为有特殊需求的用户设计可访问的机器人解释框架，提升人机协同理解。
Accessible and Pedagogically-Grounded Explainability for Human-Robot Interaction: A Framework Based on UDL and Symbolic Interfaces
- 融合UDL与符号化交互，构建多模态解释前端。
- 支持实时双向互动，实现人机理解共演。
- 适合教育、辅助机器人等包容性场景使用。
本文提出一种面向认知、沟通或学习能力各异用户的可访问且具有教学基础的机器人可解释性框架，以支持人-机器人交互（HRI）。该框架结合通用学习设计（UDL）与通用设计（UD）原则，融合符号化通信策略，促进人类与机器人之间心智模型的对齐。采用Asterics Grid与ARASAAC图标作为多模态、可解释的前端界面，并通过轻量级HTTP-to-ROS 2桥接实现实时交互与解释触发。强调可解释性并非单向传递，而是人机理解共同演进的双向过程。在教育或辅助场景中，人类中介者（如教师）可能对共享理解至关重要。通过多模态解释板示例验证框架有效性，并探讨其在教育、辅助机器人及包容性AI中的扩展潜力。
原文摘要 · Abstract (English)
This paper presents a novel framework for accessible and pedagogically-grounded robot explainability, designed to support human-robot interaction (HRI) with users who have diverse cognitive, communicative, or learning needs. We combine principles from Universal Design for Learning (UDL) and Universal Design (UD) with symbolic communication strategies to facilitate the alignment of mental models between humans and robots. Our approach employs Asterics Grid and ARASAAC pictograms as a multimodal, interpretable front-end, integrated with a lightweight HTTP-to-ROS 2 bridge that enables real-time interaction and explanation triggering. We emphasize that explainability is not a one-way function but a bidirectional process, where human understanding and robot transparency must co-evolve. We further argue that in educational or assistive contexts, the role of a human mediator (e.g., a teacher) may be essential to support shared understanding. We validate our framework with examples of multimodal explanation boards and discuss how it can be extended to different scenarios in education, assistive robotics, and inclusive AI.
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