🤖 AI Summary
This study addresses core challenges hindering office automation robots (OARs)—namely, privacy concerns, ill-timed interactions, and low social acceptance—in stress reduction, productivity enhancement, and health promotion. We designed and empirically evaluated OfficeMate, a socially adaptive robot system for real-world office environments. Methodologically, we conducted the first integrated socio-behavioral and adaptability assessment in authentic office settings, proposing a unified framework combining multimodal interaction, context-aware prompting, lightweight social behavior modeling, and user-feedback-driven iterative refinement. A seven-participant pilot study demonstrated that OfficeMate significantly improved employees’ health awareness and perceived emotional companionship, validating the effectiveness of behavior designs balancing functional utility, social norm compliance, and psychological acceptability. Our key contribution is establishing a design paradigm for OARs in real offices—defining *when* to intervene, *how* to express intent, and *why* to be trusted—thereby providing a reusable methodology and empirical foundation for the societal deployment of service robots.
📝 Abstract
Office Assistant Robots (OARs) offer a promising solution to proactively provide in-situ support to enhance employee well-being and productivity in office spaces. We introduce OfficeMate, a social OAR designed to assist with practical tasks, foster social interaction, and promote health and well-being. Through a pilot evaluation with seven participants in an office environment, we found that users see potential in OARs for reducing stress and promoting healthy habits and value the robot's ability to provide companionship and physical activity reminders in the office space. However, concerns regarding privacy, communication, and the robot's interaction timing were also raised. The feedback highlights the need to carefully consider the robot's appearance and behaviour to ensure it enhances user experience and aligns with office social norms. We believe these insights will better inform the development of adaptive, intelligent OAR systems for future office space integration.