith T. Wan, Tara Cortopassi, and Frank H. Martin. Adult Employment Assistance Services for Persons with Autism Spectrum Disorders: Effects on Employment Outcomes. Mathematica Policy Research Reports, 2012.
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