From Pets to Robots: MojiKit as a Data-Informed Toolkit for Affective HRI Design

TL;DR

This paper introduces MojiKit, a comprehensive toolkit for designing pet-inspired affective behaviors in social robots, combining structured reference materials, a prototype, and a behavior studio, validated through user workshops.

Contribution

It presents a novel, data-informed resource and toolkit that systematically guides pet-inspired affective HRI design, enhancing creativity and reducing technical barriers.

Findings

MojiKit enabled design of 35 new affective interaction patterns.

The toolkit lowered technical barriers and increased creative agency.

Participants created more diverse behaviors beyond their pet experiences.

Abstract

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.