This paper presents an experimental study regarding the use of OpenAI's ChatGPT for robotics applications. We outline a strategy that combines design principles for prompt engineering and the creation of a high-level function library which allows ChatGPT to adapt to different robotics tasks, simulators, and form factors. We focus our evaluations on the effectiveness of different prompt engineering techniques and dialog strategies towards the execution of various types of robotics tasks. We explore ChatGPT's ability to use free-form dialog, parse XML tags, and to synthesize code, in addition to the use of task-specific prompting functions and closed-loop reasoning through dialogues. Our study encompasses a range of tasks within the robotics domain, from basic logical, geometrical, and mathematical reasoning all the way to complex domains such as aerial navigation, manipulation, and embodied agents. We show that ChatGPT can be effective at solving several of such tasks, while allowing users to interact with it primarily via natural language instructions. In addition to these studies, we introduce an open-sourced research tool called PromptCraft, which contains a platform where researchers can collaboratively upload and vote on examples of good prompting schemes for robotics applications, as well as a sample robotics simulator with ChatGPT integration, making it easier for users to get started with using ChatGPT for robotics.

翻译：本文报告了一项关于将OpenAI的ChatGPT应用于机器人学任务的实验研究。我们提出了一种结合提示工程设计原则与高级函数库构建的策略，使ChatGPT能够适应不同的机器人任务、仿真平台及硬件形态。我们的评估聚焦于不同提示工程技术及对话策略对各类机器人任务执行效果的影响。我们探讨了ChatGPT在自由对话、XML标签解析、代码合成方面的能力，同时研究了任务专用提示函数以及基于对话的闭环推理机制。研究涵盖机器人学领域的多种任务，从基础逻辑推理、几何推理和数学推理，直至空中导航、机械臂操控及具身智能体等复杂领域。实验表明，ChatGPT能有效解决其中多类任务，且允许用户主要通过自然语言指令与其交互。除上述研究外，我们还介绍了一款名为PromptCraft的开源研究工具：该工具包含一个供研究者协作上传并投票评选机器人学优质提示方案示例的平台，同时集成了ChatGPT的机器人仿真样例环境，便于用户快速入门使用ChatGPT进行机器人学研究。