用宠物互动数据指导机器人情感设计，降低创作门槛。
From Pets to Robots: MojiKit as a Data-Informed Toolkit for Affective HRI Design
- 基于真实宠物互动视频构建情感行为参考体系
- 18人协作中生成35种超越个人经验的情感交互模式
- 无需编程的可视化工具让非专业人士也能自由设计
为系统化设计拟宠物社交机器人的情感行为，研究者首先分析了人类与宠物互动的视频，结合文献与访谈验证洞察，构建了结构化的参考卡片，映射出拟宠物情感交互的设计空间。在此基础上，开发了包含参考卡片、类动物机器人原型MomoBot和行为控制工作室的MojiKit工具包。通过与18名参与者开展协同创作工作坊评估，发现该工具包帮助用户设计出35种超出自身养宠经验的情感交互模式，且无代码控制界面显著降低了技术门槛，增强了创作自主性。研究贡献包括：数据驱动的拟宠物情感人机交互设计资源、连接参考资料与实体原型的一体化工具包，以及实证表明其可系统性提升情感机器人行为的丰富性与多样性。
原文摘要 · Abstract (English)
Designing affective behaviors for animal-inspired social robots often relies on intuition and personal experience, leading to fragmented outcomes. To provide more systematic guidance, we first coded and analyzed human-pet interaction videos, validated insights through literature and interviews, and created structured reference cards that map the design space of pet-inspired affective interactions. Building on this, we developed MojiKit, a toolkit combining reference cards, a zoomorphic robot prototype (MomoBot), and a behavior control studio. We evaluated MojiKit in co-creation workshops with 18 participants, finding that MojiKit helped them design 35 affective interaction patterns beyond their own pet experiences, while the code-free studio lowered the technical barrier and enhanced creative agency. Our contributions include the data-informed structured resource for pet-inspired affective HRI design, an integrated toolkit that bridges reference materials with hands-on prototyping, and empirical evidence showing how MojiKit empowers users to systematically create richer, more diverse affective robot behaviors.
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