Designing Culturally Adaptive Emotional Gestures to Enhance Child-Robot Interaction with NAO Robots in ASD Therapy

Abstract

Integrating social robots into human-robot interactions demands advancements in natural language processing, navigation, computer vision, and expressive gestures to foster meaningful interactions. However, a gap remains in designing culturally relevant and developmentally appropriate gestures, particularly in the Sri Lankan context. Autism Spectrum Disorder (ASD), a neurodevelopmental condition impacting early education, often remains underdiagnosed, exacerbating learning challenges. This study introduces a novel approach utilizing robot-child interactions for ASD screening to minimize such delays. Expressive gestures were developed for the NAO6 humanoid robot to engage Sinhala-speaking children aged 2 to 6 years, including those with ASD, in Sri Lanka. Using the NAOqi Python API and Choregraphe simulator, culturally aligned gestures expressing emotions like happiness, sadness, fear, anger, and more were designed and synchronized with voice and LED effects. Pilot studies with typical children demonstrated the significance of linguistic and cultural alignment in enhancing engagement, emotional response, and trust. By addressing cultural nuances and advancing early ASD screening, this framework holds potential for broader applications in education, therapy, and diagnosis, improving human-robot interactions globally.