提出可统一分类各类机器人外观的元建模框架
MetaMorph -- A Metamodelling Approach For Robot Morphology
- 基于222个机器人的元建模方法，构建统一外观分类体系
- 实现不同机器人间视觉特征的结构化对比与距离度量
- 适合人机交互、机器人设计等领域的研究人员使用
机器人外观对人机交互（HRI）有关键影响，但通常仅用类人、仿生或技术型等宽泛类别描述。现有精确方法多聚焦类人特征，无法涵盖所有类型，限制了设计与交互效果之间有意义关联的建立。为此，我们提出MetaMorph，一个全面的机器人形态分类框架。该框架基于222个来自IEEE机器人指南的机器人数据，采用元建模方法，提供一种结构化方式来比较视觉特征。该模型使研究者能够评估不同机器人模型间的视觉距离，并探索适配特定任务与场景的最优设计特征。
原文摘要 · Abstract (English)
Robot appearance crucially shapes Human-Robot Interaction (HRI) but is typically described via broad categories like anthropomorphic, zoomorphic, or technical. More precise approaches focus almost exclusively on anthropomorphic features, which fail to classify robots across all types, limiting the ability to draw meaningful connections between robot design and its effect on interaction. In response, we present MetaMorph, a comprehensive framework for classifying robot morphology. Using a metamodeling approach, MetaMorph was synthesized from 222 robots in the IEEE Robots Guide, offering a structured method for comparing visual features. This model allows researchers to assess the visual distances between robot models and explore optimal design traits tailored to different tasks and contexts.
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