AI‐Enhanced Simulation Platform for Robotics Education: From Kinematics to Reinforcement Learning
Year: 2026
Type: article
Abstract: ABSTRACT Undergraduate robotics education faces a widening gap between classical kinematic/dynamic theory and contemporary AI‐driven industrial practice. While simulation tools bridge theory and practice, most lack native support for data‐driven algorithm development. This paper presents an AI‐enhanced virtual‐real integrated simulation platform built on MATLAB/Simulink and deployed via VxWorks real‐time hardware‐in‐the‐loop. The platform integrates three AI modules—genetic‐algorithm/reinforcement‐learning trajectory optimization, neural‐network inverse kinematics, and an LLM coding advisor—within a coherent pedagogical workflow. A multi‐year deployment across 600+ students shows a 12.1% increase in complex‐task performance, reduced score variance, and significant gains in AI literacy. This work offers a replicable model for embedding AI competencies into foundational engineering courses.
DOI: 10.1002/cae.70276
Authors: Haihui Dong, Hongguang Wang, Yanfang Song, Ganxi Luo, Bolin Li, Adham Manyara, Mucong Chi, Junda Huang, Lianhui Li
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W7214383167