Clabaugh Caitlyn, Mahajan Kartik, Jain Shomik, Pakkar Roxanna, Becerra David, Shi Zhonghao, Deng Eric, Lee Rhianna, Ragusa Gisele, Matarić Maja
Interaction Lab, Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.
STEM Education Research Group, Division of Engineering Education, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.
Front Robot AI. 2019 Nov 6;6:110. doi: 10.3389/frobt.2019.00110. eCollection 2019.
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.
社交辅助机器人（SAR）已展现出巨大潜力，可促进自闭症谱系障碍（ASD）儿童的社交和教育发展。随着SAR不断证明自身是对人类干预的有效增强手段，研究人员已着手研究其在包括家庭在内的现实环境中的长期影响。计算个性化成为一项核心计算挑战，因为有必要使SAR系统能够适应每个孩子独特且不断变化的需求。为此，我们将个性化形式化为一个分层人机学习框架（hHRL），它由五个控制器（披露、承诺、指令、反馈和询问）组成，由一个元控制器进行协调，该元控制器利用强化学习根据每个用户独特的学习模式来个性化指令挑战级别和机器人反馈。我们在一项针对17名3至7岁ASD儿童的研究中实例化并评估了该方法，这些儿童在其家中接受了为期一个月的干预。我们的研究结果表明，完全自主的SAR系统能够随着时间推移根据每个孩子的熟练程度个性化其指令和反馈。结果，每个儿童参与者在目标技能方面都有提高，并且对干预内容有长期记忆。此外，所有儿童用户在大部分干预过程中都积极参与，他们的家庭报告称SAR系统有用且适应性强。总之，我们的结果表明，自主、个性化的SAR干预在为有不同学习需求的儿童提供长期家庭发展支持方面既可行又有效。
