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
- Name: Self-driving cars with Duckietown
- Platform: edX
- Cost: free to attend
- Instructors: Swiss Federal Institute of Technology in Zurich (ETHZ), Université de Montréal (UdM), Toyota Technological Institute at Chicago (TTIC)
Prerequisites
- Basic Linux, Python, Git
- Elements of linear algebra, probability, calculus
- Elements of kinematics, dynamics
- Computer with native Ubuntu installation
- Broadband internet connection
What you will learn
- Computer Vision
- Robot operations
- Object Detection
- Onboard localization
- Robot Control
- Planning
- Reinforcement Learning
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!
