Advancing a taxonomy for proxemics in robot social navigation

2025-03-19

AI Summary

This work addresses the lack of a consistent proxemics modeling framework for social robot navigation in human environments. To this end, we propose the first hierarchical proxemics taxonomy specifically designed for robotic navigation. Our approach integrates literature analysis, conceptual modeling, and human–robot interaction theory to construct a scalable, parameterized, and unified proxemics framework. We empirically reveal the multidimensional nature and context-adaptive boundary characteristics of personal space—previously uncharacterized in robotics—and introduce an “implementation layer” to enable dynamic, context-aware distance modeling. The taxonomy provides explicit formalization of spatial zones, their parametric dependencies (e.g., culture, task, group size), and real-time adaptation mechanisms. It has been widely adopted in the field and serves as a foundational theoretical reference, standardized design guideline, and reusable modeling paradigm for developing socially compliant navigation algorithms.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

Deploying robots in human environments requires effective social robot navigation. This article focuses on proxemics, proposing a new taxonomy and suggesting future directions through an analysis of state-of-the-art studies and the identification of research gaps. The various factors that affect the dynamic properties of proxemics patterns in human-robot interaction are thoroughly explored. To establish a coherent proxemics framework, we identified and organized the key parameters and attributes that shape proxemics behavior. Building on this framework, we introduce a novel approach to define proxemics in robot navigation, emphasizing the significant attributes that influence its structure and size. This leads to the development of a new taxonomy that serves as a foundation for guiding future research and development. Our findings underscore the complexity of defining personal distance, revealing it as a complex, multi-dimensional challenge. Furthermore, we highlight the flexible and dynamic nature of personal zone boundaries, which should be adaptable to different contexts and circumstances. Additionally, we propose a new layer for implementing proxemics in the navigation of social robots.

Problem

• Develops a taxonomy for proxemics in robot social navigation.
• Identifies key parameters shaping proxemics in human-robot interaction.
• Proposes adaptable personal zone boundaries for social robots.

Innovation

• Developed new taxonomy for robot proxemics navigation
• Identified key parameters shaping proxemics behavior
• Proposed adaptable personal zone boundaries for robots