大規模言語モデルを活用した人間とロボットのチームにおける可変的自律性の実現
Exploring Large Language Models to Facilitate Variable Autonomy for Human-Robot Teaming
GPTを搭載した複数ロボットとのVR環境を構築し、自然言語による対話で可変的自律性を実現する枠組みを提案。ユーザー研究から、言語能力を探求したユーザーほど自然なコミュニケーションの恩恵を受けることを示した。
著者: Younes Lakhnati, Max Pascher, Jens Gerken
分類: cs.HC, cs.AI, cs.RO
原文アブストラクト
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.
