Designing a Socially Assistive Robot for LongTerm InHome Use for Children with Autism Spectrum Disorders Socially assistive robotics (SAR) research has shown great potential for supplementing and augmenting therapy for children with autism spectrum disorders (ASD). However, the vast majority of SAR research has been limited to shortterm studies in highly controlled environments. The design and development of a SAR system capable of interacting autonomously it in situ for long periods of time involves many engineering and computing challenges. This paper presents the design of a fully autonomous SAR system for longterm, inhome use with children with ASD. We address design decisions based on robustness and adaptability needs, discuss the development of the robotu0027s character and interactions, and provide insights from the monthlong, inhome data collections with children with ASD. This work contributes to a larger research program that is exploring how SAR can be used for enhancing the social and cognitive development of children with ASD.

Modeling Engagement in LongTerm, InHome Socially Assistive Robot Interventions for Children with Autism Spectrum Disorders. Socially assistive robotics (SAR) has great potential to provide accessible, affordable, and personalized therapeutic interventions for children with autism spectrum disorders (ASD). However, humanrobot interaction (HRI) methods are still limited in their ability to autonomously recognize and respond to behavioral cues, especially in atypical users and everyday settings. This work applies supervised machine learning algorithms to model user engagement in the context of longterm, inhome SAR interventions for children with ASD. Specifically, two types of engagement models are presented for each user: 1) generalized models trained on data from different users; and 2) individualized models trained on an early subset of the useru0027s data. The models achieved approximately 90% accuracy (AUROC) for post hoc binary classification of engagement, despite the high variance in data observed across users, sessions, and engagement states. Moreover, temporal patterns in model predictions could be used to reliably initiate reengagement actions at appropriate times. These results validate the feasibility and challenges of recognition and response to user disengagement in longterm, realworld HRI settings. The contributions of this work also inform the design of engaging and personalized HRI, especially for the ASD community.