Abstract
Society is starting to come up with exciting applications for social robots like butlers, coaches, and waiters. However, these robots face a challenging task: to meet people during a first encounter. This survey explores the literature that contributes to this task. We define a taxonomy based on psychology and sociology models: Kendon's greeting model and Greenspan's model of social competence. We use Kendon's model as a framework to compare and analyze works that describe robotic systems that engage with people. To categorize individual skills, we use three components of Social Awareness that belong to Greenspan's model: Social Sensitivity, Social Insight, and Communication. Under each section, we highlight some research gaps and propose research directions to address them. Through our analysis, we suggest significant research directions for enhanced first encounters. First, social scripts need to be evaluated under equal conditions. Second, interaction management and tracking for first encounters should consider state and observation uncertainties. Third, perception methods need lighter and robust integration in mobile platforms. Fourth, methods to explicitly define social norms are still scarce. Finally, research on social feedback and interaction recovery may fill the gaps of imperfect first encounters.