标题： 一种具有正确性保证的基于大语言模型的自然语言到机器人语言翻译框架 显示英文标题

标题： An LLM-powered Natural-to-Robotic Language Translation Framework with Correctness Guarantees

摘要： 大型语言模型（LLM）越来越多地被部署在机器人中，以生成针对特定用户任务的机器人控制程序，从而实现具身智能。 现有方法主要关注利用LLM从自然语言中的用户任务直接生成可执行程序的LLM训练和提示设计。 然而，由于LLM的不一致性以及任务的高复杂性，这种尽力而为的方法经常导致生成代码中出现大量的编程错误，这在轻量级LLM被应用时显著削弱了效果。 本文介绍了一种自然-机器人语言翻译框架，该框架（i）为生成的控制程序提供正确性验证，并（ii）通过基于反馈的微调提高LLM在程序生成中的性能。 为了实现这一点，提出了一种机器人技能语言（RSL），以抽象掉控制程序的复杂细节，将自然语言任务与底层机器人技能联系起来。 然后，构建了RSL编译器和调试器，用于验证LLM生成的RSL程序，并向LLM提供错误反馈，以便对输出进行细化，直到通过编译器验证。 这在将LLM生成的程序卸载到机器人执行之前提供了正确性保证，显著提高了LLM驱动的机器人应用的效果。 实验表明，NRTrans在多种LLM和任务下优于现有方法，并且在轻量级LLM上实现了高成功率。 显示英文摘要

摘要： The Large Language Models (LLM) are increasingly being deployed in robotics to generate robot control programs for specific user tasks, enabling embodied intelligence. Existing methods primarily focus on LLM training and prompt design that utilize LLMs to generate executable programs directly from user tasks in natural language. However, due to the inconsistency of the LLMs and the high complexity of the tasks, such best-effort approaches often lead to tremendous programming errors in the generated code, which significantly undermines the effectiveness especially when the light-weight LLMs are applied. This paper introduces a natural-robotic language translation framework that (i) provides correctness verification for generated control programs and (ii) enhances the performance of LLMs in program generation via feedback-based fine-tuning for the programs. To achieve this, a Robot Skill Language (RSL) is proposed to abstract away from the intricate details of the control programs, bridging the natural language tasks with the underlying robot skills. Then, the RSL compiler and debugger are constructed to verify RSL programs generated by the LLM and provide error feedback to the LLM for refining the outputs until being verified by the compiler. This provides correctness guarantees for the LLM-generated programs before being offloaded to the robots for execution, significantly enhancing the effectiveness of LLM-powered robotic applications. Experiments demonstrate NRTrans outperforms the existing method under a range of LLMs and tasks, and achieves a high success rate for light-weight LLMs.