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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805891/ https://www.ncbi.nlm.nih.gov/pubmed/33501125 http://dx.doi.org/10.3389/frobt.2019.00110 |
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author | Clabaugh, Caitlyn Mahajan, Kartik Jain, Shomik Pakkar, Roxanna Becerra, David Shi, Zhonghao Deng, Eric Lee, Rhianna Ragusa, Gisele Matarić, Maja |
author_facet | Clabaugh, Caitlyn Mahajan, Kartik Jain, Shomik Pakkar, Roxanna Becerra, David Shi, Zhonghao Deng, Eric Lee, Rhianna Ragusa, Gisele Matarić, Maja |
author_sort | Clabaugh, Caitlyn |
collection | PubMed |
description | 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 3–7 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. |
format | Online Article Text |
id | pubmed-7805891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78058912021-01-25 Long-Term Personalization of an In-Home Socially Assistive Robot for Children With Autism Spectrum Disorders Clabaugh, Caitlyn Mahajan, Kartik Jain, Shomik Pakkar, Roxanna Becerra, David Shi, Zhonghao Deng, Eric Lee, Rhianna Ragusa, Gisele Matarić, Maja Front Robot AI Robotics and AI 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 3–7 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. Frontiers Media S.A. 2019-11-06 /pmc/articles/PMC7805891/ /pubmed/33501125 http://dx.doi.org/10.3389/frobt.2019.00110 Text en Copyright © 2019 Clabaugh, Mahajan, Jain, Pakkar, Becerra, Shi, Deng, Lee, Ragusa and Matarić. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Robotics and AI Clabaugh, Caitlyn Mahajan, Kartik Jain, Shomik Pakkar, Roxanna Becerra, David Shi, Zhonghao Deng, Eric Lee, Rhianna Ragusa, Gisele Matarić, Maja Long-Term Personalization of an In-Home Socially Assistive Robot for Children With Autism Spectrum Disorders |
title | Long-Term Personalization of an In-Home Socially Assistive Robot for Children With Autism Spectrum Disorders |
title_full | Long-Term Personalization of an In-Home Socially Assistive Robot for Children With Autism Spectrum Disorders |
title_fullStr | Long-Term Personalization of an In-Home Socially Assistive Robot for Children With Autism Spectrum Disorders |
title_full_unstemmed | Long-Term Personalization of an In-Home Socially Assistive Robot for Children With Autism Spectrum Disorders |
title_short | Long-Term Personalization of an In-Home Socially Assistive Robot for Children With Autism Spectrum Disorders |
title_sort | long-term personalization of an in-home socially assistive robot for children with autism spectrum disorders |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805891/ https://www.ncbi.nlm.nih.gov/pubmed/33501125 http://dx.doi.org/10.3389/frobt.2019.00110 |
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