用大模型让新手通过自然语言控制机器人仿真，提升学习体验。
EduSim-LLM: An Educational Platform Integrating Large Language Models and Robotic Simulation for Beginners
- 将自然语言指令转为机器人可执行动作序列
- 复杂任务下指令解析准确率超88.9%
- 适合机器人教学与初学者实践
近年来，大型语言模型（LLMs）的快速发展显著提升了自然语言理解与人机交互能力，在机器人领域带来新机遇。然而，将自然语言理解融入机器人控制仍是人机交互与智能自动化产业快速发展的关键挑战，限制了复杂机器人系统的直观操控与教育可用性。为此，我们提出EduSim-LLM教育平台，整合大语言模型与机器人仿真，构建基于自然语言驱动的控制模型，实现将自然语言指令转化为CoppeliaSim中的可执行机器人行为序列。设计了直接控制与自主控制两种人机交互模式，基于多个语言模型开展系统仿真，评估多机器人协作、运动规划与操作能力。实验表明，LLMs能可靠地将自然语言转化为结构化机器人动作；应用提示工程模板后，指令解析准确率显著提升；在最高复杂度测试中，整体准确率超过88.9%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。