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

TL;DR

EduSim-LLM is an educational platform that combines large language models with robotic simulation to enable natural language control of robots, improving accessibility and understanding of human-robot interaction.

Contribution

The paper introduces EduSim-LLM, a novel platform integrating LLMs with robot simulation for natural language control, including new interaction models and systematic evaluation methods.

Findings

LLMs reliably convert natural language into robot actions

Prompt-engineering improves instruction parsing accuracy

Accuracy exceeds 88.9% in complex tasks

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.