Large Language Models (LLMs) have demonstrated impressive performance across various domains, including code generation and problem solving. However, their application in robotic control, particularly in low-level tasks that require precise manipulation, real-time feedback, and environment-dependent execution, remains limited. To address this challenge, we propose the Closed-Loop Modular Code Synthesizer framework. This framework leverages a pre-trained LLM without any task-specific fine-tuning to perform modular code planning and generation, and iteratively executes the generated code while inserting debugging probes to observe its behavior. This closed-loop structure facilitates systematic debugging and refinement, ultimately producing executable control programs. We apply the proposed framework to the calibration of an RGB-D camera and a robotic arm, validating its effectiveness in real-world settings. Furthermore, through a subsequent pick-and-place task, we demonstrate not only the accuracy of the calibration but also the potential extensibility of the framework. Across both tasks, the framework achieved high execution accuracy and autonomy, illustrating the practicality and scalability of LLM-based robotic control using our framework.

翻译：摘要：大语言模型（LLMs）在代码生成与问题解决等多个领域展现出卓越性能。然而，其在机器人控制领域的应用——尤其需要精确操控、实时反馈及环境依赖执行的底层任务——仍十分有限。为应对这一挑战，我们提出闭环模块化代码合成框架。该框架利用预训练的LLM，无需针对特定任务进行微调，即可执行模块化代码规划与生成，并通过迭代执行生成的代码、插入调试探针观测其行为。这种闭环结构促进了系统性调试与优化，最终生成可执行的控制程序。我们将所提框架应用于RGB-D相机与机械臂的标定，验证了其在真实场景中的有效性。进一步通过后续的抓取放置任务，不仅证实了标定精度，还展示了框架的可扩展性。在两项任务中，该框架均实现了高执行精度与自主性，体现了基于LLM的机器人控制的实际效用与扩展潜力。