  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.
