Learn to Program Self-Driving Cars (and Help Duckies Commute) With Duckietown Robotics

Duckietown is based on an MIT engineering course (2.166), first taught in 2016. It’s designed to be an affordable, modular, scalable, duckie-filled introduction to autonomous vehicles.

Unfortunately, duckies are terrible drivers, probably because they don’t have arms or legs and their insides are filled with squeakers instead of brains. So to get from place to place, the duckies depend on little autonomous cars, which are programmed by MIT students. And now you’ll be able to program them, too: Duckietown kits are already being used to teach practical robots to students around the world, and the idea with the Kickstarter is to help them scale up, providing robots and classroom kits that can do more for less money. At the moment, a Duckiebox full of everything you need to make a single Duckiebot while a starter kit which also includes road tiles, road tape, signs, and extra duckies . And of course, there’s software plus a substantial and growing curriculum to back all of this up. Even homework assignments, hooray! The Duckiebots may not look all kinds of fancy, but they’re very capable little platforms, especially in the environment for which they’re designed. Their only sensor is an HD camera, and all of the autonomous driving computation is done on board with a Jeson Nano. With just a single Duckiebot, you can go through practical, experimental lessons including “Modern Robotic Systems,” “Architectures,” “Representations,” “Modeling,” “Calibration,” “Signal Processing,” “Computer Vision,” “Estimation,” and “Control.” Toss a second Duckiebot into the mix, and things get more complex, and if you want to get superfancy, you can even add an Intel Movidius neural compute stick for end-to-end imitation learning and sim2real reinforcement learning transfer. 

Duckietown has a playful appearance but really is a complex platform, in the sense that its functionalities are the result of the interplay of hardware and many different software components. To make this complexity manageable, every Duckiebot runs the same code, and all new functionalities are either modifications or additions to this unique “master” code. During courses, students and teachers are called upon to create new functionalities or improve existing ones. To do so, they need to change the existing master code. Often, the outcome of their projects affects or depends upon changes to the master code made by their peers–or really by anyone, either in the same class, or elsewhere in the world. Once the course is over, their contributions to the master code are now running on every other Duckiebot in the world. The Duckietown Engineering role-playing experience is a startup simulation. It is about communicating this sense of interdependency, as one would expect in a newly funded company, where the “engineers in training” as well as the “department heads” are all focused towards the common greater objective, and where the success of the whole depends on the success of all. Since the platform really is one, the team in the same way needs to feel like one. This is in stark contrast with the typical classroom learning experience, where students work to create their own project, that is independent from the projects of their peers and has a lifespan limited to the duration of the course. “By just following the instructions, learners will experience the hardware assembly of a robot, the basics of Linux and ROS, the need to calibrate the camera, and be able to ‘play around’ with fundamental car behaviors like lane following, obstacle avoidance, and stopping at a red light” Duckiebots are vision based. How much of a constraint is this for students interested in learning about autonomy, which often uses many other types of sensors? In order to keep Duckiebots affordable, we looked at designing a platform that had the minimum amount of hardware while still being able to do “scientifically interesting” things. Vision provides so much data that it allows Duckiebots to have only one sensor and do most of the things a real self-driving car would do. At the same time, it introduces some of the relevant scientific challenges a real self-driving car would face. The exclusive use of vision in Duckietown is not a constraint, quite the opposite. It is an opportunity to play with serious science rather than work with toy sensors, while keeping the platform affordable. How much experience with robotics or writing code do students (or teachers) need to be able to use a Duckietown kit? Can you highlight a few things that students will learn? In order to use the Duckietown kit, learners (whether students or teachers) will be able to follow step-by-step instructions detailed in our open source Duckietown book (or Duckiebook). Not much robotics or coding experience is necessary to follow these instructions, although they are just the tip of an iceberg, that the more experience might choose to explore. By just following the instructions, learners will experience the hardware assembly of a robot (need for sensing, actuation, power and computation), the basics of Linux and ROS (Robotic Operating System) operations, the need to calibrate the camera, and be able to “play around” (tune high level parameters) with fundamental car behaviors like lane following, obstacle (i.e. duckie and Duckiebot) avoidance, intersection navigation, and stopping at a red light. How is the Duckietown experience different for students of different ages and abilities, and how can Duckietown grow with them? At undergraduate level, students using Duckietown learn the fundamentals of autonomy (perception, estimation, decision making, actuation, the relevance of environmental disturbances, modeling, etc.), robotics (system level interplay of functionalities), and computer vision (line detection). Moving up to graduate school, students can touch with hand the lower level implementation, see how the difference is everything “working” or not really lies in the details. Graduate student will appreciate the modularity of the platform (hardware and software) and find it easy to completely remove “blocks” of existing functionalities and replace them with their own.       

Where do you hope that Duckietown will be in five years? From an education standpoint, we envision Duckietown becoming a milestone learning experience in the fields of robotics and autonomy education for everybody, regardless if one is affiliated with an educational institution or a stay-at-home “makademics” (makers with the spirit of academics). We envision it to be an educational experience that will be automatically tailored to each learner, providing an international community with which to relate and a local community with which to share the enthusiasm. From a research standpoint, we envision Duckietown to become a standardized research test bed for embodied autonomy.