Despite significant technological advancements, the process of programming robots for adaptive assembly remains labor-intensive, demanding expertise in multiple domains and often resulting in task-specific, inflexible code. This work explores the potential of Large Language Models (LLMs), like ChatGPT, to automate this process, leveraging their ability to understand natural language instructions, generalize examples to new tasks, and write code. In this paper, we suggest how these abilities can be harnessed and applied to real-world challenges in the manufacturing industry. We present a novel system that uses ChatGPT to automate the process of programming robots for adaptive assembly by decomposing complex tasks into simpler subtasks, generating robot control code, executing the code in a simulated workcell, and debugging syntax and control errors, such as collisions. We outline the architecture of this system and strategies for task decomposition and code generation. Finally, we demonstrate how our system can autonomously program robots for various assembly tasks in a real-world project.

翻译：尽管技术取得了显著进步，为自适应装配编程机器人的过程仍然劳动密集，需要多领域专业知识，且往往产生特定任务的僵化代码。本研究探索了大型语言模型（如ChatGPT）在自动化这一过程中的潜力，利用其理解自然语言指令、将示例泛化到新任务以及编写代码的能力。本文提出如何利用这些能力并将其应用于制造业中的实际挑战。我们提出了一种新颖的系统，该系统使用ChatGPT通过将复杂任务分解为更简单的子任务、生成机器人控制代码、在模拟工作单元中执行代码以及调试语法和控制错误（如碰撞）来自动化编程机器人进行自适应装配的过程。我们概述了该系统的架构以及任务分解和代码生成的策略。最后，我们展示了该系统如何在真实项目中自主编程机器人完成各种装配任务。