Understanding human comfort when in the presence of robots is vital to constructing socially adaptive robotic systems. This study introduces the Human–Robot Proxemic Index (HRPI). This quantitative model estimates user comfort based on three contextual dimensions: human activity (behavior-dependent, BD), interaction type (interaction-dependent, ID), and object characteristics (object-dependent, OD). Unlike previous proxemic models that focused solely on physical distance, HRPI integrates multidimensional contextual factors and applies sigmoid-based personalization to account for individual sensitivity. A ceiling-mounted service robot and nine participants took part in experiments. Pre- and post-interaction questionnaires were used to find out how comfortable the participants felt and what distance they preferred. The collected data were normalized and incorporated into HRPI through weighted assessment, and validation with ideal dummy data in trials showed that HRPI-based control dynamically adjusted the robot’s approach distance and speed according to user preferences. These findings highlight the strengths of HRPI as a multidimensional, context-aware framework for guiding socially appropriate robot movements and suggest that its integration with topological spatial mapping could further enhance human–robot collaboration in real-world environments.