From Pets to Robots: MojiKit as a Data-Informed Toolkit for Affective HRI Design
This work addresses the challenge of fragmented design outcomes in affective human-robot interaction for designers and developers, offering a systematic, data-informed approach.
The researchers tackled the problem of designing affective behaviors for animal-inspired social robots by developing MojiKit, a toolkit that includes reference cards, a robot prototype, and a behavior control studio, which helped participants design 35 affective interaction patterns and lowered technical barriers in workshops.
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.