Networked Systems: Autonomy Education with Duckietown

Autonomy Education: Teaching Networked Systems

General Information

Autonomy Education: Teaching Networked Systems

In this work, Prof. Qing-Shan Jia from Tsinghua University in China explores the challenges and innovations in teaching networked systems, a domain with applications ranging from smart buildings to autonomous systems.

The study reviews curriculum structures and introduces practical solutions developed by the Tsinghua University Center for Intelligent and Networked Systems (CFINS).

Over the past two decades, CFINS has designed courses, developed educational platforms, and authored textbooks to bridge the gap between theoretical knowledge and practical application.

They feature Duckietown as part of an educational platform for autonomous driving. Duckietown offers a low-cost, do-it-yourself (DIY) framework for students to construct and program Duckiebots – autonomous mobile robotic vehicles. Duckietown allows learners to apply theoretical concepts in areas related to robot autonomy, like signal processing, machine learning, reinforcement learning, and control systems.

Duckietown enables students to gain hands-on experience in systems engineering, with calibration of sensors, programming navigation algorithms, and working on cooperative behaviors in multi-robot settings. This approach allows for the creation of complex cyber physical systems using state-of-the-art science and technology, not only democratizing access to autonomy education but also fostering understanding, even with remote learning scenarios. 

The integration of Duckietown into the curriculum exemplifies the innovative strategies employed by CFINS to make networked systems education both practical and impactful.

Abstract

In the author’s words:

Networked systems have become pervasive in the past two decades in modern societies. Engineering applications can be found from smart buildings to smart cities. It is important to educate the students to be ready for designing, analyzing, and improving networked systems. 

But this is becoming more and more challenging due to the conflict between the growing knowledge and the limited time in the curriculum. In this work we consider this important problem and provide a case study to address these challenges. 

A group of courses have been developed by the Center for Intelligent and Networked Systems, department of Automation, Tsinghua University in the past two decades for undergraduate and graduate students. We also report the related education platform and textbook development. Wish this would be useful for the other universities.

Conclusion - Networked Systems: Autonomy Education with Duckietown

Here are the conclusions from the author of this paper:

“In this work we provided a case study on the education practice of networked systems in the center for intelligent and networked systems, department of automation, Tsinghua University. The courses mentioned in this work have been delivered for 20 years, or even more. From this education practice, the following experience is summarized. First, use research to motivate the study. 

Networked systems is a vibrant research field. The exciting applications in smart buildings, autonomous driving, smart cities serve as good examples not just to motivate the students but also to make the teaching materials concrete. Inviting world-class talks and short-courses are also good practice. Second, education platforms help to learn the knowledge better. Students have hands-on experience while working on these education platforms. 

This project-based learning provides a comprehensive experience that will get the students ready for addressing the real-world engineering problems. Third, online/offline hybrid teaching mode is new and effective. This is especially important due to the pandemic. Lotus Pond, RainClassroom, and Tencent Meeting have been well adopted in Tsinghua. Students can interact with the teachers more frequently and with more specific questions. 

They can also replay the course offline, including their answers to the quiz and questions in the classroom. We hope that this summary on the education on networked systems might help the other educators in the field.”

Project Authors

Qing-Shan Jia is a Professor at the Tsinghua University, Beijing, People’s Republic of China.

Learn more

Duckietown is a platform for creating and disseminating robotics and AI learning experiences.

It is modular, customizable and state-of-the-art, and designed to teach, learn, and do research. From exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge, Duckietown evolves with the skills of the user.