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This paper introduces MojiKit, a toolkit designed to facilitate the creation of affective behaviors in animal-inspired social robots by grounding the design process in data from human-pet interactions. The toolkit includes reference cards derived from video analysis, a zoomorphic robot prototype (MomoBot), and a behavior control studio. Evaluation via co-creation workshops demonstrated that MojiKit enabled participants to design a broader range of affective interaction patterns and lowered technical barriers to entry.
Ditch the guesswork in affective robot design: MojiKit empowers creators to move beyond personal pet experiences by providing a data-informed toolkit for systematically designing richer, more diverse robot behaviors.
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.
