This paper presents a novel approach to enhance autonomous robotic manipulation using the Large Language Model (LLM) for logical inference, converting high-level language commands into sequences of executable motion functions. The proposed system combines the advantage of LLM with YOLO-based environmental perception to enable robots to autonomously make reasonable decisions and task planning based on the given commands. Additionally, to address the potential inaccuracies or illogical actions arising from LLM, a combination of teleoperation and Dynamic Movement Primitives (DMP) is employed for action correction. This integration aims to improve the practicality and generalizability of the LLM-based human-robot collaboration system.

翻译：本文提出了一种新方法，利用大语言模型进行逻辑推理，将高层语言指令转换为可执行的逐步运动函数序列，以增强自主机器人操作能力。所提出的系统融合了大语言模型与基于YOLO的环境感知优势，使机器人能够根据给定指令自主做出合理决策并进行任务规划。此外，针对大语言模型可能产生的潜在不准确或不合逻辑的动作，采用遥操作与动态运动基元相结合的方式进行动作校正。这一集成旨在提升基于大语言模型的人机协作系统的实用性与泛化能力。