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ETHx: Self-Driving Cars with Duckietown
Self-Driving Cars with Duckietown is the first robotics and AI MOOC with scale-model self-driving cars. Learn state-of-the-art autonomy hands-on: build your own real robot (Duckiebot) and get it to drive autonomously in your scaled city (Duckietown).
This Course Includes
edx
0 (0 reviews )
9 weeks at 4-10 hours per week
english
Online - Self Paced
course
ETHx
About ETHx: Self-Driving Cars with Duckietown
Robotics and AI are all around us and promise to revolutionize our daily lives. Autonomous vehicles have a huge potential to impact society in the near future, for example, by making owning private vehicles unnecessary!
Have you ever wondered how autonomous cars actually work?
With this course, you will start from a box of parts and finish with a scaled self-driving car that drives autonomously in your living room. In the process, you will use state-of-the-art approaches, the latest software tools, and real hardware in an engaging hands-on learning experience.
Self-driving cars with Duckietown is a practical introduction to vehicle autonomy. It explores real-world solutions to the theoretical challenges of autonomy, including their translation into algorithms and their deployment in simulation as well as on hardware.
Using modern software architectures built with Python, Robot Operating System (ROS), and Docker, you will appreciate the complementary strengths of classical architectures and modern machine learning-based approaches. The scope of this Beginner course is to go from zero to having a self-driving car safely driving in a Duckietown.
This course is presented by Professors and Scientists who are passionate about robotics and accessible education. It uses the Duckietown robotic ecosystem, an open-source platform created at the MIT Computer Science and Artificial Intelligence Laboratory and now used by over 150 universities worldwide.
We support a track for learners to deploy their solutions in a simulation environment, and an additional option for learners that want to engage in the challenging but rewarding, tangible, hands-on learning experience of making the theory come to life in the real world. The hardware track is streamlined through an all-inclusive low-cost Jetson Nano-powered Duckiebot kit, inclusive of city track, available here.
This course is made possible thanks to the support of the Swiss Federal Institute of Technology in Zurich (ETH Zurich), in collaboration with the University of Montreal (Prof. Liam Paull), the Duckietown Foundation, and the Toyota Technological Institute at Chicago (Prof. Matthew Walter).
What You Will Learn?
- recognize essential robot subsystems (sensing, actuation, computation, memory, mechanical) and describe their functions .
- make your Duckiebot drive in user-specified paths .
- understand how to command a robot to reach a goal position .
- make your Duckiebot take driving decisions autonomously according to "traditional approaches", i.e., following the estimation, planning, control architecture .
- make your Duckiebot take driving decisions autonomously according to "modern approaches" (reinforcement learning) .
- process streams of images .
- be able to set up an efficient software environment for robotics with state-of-the-art tools (Docker, ROS, Python) .
- program your Duckiebot and make it safely drive in empty roads lanes .
- program your Duckiebot and make it recognize and avoid rubber duckie obstacles .
- submit your robot agents (a.k.a. "robot minds") to public challenges, and test your skills against your peers .
- independently assemble a Duckiebot and a Duckietown .
- remotely operate your Duckiebot and see with its eye(s) .
- be able to discuss differences between theory, simulation, and real word implementation for different approaches .
- experience the challenges of deploying complex autonomous robots in the real world, and reap the rewards of getting it to work .