Computer simulation and hands-on labs: A case study of teaching robotics and AI

Abstract

When teaching robotics, instructors face the challenge of finding an effective approach to bridge theoretical concepts and practical applications. Both computer simulations and hands-on laboratory experiments provide learners with opportunities for active, immersive, and experiential learning. As students progress from introductory to advanced topics and from theory to practice, their performance is contingent upon earlier knowledge and may increase, remain unchanged, or decrease. The question that arises is whether computer simulation can serve as a viable foundation for fostering an understanding of theory that enables the subsequent grasp of advanced practical concepts in robotics. Put another way, when students are introduced to the field of robotics through computer simulation, how will they perform when presented with advanced hands-on tasks involving the construction of physical robots to solve problems in physical space? To answer this question, we examined undergraduate student performance ( n = 107) across two robotics courses—an introductory course using computer simulation (Robot Operating System, Rviz, and GAZEBO) and an advanced course using physical hardware (Puzzlebot), leveraging the hardware's capability for AI tasks such as machine vision (Nvidia Jetson Nano development kit). Our findings suggest that student performance increased as they progressed from using computer simulation to engaging with hardware in the physical environment, further suggesting that teaching with computer simulations provides an adequate foundation to learn and complete more advanced tasks.