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

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