Effects of Interaction Proximity and Orientation on User Experience with AI Companion Robots in Information Delivery Contexts

Objective: This study investigates how an AI companion robot's interaction proximity and face-to-face behavior affect user experience in home environments.<br/>Background: AI companion robots have advanced to fulfill both functional and social roles, including the delivery of personalized information. These robots commonly operate in close proximity to users, utilizing visual and auditory modalities for communication. However, the unique characteristics of home and personal environments can substantially impact user experience and acceptance. A thorough understanding of user perceptions of robot behavior in such contexts is essential for the design of effective human–robot interactions.<br/>Method: A within-subjects experiment was conducted with 40 participants. Robot interaction was manipulated in a 2 × 2 design varying proximity (close, distant) and orientation (user-facing, wall-facing). After interacting with a robot that presented weather and schedule information, participants completed questionnaires measuring perception, acceptance, closeness, user experience, and intention to use. Follow-up interviews were conducted to collect qualitative feedback.<br/>Results: Face-to-face interactions at close proximity received the highest ratings for intimacy, likability, personification, and social presence. In contrast, distant, wall facing interactions were perceived as more professional and less distracting. The higher the age group, the more participants preferred human-like qualities such as eye contact, whereas younger participants valued efficiency and the robot's functional role. Comfort and intention to use were generally rated positively.<br/>Conclusion: The interaction of AI companion robots is effective in building intimacy at close range, but more suitable for delivering information and minimizing distraction at a distance.<br/>Application: These findings highlight the need to develop context-aware, user-tailored interaction strategies for social robots designed for home environments.