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.

翻译：为动物仿生社交机器人设计情感行为通常依赖于直觉和个人经验，导致设计成果零散。为提供更系统化的指导，我们首先对人类与宠物互动视频进行编码分析，通过文献研究和访谈验证洞察，并创建了结构化参考卡片，用以映射宠物启发的交互设计空间。在此基础上，我们开发了MojiKit工具包，该工具包整合了参考卡片、仿生机器人原型（MomoBot）以及行为控制工作室。我们通过18名参与者参与的共创工作坊对MojiKit进行评估，发现该工具包帮助参与者设计了35种超越其个人养宠经验的情感交互模式，同时无代码工作室降低了技术门槛并增强了创作自主性。我们的贡献包括：为宠物启发的交互设计提供基于数据的结构化资源、连接参考资料与动手实践的集成工具包，以及证明MojiKit如何帮助用户系统化创建更丰富多元的机器人情感行为的实证证据。