面向孤独症儿童机器人辅助学习的深度—模糊个性化框架

摘要

关于机器人在孤独症治疗中成效的研究，此前主要依赖“绿野仙踪”（Wizard-of-Oz）方法，即由机器人执行预先设定的行为，但该方法高度依赖人工干预，因而受到限制。为突破这一局限，本文提出一种面向社交机器人的新型深度—模糊个性化框架，以增强其与孤独症儿童交互时的适应性。该框架包含一个平均精度均值达93%的深度学习模型——单次检测式情绪检测器（SED），以及一个基于模糊逻辑的参与度预测引擎；后者利用得分、智商水平与任务复杂度等因素，估计孤独症儿童在与机器人交互过程中的参与度。本文在人形机器人RoCA上实现该框架，评估这一个性化方法对加纳孤独症儿童在人机交互中学习成效的影响。统计分析（曼—惠特尼检验，U=3.0，P=0.012）表明，RoCA采用深度—模糊方法后，学习增益获得显著提升。

Abstract

Research exploring the efficacy of robots in autism therapy has predominantly relied on the Wizard-of-Oz method, where robots execute predetermined behaviours. However, this approach is constrained by its heavy reliance on human intervention. To address this limitation, we introduce a novel deep-fuzzy personalization framework for social robots to enhance adaptability in interactions with autistic children. This framework incorporates a deep learning model called singleshot emotion detector (SED) with a mean average precision of 93% and a fuzzy-based engagement prediction engine, utilizing factors such as scores, IQ levels, and task complexity to estimate the engagement of autistic children during robot interactions. Implemented on the humanoid robot RoCA, our study assesses the impact of this personalization approach on learning outcomes in interactions with Ghanaian autistic children. Statistical analysis, specifically Mann Whitney tests (U=3.0, P=0.012), demonstrates the significant improvement in learning gains associated with RoCA's adoption of the deep-fuzzy approach.