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.

通过将 OpenAI 的 ChatGPT 与具身智能系统相结合，本研究评估了它对交互式决策基准的影响。我们引入了 InterAct 的概念，将 ChatGPT 赋予多个角色，如检查员和分类员，并与原始语言模型进行整合。我们的研究在 AlfWorld 中展示了惊人的 98% 成功率，该环境模拟了一个家庭环境中的 6 个不同任务，强调了有效的提示工程的重要性。结果突显了 ChatGPT 在理解和高效完成复杂任务方面的能力，为任务规划的进一步发展铺平了道路。