In this paper, we propose a SOCratic model for Robots Approaching humans based on TExt System (SOCRATES) focusing on the human search and approach based on free-form textual description; the robot first searches for the target user, then the robot proceeds to approach in a human-friendly manner. In particular, textual descriptions are composed of appearance (e.g., wearing white shirts with black hair) and location clues (e.g., is a student who works with robots). We initially present a Human Search Socratic Model that connects large pre-trained models in the language domain to solve the downstream task, which is searching for the target person based on textual descriptions. Then, we propose a hybrid learning-based framework for generating target-cordial robotic motion to approach a person, consisting of a learning-from-demonstration module and a knowledge distillation module. We validate the proposed searching module via simulation using a virtual mobile robot as well as through real-world experiments involving participants and the Boston Dynamics Spot robot. Furthermore, we analyze the properties of the proposed approaching framework with human participants based on the Robotic Social Attributes Scale (RoSAS)

翻译：本文提出一种基于文本系统的机器人接近人类的苏格拉底模型（SOCRATES），聚焦于基于自由形式文本描述的人类搜索与接近任务：机器人首先搜索目标用户，随后以人类友好的方式接近。具体而言，文本描述包含外貌线索（如“穿白衬衫、黑头发”）和位置线索（如“是与机器人合作的学生”）。我们首先提出一种人类搜索苏格拉底模型，该模型连接语言领域的大型预训练模型以解决下游任务——基于文本描述搜索目标人物。随后，我们提出一种混合学习框架用于生成目标友好的机器人运动以接近人类，该框架由示范学习模块和知识蒸馏模块组成。通过虚拟移动机器人仿真实验，以及涉及参与者和波士顿动力Spot机器人的真实场景实验，我们验证了所提搜索模块的有效性。此外，基于机器人社会属性量表（RoSAS），我们分析了所提接近框架在人类参与者中的特性。