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：本文提出了一种基于文本系统的苏格拉底式机器人接近人类模型（SOCRATES），重点研究基于自由形式文本描述的人类搜索与接近方法：机器人首先搜索目标用户，随后以友好方式接近目标。具体而言，文本描述由外观特征（如“穿着白衬衫、黑头发”）和位置线索（如“是研究机器人的学生”）组成。我们首先提出一种人类搜索苏格拉底模型，该模型通过连接语言域中的大型预训练模型来解决基于文本描述搜索目标人物的下游任务。随后，我们提出一种混合学习框架，用于生成面向目标、亲切友好的机器人运动以接近人类，该框架包含示范学习模块和知识蒸馏模块。我们通过虚拟移动机器人仿真实验以及涉及真实参与者与波士顿动力Spot机器人的真实场景实验验证了所提出的搜索模块。此外，我们基于机器人社会属性量表（RoSAS）分析了所提出接近框架在人类参与者实验中的特性。