EduSim-LLM: An Educational Platform Integrating Large Language Models and Robotic Simulation for Beginners

• URL: http://arxiv.org/abs/2601.01196v1
• Date: Sat, 03 Jan 2026 14:40:39 GMT
• Title: EduSim-LLM: An Educational Platform Integrating Large Language Models and Robotic Simulation for Beginners
• Abstract summary: We present the EduSim-LLM, an educational platform that integrates Large Language Models with robot simulation.<n>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.
• Score: 0.22917707112773592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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