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

TL;DR

This study presents a hierarchical reinforcement learning framework for long-term personalization of socially assistive robots, demonstrating its effectiveness in improving developmental skills in children with autism spectrum disorders over month-long home interventions.

Contribution

The paper introduces a novel hierarchical human-robot learning framework that enables autonomous personalization of SAR systems for children with ASD in real-world home environments.

Findings

Children showed skill improvements and long-term retention.

The SAR system maintained engagement and was rated useful by families.

Personalization adapted to each child's learning patterns over time.

Abstract

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.