The deployment of robots into human scenarios necessitates advanced planning strategies, particularly when we ask robots to operate in dynamic, unstructured environments. RoboCup offers the chance to deploy robots in one of those scenarios, a human-shaped game represented by a soccer match. In such scenarios, robots must operate using predefined behaviors that can fail in unpredictable conditions. This paper introduces a novel application of Large Language Models (LLMs) to address the challenge of generating actionable plans in such settings, specifically within the context of the RoboCup Standard Platform League (SPL) competitions where robots are required to autonomously execute soccer strategies that emerge from the interactions of individual agents. In particular, we propose a multi-role approach leveraging the capabilities of LLMs to generate and refine plans for a robotic soccer team. The potential of the proposed method is demonstrated through an experimental evaluation,carried out simulating multiple matches where robots with AI-generated plans play against robots running human-built code.

翻译：将机器人部署到人类场景需要先进的规划策略，尤其是在要求机器人于动态、非结构化环境中运行时。RoboCup提供了一个将机器人部署到此类场景的机会，即由足球比赛所代表的类人游戏。在此类场景中，机器人必须使用预定义的行为运行，而这些行为在不可预测的条件下可能会失效。本文介绍了一种大语言模型（LLMs）的新颖应用，旨在解决在此类环境中生成可执行策略的挑战，具体是在RoboCup标准平台联盟（SPL）竞赛的背景下，该竞赛要求机器人自主执行从个体智能体交互中涌现出的足球策略。我们特别提出了一种多角色方法，利用LLMs的能力为机器人足球队生成并优化策略。通过实验评估证明了所提方法的潜力，该评估模拟了多场比赛，其中运行AI生成策略的机器人与运行人工编写代码的机器人进行对抗。