Join the new “Self-Driving Cars with Duckietown” MOOC

Join the self-driving cars with Duckietown MOOC user-paced edition

Over 7200 learners engaged in a robotics and AI learning adventure with “Self-Driving Cars with Duckietown”, the first massive online open course (MOOC) on robot autonomy with hardware, hosted on the edX platform.

Kicking off on November 29th, this new edition is a user-paced course with rich and engaging modules offering a grand tour of real-world robotics, from computer vision to perception, planning, modeling, control, and machine learning, released all at once!

With simulation and real-world learning activities, learners can touch with hand the emergence of autonomy in their robotic agents with approaches of increasing complexity, from Brateinberg vehicles to deep learning applications.

We are thrilled to welcome you to the start of the second edition of Self-Driving Cars with Duckietown.

This is a new learning experience in many different ways, for both you and us. While the course is self-paced, the instructors and staff, as well as your peer learners and the community of those that came before you are standing behind, ready to intervene and support your efforts at any time.

Learn autonomy hands-on by making real robots take their own decisions and accomplish broadly defined tasks. Step by step from the theory, to the implementation, to the deployment in simulation as well as on Duckiebots.

Leverage the power of the NVIDIA Jetson Nano-powered Duckiebot to see your algorithms come to life!

MOOC Factsheet

Prerequisites

What you will learn

Designed for university-level students and professionals, this course is brought to you by the Swiss Federal Institute of Technology in Zurich (ETHZ), in collaboration with the University of Montreal, the Duckietown Foundation, and the Toyota Technological Institute at Chicago.

Learning autonomy requires a fundamentally different approach when compared to other computer science and engineering disciplines. Autonomy is inherently multi-disciplinary, and mastering it requires expertise in domains ranging from fundamental mathematics to practical machine-learning skills.

The Duckietown robotic ecosystem was created at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) in 2016 and is now used in over 175 universities worldwide.

“The Duckietown educational platform provides a hands-on, scaled-down, accessible version of real-world autonomous systems.” said Emilio Frazzoli, Professor of Dynamic Systems and Control, ETH Zurich, “Integrating NVIDIA’s Jetson Nano power in Duckietown enables unprecedented access to state-of-the-art compute solutions for learning autonomy.”

Enroll now and don’t miss the chance to join in the first vehicle autonomy MOOC with hands-on learning!

The “Self-Driving cars with Duckietown” Massive Open Online Course on edX

"Self-Driving Cars with Duckietown" hands-on MOOC on edX

We are launching a massive open online course (MOOC): “Self-Driving Cars with Duckietown” on edX, and it is free to attend! 

This course is made possible thanks to the support of the Swiss Federal Institute of Technology in Zurich (ETHZ), in collaboration with the University of Montreal, the Duckietown Foundation, and the Toyota Technological Institute at Chicago.

This course combines remote and hands-on learning with real-world robots. It is offered on edX, the trusted platform for learning, and it is now open for enrollment

Learning activities will support the use of Jetson Nano equipped Duckiebots, powered by NVIDIA.

Learning autonomy

Participants will engage in software and hardware hands-on learning experiences, with focus on overcoming the challenges of deploying autonomous robots in the real world.

This course will explore the theory and implementation of model- and data-driven approaches for making a model self-driving car drive autonomously in an urban environment.

Pedestrian detection

MOOC Factsheet

Prerequisites

What you will learn

Why Self-driving cars with Duckietown?

Teaching autonomy requires a fundamentally different approach when compared to other computer science and engineering disciplines, because it is multi-disciplinaryMastering it requires expertise in domains ranging from fundamental mathematics to practical machine-learning skills.

Robot Perception 

Robots operate in the real world, and theory and practice often do not play well togetherThere are many hardware platforms and software tools, each with its own strengths and weaknesses. It is not always clear what tools are worth investing time in mastering, and how these skills will generalize to different platforms. 

Duckiebot Detection

Learning through challenges

Progressing through behaviors of increasing complexity, participants uncover concepts and tools that address the limitations of previous approaches. This allows to get Duckiebots to actually do things, while gradually re-iterating concepts through various technical frameworks. Simulation and real-world experiments will be performed using a Python, ROS, and Docker based software stack.

Robot Planning

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This course combines remote and hands-on learning with real-world robots.

It is offered on edX, the trusted platform for learning, and it is now open for enrollment.

Learning activities will support the use of NVIDIA Jetson Nano powered Duckiebots.