Social and Emotional Skills Training with Embodied Moxie

We present a therapeutic framework, namely STAR Framework, that leverages established and evidence-based therapeutic strategies delivered by the Embodied Moxie, an animate companion to support children with mental behavioral developmental disorders (MBDDs). This therapeutic framework jointly with Moxie aims to provide an engaging, safe, and secure environment for children aged five to ten years old. Moxie delivers content informed by therapeutic strategies including but not limited to naturalistic Applied Behavior Analysis, graded cueing, and Cognitive Behavior Therapy. Leveraging multimodal input from a camera and microphones, Moxie is uniquely positioned to be a first-hand witness of a child's progress and struggles alike. Moxie measures skills captured in state-of-the-art assessment scales, such as the Social Responsiveness Scale and Social Skill Improvement Scale, and augments those measures with quantitatively measured behavior skills, such as eye contact and language skills. While preliminary, the present study (N=12) also provides evidence that a six-week intervention using the STAR Framework and Moxie had significant impact on the children's abilities. We present our research in detail and provide an overview of the STAR Framework and all related components, such as Moxie and the companion app for parents.

Modeling Engagement in Long-Term, In-Home 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, human-robot 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 long-term, in-home 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 user's 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 re-engagement actions at appropriate times. These results validate the feasibility and challenges of recognition and response to user disengagement in long-term, real-world HRI settings. The contributions of this work also inform the design of engaging and personalized HRI, especially for the ASD community.

Designing a Socially Assistive Robot for Long-Term In-Home 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 short-term 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 long-term, in-home use with children with ASD. We address design decisions based on robustness and adaptability needs, discuss the development of the robot's character and interactions, and provide insights from the month-long, in-home 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.

Long-Term Personalization of an In-Home Socially Assistive Robot for Children With Autism Spectrum Disorders

Socially assistive robots (SAR) have shown great potential to augment the social and educational development of children with autism spectrum disorders (ASD). As SAR continues to substantiate itself as an effective enhancement to human intervention, researchers have sought to study its longitudinal impacts in real-world environments, including the home. Computational personalization stands out as a central computational challenge as it is necessary to enable SAR systems to adapt to each child's unique and changing needs. Toward that end, we formalized personalization as a hierarchical human robot learning framework (hHRL) consisting of five controllers (disclosure, promise, instruction, feedback, and inquiry) mediated by a meta-controller that utilized reinforcement learning to personalize instruction challenge levels and robot feedback based on each user's unique learning patterns. We instantiated and evaluated the approach in a study with 17 children with ASD, aged 3 to 7 years old, over month-long interventions in their homes. Our findings demonstrate that the fully autonomous SAR system was able to personalize its instruction and feedback over time to each child's proficiency. As a result, every child participant showed improvements in targeted skills and long-term retention of intervention content. Moreover, all child users were engaged for a majority of the intervention, and their families reported the SAR system to be useful and adaptable. In summary, our results show that autonomous, personalized SAR interventions are both feasible and effective in providing long-term in-home developmental support for children with diverse learning needs.