[Paper Review] Teaching Autonomous Systems Hands-On: Leveraging Modular Small-Scale Hardware in the Robotics Classroom

Johannes Betz, Hongrui Zheng+9 more|arXiv (Cornell University)|Sep 21, 2022

Teaching and Learning Programming4 citations

TL;DR

This paper presents F1TENTH, a modular, small-scale autonomous vehicle platform designed for hands-on teaching of autonomous systems in higher education. By integrating theory, simulation, and real hardware through race-based assessments, the platform significantly boosts student motivation and understanding, with over 80% reporting enhanced learning motivation and 70% reporting improved subject comprehension.

ABSTRACT

Although robotics courses are well established in higher education, the courses often focus on theory and sometimes lack the systematic coverage of the techniques involved in developing, deploying, and applying software to real hardware. Additionally, most hardware platforms for robotics teaching are low-level toys aimed at younger students at middle-school levels. To address this gap, an autonomous vehicle hardware platform, called F1TENTH, is developed for teaching autonomous systems hands-on. This article describes the teaching modules and software stack for teaching at various educational levels with the theme of "racing" and competitions that replace exams. The F1TENTH vehicles offer a modular hardware platform and its related software for teaching the fundamentals of autonomous driving algorithms. From basic reactive methods to advanced planning algorithms, the teaching modules enhance students' computational thinking through autonomous driving with the F1TENTH vehicle. The F1TENTH car fills the gap between research platforms and low-end toy cars and offers hands-on experience in learning the topics in autonomous systems. Four universities have adopted the teaching modules for their semester-long undergraduate and graduate courses for multiple years. Student feedback is used to analyze the effectiveness of the F1TENTH platform. More than 80% of the students strongly agree that the hardware platform and modules greatly motivate their learning, and more than 70% of the students strongly agree that the hardware-enhanced their understanding of the subjects. The survey results show that more than 80% of the students strongly agree that the competitions motivate them for the course.

Motivation & Objective

• Address the gap in higher education robotics courses that lack systematic hands-on training with real hardware.
• Develop a scalable, modular hardware and software platform suitable for teaching autonomous driving fundamentals across undergraduate and graduate levels.
• Replace traditional exams with competitive racing events to increase student engagement and motivation.
• Provide a curriculum that bridges theoretical concepts in perception, planning, and control with practical implementation on real robotic systems.
• Enable widespread adoption through open-source hardware and software, supporting long-term educational and research use.

Proposed method

• Design a modular, small-scale autonomous vehicle platform (F1TENTH) with standardized hardware and software components for consistent deployment.
• Develop a six-module course structure organized around three themes: Foundations, High-Speed, and Multi-Vehicle, progressively increasing in complexity.
• Integrate simulation environments (F1TENTH 2D Simulator) to allow students to test algorithms before real-world deployment.
• Implement race-based assessments where students apply their software to real F1TENTH vehicles, replacing traditional exams.
• Use open-source tools and platforms (openEdx, GitHub) to distribute course materials, hardware build guides, and software stack.
• Conduct longitudinal course deployment across four universities and collect student feedback to evaluate educational impact.

Experimental results

Research questions

• RQ1 To what extent does hands-on hardware-based learning with F1TENTH improve student motivation and engagement in autonomous systems courses?
• RQ2 How effective is the F1TENTH platform in enhancing students’ understanding of core autonomous driving concepts such as perception, planning, and control?
• RQ3 What is the educational value of race-based competitions compared to traditional assessment methods in robotics education?
• RQ4 How scalable and adoptable is the F1TENTH platform across diverse academic institutions and educational levels?
• RQ5 What role does real hardware deployment play in developing computational and systems thinking in students?

Key findings

• Over 80% of students strongly agreed that the F1TENTH hardware platform and lab modules greatly motivated their learning.
• More than 70% of students strongly agreed that the hardware-enhanced their understanding of autonomous systems concepts.
• Despite high motivation from competitions, only 50% of students strongly agreed that the races enhanced their learning outcomes, indicating limited educational value beyond motivation.
• The F1TENTH platform has been successfully adopted in semester-long courses at four universities over multiple years, demonstrating scalability and sustainability.
• The open-source release of hardware designs, software stack, and course materials has enabled broad adaptation and reuse across institutions and research projects.
• The platform successfully bridges the gap between research-grade autonomy systems and low-end toy platforms, offering a practical, modular, and scalable teaching solution.