In a rapidly evolving digital landscape autonomous tools and robots are becoming commonplace. Recognizing the significance of this development, this paper explores the integration of Large Language Models (LLMs) like Generative pre-trained transformer (GPT) into human-robot teaming environments to facilitate variable autonomy through the means of verbal human-robot communication. In this paper, we introduce a novel framework for such a GPT-powered multi-robot testbed environment, based on a Unity Virtual Reality (VR) setting. This system allows users to interact with robot agents through natural language, each powered by individual GPT cores. By means of OpenAI's function calling, we bridge the gap between unstructured natural language input and structure robot actions. A user study with 12 participants explores the effectiveness of GPT-4 and, more importantly, user strategies when being given the opportunity to converse in natural language within a multi-robot environment. Our findings suggest that users may have preconceived expectations on how to converse with robots and seldom try to explore the actual language and cognitive capabilities of their robot collaborators. Still, those users who did explore where able to benefit from a much more natural flow of communication and human-like back-and-forth. We provide a set of lessons learned for future research and technical implementations of similar systems.

翻译：在快速演变的数字环境中，自主工具和机器人正日益普及。认识到这一发展的重要性，本文探讨了将生成式预训练变换器（GPT）等大型语言模型（LLMs）集成到人机协作环境中，通过言语人机通信实现可变自主性。本文介绍了一种基于Unity虚拟现实（VR）环境的新型框架，用于此类由GPT驱动的多机器人测试平台。该系统允许用户通过自然语言与机器人代理交互，每个代理均由独立的GPT核心驱动。借助OpenAI的函数调用功能，我们弥合了非结构化自然语言输入与结构化机器人动作之间的鸿沟。一项包含12名参与者的用户研究探讨了GPT-4的有效性，更重要的是，研究了用户在多机器人环境中获得自然语言对话机会时所采取的策略。我们的研究结果表明，用户可能对如何与机器人对话存在先入为主的期望，很少尝试探索机器人协作者的实际语言和认知能力。尽管如此，那些进行探索的用户能够受益于更自然的沟通流程和类人化的双向交互。我们为未来类似系统的研究和技术实现提供了一系列经验教训。