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.

社会开始为管家、教练和服务员等社交机器人提出令人兴奋的应用程序。然而，这些机器人面临着一项具有挑战性的任务：在初次见面时与人见面。本调查探讨了有助于这项任务的文献。我们根据心理学和社会学模型定义了一个分类法：Kendon 的问候模型和 Greenspan 的社会能力模型。我们使用 Kendon 的模型作为框架来比较和分析描述与人互动的机器人系统的作品。为了对个人技能进行分类，我们使用了属于格林斯潘模型的社会意识的三个组成部分：社会敏感性、社会洞察力和沟通。在每个部分下，我们强调了一些研究空白并提出了解决这些问题的研究方向。通过我们的分析，我们建议了重要的研究方向，以增强初次相遇。首先，社交脚本需要在同等条件下进行评估。其次，首次相遇的交互管理和跟踪应考虑状态和观察的不确定性。第三，感知方法需要在移动平台中更轻、更健壮的集成。第四，明确定义社会规范的方法仍然稀缺。最后，对社会反馈和互动恢复的研究可能会填补不完美的初次相遇的空白。明确定义社会规范的方法仍然很少。最后，对社会反馈和互动恢复的研究可能会填补不完美的初次相遇的空白。明确定义社会规范的方法仍然很少。最后，对社会反馈和互动恢复的研究可能会填补不完美的初次相遇的空白。