Duckietown: An Innovative Way to Teach Autonomy

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 560)


Teaching robotics is challenging because it is a multidisciplinary, rapidly evolving and experimental discipline that integrates cutting-edge hardware and software. This paper describes the course design and first implementation of Duckietown, a vehicle autonomy class that experiments with teaching innovations in addition to leveraging modern educational theory for improving student learning. We provide a robot to every student, thanks to a minimalist platform design, to maximize active learning; and introduce a role-play aspect to increase team spirit, by modeling the entire class as a fictional start-up (Duckietown Engineering Co.). The course formulation leverages backward design by formalizing intended learning outcomes (ILOs) enabling students to appreciate the challenges of: (a) heterogeneous disciplines converging in the design of a minimal self-driving car, (b) integrating subsystems to create complex system behaviors, and (c) allocating constrained computational resources. Students learn how to assemble, program, test and operate a self-driving car (Duckiebot) in a model urban environment (Duckietown), as well as how to implement and document new features in the system. Traditional course assessment tools are complemented by a full scale demonstration to the general public. The “duckie” theme was chosen to give a gender-neutral, friendly identity to the robots so as to improve student involvement and outreach possibilities. All of the teaching materials and code is released online in the hope that other institutions will adopt the platform and continue to evolve and improve it, so to keep pace with the fast evolution of the field.


Duckietown Autonomous vehicles Educational robotics Active learning Constructive alignment Backwards design 



This work was funded by the National Science foundation through award IIS #1318392 and through the National Robotics Initiative award #1405259. The work was also supported by the Toyota Research Institute and the Ford Motor Company.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Massachusetts Institute of TechnologyCambridgeUSA

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