In recent years, the rapid development of Large Language Models (LLMs) has significantly enhanced natural language understanding and human-computer interaction, creating new opportunities in the field of robotics. However, the integration of natural language understanding into robotic control is an important challenge in the rapid development of human-robot interaction and intelligent automation industries. This challenge hinders intuitive human control over complex robotic systems, limiting their educational and practical accessibility. To address this, we present the EduSim-LLM, an educational platform that integrates LLMs with robot simulation and constructs a language-drive control model that translates natural language instructions into executable robot behavior sequences in CoppeliaSim. We design two human-robot interaction models: direct control and autonomous control, conduct systematic simulations based on multiple language models, and evaluate multi-robot collaboration, motion planning, and manipulation capabilities. Experiential results show that LLMs can reliably convert natural language into structured robot actions; after applying prompt-engineering templates instruction-parsing accuracy improves significantly; as task complexity increases, overall accuracy rate exceeds 88.9% in the highest complexity tests.

翻译：近年来，大语言模型（LLMs）的快速发展显著提升了自然语言理解与人机交互能力，为机器人领域创造了新的机遇。然而，将自然语言理解融入机器人控制，是人机交互与智能自动化产业快速发展面临的一项重要挑战。这一挑战阻碍了人类对复杂机器人系统的直观控制，限制了其在教育与实际应用中的可及性。为此，我们提出了EduSim-LLM，一个将LLMs与机器人仿真相结合的教育平台，并构建了一个语言驱动控制模型，该模型能将自然语言指令转换为CoppeliaSim中可执行的机器人行为序列。我们设计了两种人机交互模型：直接控制与自主控制，基于多种语言模型进行了系统性仿真，并评估了多机器人协作、运动规划与操作能力。实验结果表明，LLMs能够可靠地将自然语言转换为结构化的机器人动作；应用提示工程模板后，指令解析准确率显著提升；随着任务复杂度增加，在最高复杂度测试中整体准确率超过88.9%。