Advancing a taxonomy of proxemics for socially aware robot navigation.

Socially aware robot navigation requires robots to move among people in ways that respect human social norms, comfort, and perceived safety. Proxemics, the regulation of interpersonal space, plays a central role in this process. Applied HRI work often relies on simplified, static representations of personal space, overlooking the dynamic, asymmetric, and context-dependent nature of proxemic behavior observed in real-world interactions. The literature reflects a clear progression from simplified, concentric representations of proxemics toward increasingly context-sensitive and interaction-dependent models. This evolution indicates a growing consensus that interpersonal comfort cannot be adequately captured by a single, universal geometric shape. Instead, proxemic representations vary as a function of interaction context, task demands, cultural norms, and environmental constraints. To build on this evolution, we propose a comprehensive taxonomy of proxemics for socially aware robot navigation addressing gaps in the literature. Grounded in an extensive review of proxemics-related HRI studies published between 2020 and 2025, the taxonomy was developed through a hybrid methodology that integrates a top-down analysis of established HRI taxonomies and an AI exploratory approach with a bottom-up extraction of variables from 39 empirical studies. The resulting taxonomy systematically organizes proxemic dimensions into four interrelated clusters: Human, Robot, Environment, and Context. Together, these clusters capture the key variables shaping proxemic form (shape geometry and the scale of the personal zone boundary) and dynamics, including human activity and posture, robot design and behavior, environmental structure, task context, and the dynamic spatial properties of proxemics as captured by their metrics (the proxemics output variables). The proposed structured taxonomy of proxemics will inform the design of socially adaptive robot navigation systems and provide a foundation for future empirical research. Our analyses reveal significant gaps in current research practices, including limited consideration of interactions among multiple variables, overreliance on static laboratory settings, and insufficient integration of contextual and human-centered variables. To address these limitations, we propose future directions.