arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2312.07214cs.HCcs.AIcs.RO
收藏

探索大语言模型以促进人机团队协作中的可变自主性

Exploring Large Language Models to Facilitate Variable Autonomy for Human-Robot Teaming

TU Dortmund University(多特蒙德工业大学);University of Duisburg-Essen(杜伊斯堡-埃森大学)

Younes Lakhnati, Max Pascher, Jens Gerken

AI总结:

本文提出一种基于Unity VR和GPT驱动的多机器人框架,通过OpenAI函数调用实现自然语言交互以支持可变自主性,12人用户研究揭示了用户对话策略及对类人交流的获益。

摘要:

在快速演进的数字环境中,自主工具和机器人正变得日益普及。认识到这一发展的重要性,本文探讨了将诸如生成式预训练转换器(GPT)等大语言模型(LLMs)集成到人机团队协作环境中,通过人类与机器人之间的口头交流来促进可变自主性。本文基于Unity虚拟现实(VR)环境,为这种由GPT驱动的多机器人测试平台环境引入了一种新颖的框架。该系统允许用户通过自然语言与机器人智能体交互,每个智能体均由独立的GPT核心驱动。借助OpenAI的函数调用,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,探讨了用户在多机器人环境中获得自然语言对话机会时所采用的策略。研究结果表明,用户对如何与机器人对话可能存在预设期望,很少尝试探索其机器人协作者的实际语言和认知能力。尽管如此,那些确实进行了探索的用户能够从更加自然的交流流程和类人的来回对话中获益。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。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑