This research paper delves into the integration of OpenAI's ChatGPT into embodied agent systems, evaluating its influence on interactive decision-making benchmark. Drawing a parallel to the concept of people assuming roles according to their unique strengths, we introduce InterAct. In this approach, we feed ChatGPT with varied prompts, assigning it a numerous roles like a checker and a sorter, then integrating them with the original language model. Our research shows a remarkable success rate of 98% in AlfWorld, which consists of 6 different tasks in a simulated household environment, emphasizing the significance of proficient prompt engineering. The results highlight ChatGPT's competence in comprehending and performing intricate tasks effectively in real-world settings, thus paving the way for further advancements in task planning.

翻译：本研究论文深入探讨了OpenAI的ChatGPT在具身代理系统中的集成，评估了其对交互式决策基准的影响。借鉴人们根据自身独特优势承担角色的概念，我们引入了InterAct。在该方法中，我们向ChatGPT提供多样化的提示，赋予其诸如检查员和分类员等多种角色，并将其与原始语言模型整合。我们的研究表明，在由模拟家庭环境中6个不同任务组成的AlfWorld中，成功率高达98%，这突显了高效提示工程的重要性。结果强调了ChatGPT在现实环境中有效理解和执行复杂任务的能力，从而为任务规划领域的进一步进展铺平了道路。