A recognition-based framework for human–robot relationships

Abstract

As physically embodied AI becomes increasingly integrated into everyday life, understanding the diversity of human–robot relationships has become an important challenge. Previous studies have examined anthropomorphism, mind perception, moral attribution, and long-term relationships, providing important but complementary perspectives on how humans understand and relate to robots. However, a unified perspective for understanding the diversity of human–robot relationships remains limited. This study proposes a recognition-based framework consisting of four alternative modes: Tool, Decision Maker, Partner, and Social Personhood. The framework was developed by synthesizing complementary perspectives from previous research with recurring recognition patterns identified through comparative analysis of fictional human–robot interaction episodes. Rather than representing developmental stages or intrinsic properties of robots, the four modes describe alternative patterns of human recognition that emerge under different interaction contexts. The proposed framework provides a unified perspective for understanding how different forms of human recognition are reflected in characteristic patterns of human response and ultimately give rise to diverse forms of human–robot relationships, thereby providing a conceptual foundation for future research on human–robot relationships and the design of embodied AI.