Towards Autonomous Driving with Small-Scale Cars: A Survey of Recent Development

Posted on June 30, 2024 | by Duckietown Admin

General Information

Towards Autonomous Driving with Small-Scale Cars: A Survey of Recent Development

“Towards Autonomous Driving with Small-Scale Cars: A Survey of Recent Development by Dianzhao Li, Paul Auerbach, and Ostap Okhrin is a review that highlights the rapid development of the industry and the important contributions of small-scale car platforms to robot autonomy research.

This survey is a valuable resource for anyone looking to get their bearings in the landscape of autonomous driving research.

We are glad see Duckietown – not only included on the list – but identified as one of the platforms that started a marked increase in the trend of yearly published papers.

The mission of Duckietown, since we started at as a class at MIT, is to democratize access to the science and technology of robot autonomy. Part of how we intended to achieve this mission was to streamline the way autonomous behaviors for non-trivial robots were developed, tested and deployed in the real world.

From 2018-2021 we ran several editions of the AI Driving Olympics (AI-DO): an international competition to benchmark the state of the art of embodied AI for safety-critical applications. It was a great experience – not only because it led to the development of the Challenges infrastructure, the Autolab infrastructure, and many agent baselines that catalyze further developments that are now available to the broader community, but even because it was the first time physical robots were brought the world’s leading scientific conference in Machine Learning (NeurIPS: the Neural Information Processing Systems conference – known as NIPS the first time AI-DO was launched).

All this infrastructure development and testing might have been instrumental in making R&D in autonomous mobile robotics more efficient. Practitioners in the field know-how doing R&D is particularly difficult because final outcomes are the result of the tuple (robot) x (environment) x (task) – so not standardizing everything other than the specific feature under development (i.e., not following the ceteris paribus principle) often leads to apples and pair comparisons, i.e., bad science, which hampers the overall progress of the field.

We are happy to see Duckietown recognized as a contributor to facilitating the making of good science in the field. We beleive that even better and more science will come in the next years, as the students being educated with the Duckietown system start their professional journeys in academia or the workforce.

We are excited to see what the future of robot autonomy will look like, and we will continue doing our best by providing tools, workflows, and comprehensive resources to facilitate the professional development of the next generations of scientists, engineers, and practicioners in the field!

To learn more about Duckietown teaching resources follow the link below.

Starting around 2016, with the introduction of Duckietown, BARC, and Autorally, there was a significant increase in research papers.

Dianzhao Li, Paul Auerbach, Ostap Okhrin

Abstract

We report the abstract of the authors’ work:

“While engaging with the unfolding revolution in autonomous driving, a challenge presents itself, how can we effectively raise awareness within society about this transformative trend? While full-scale autonomous driving vehicles often come with a hefty price tag, the emergence of small-scale car platforms offers a compelling alternative.

These platforms not only serve as valuable educational tools for the broader public and young generations but also function as robust research platforms, contributing significantly to the ongoing advancements in autonomous driving technology.

This survey outlines various small-scale car platforms, categorizing them and detailing the research advancements accomplished through their usage. The conclusion provides proposals for promising future directions in the field.”

Towards Autonomous Driving with Small-Scale Cars: A Survey of Recent Development

Here is a visual tour of the work. For more details, check out the full paper.

Based on published studies each year on Google Scholar with the search terms: robot car, the research on small-scale cars has seen substantial growth in the number of papers over the years. In the early 2000s, projects like s-bot, e-puck, and TurtleBot emerged. Starting around 2016, with the introduction of Duckietown, BARC, and Autorally, there was a significant increase in research papers. This trend continued with the development of projects like DeepRacer, Donkeycar, and F1TENTH. More computationally advanced small-scale cars have been introduced in recent years, such as ART/ATK and XTENTH-CAR. — Fig. 1-A. Illustration of the development and current states of small-scale car platforms, each depicted platform image sourced from its respective paper or website.

Fig 1-B. Samples of small-scale car platforms, categorized into educational platforms and research platforms, including multiple vehicle setups such as ORAC and UDSSC

Comparison of two pipelines for the autonomous driving system. An end-to-end system maps raw sensor inputs directly into control commands, whereas a modular system includes multiple subsystems to process the sensor inputs sequentially and output control commands. — Fig. 2. Comparison of two pipelines for the autonomous driving system.

Research in the literature concerning small-scale cars is classified into various benchmark tasks in this survey, ranging from localization and mapping, path planning and following, lane keeping to racing, and cooperative driving.whereas a modular system includes multiple subsystems to process the sensor inputs sequentially and output control commands.whereas a modular system includes multiple subsystems to process the sensor inputs sequentially and output control commands. — Fig. 3. Research in the literature concerning small-scale cars is classified into various benchmark tasks in this survey, ranging from localization and mapping, path planning and following, lane keeping to racing, and cooperative driving.whereas a modular system includes multiple subsystems to process the sensor inputs sequentially and output control commands.

Fig. 4. Overall perception and control pipeline. The perception module receives onboard sensor data and produces a predicted occupancy map using a U-Net style generative neural network. The control algorithm receives the robot state, predicted occupancy map, and goal point and generates collision-free trajectories with RRT

Fig. 5. The probabilistic dynamics model is trained using a multistep loss that considers how uncertainty propagates. The trajectory tracking controller is used to predict a distribution of closed-loop trajectories. The divergence constrained optimizer is used to find a closed- loop trajectory with low divergence for MuSHR cars

Fig. 6. Architecture of the proposed adaptive trajectory tracking control system

Different outputs of the multi-step image processing pipeline start from the image input from the camera, proceed to detect road markings according to the color with HSV thresholding, detect marking edges with a canny filter, and finalize with detection marking used to estimate the lateral displacement and angle offset. — Fig. 7. Image Processing Pipeline for Road Marking Detection and Lateral Displacement Estimation

Fig. 8. Image patches are encoded using a ViT and predict a class label for each patch. Then, the coarse segmentation output is used for a potential-field based controller.

Framework proposes the perception module leverages camera images to produce impact attributes regarding the environment, then the DRL control module utilizes the information to control the agent perform car following behavior. — Fig. 9. Framework for Car Following Behavior Using Perception and DRL Control Modules

Fig. 10. The cost map of the track in front of the vehicle is predicted with CNN.

Fig. 11. End-to-end IL system used for an Autorally racing car.

Fig. 12. Overall schematics of the multi-vehicle, mixed reality RL approach. Both virtual and real DeepRacer vehicles exist within the simulation that manages the physics of the virtual cars and emulates collisions in mixed reality.

Fig. 13. Diagram of trajectory planner with C-MOBIL and C-IDM. The ego vehicle plans a trajectory and velocity profile based on its state and the actual state of the neighboring vehicles

Summary and conclusion

Here is what the authors learned from this survey:

“In this paper, we offer an overview of the current state-of-the- art developments in small-scale autonomous cars. Through a detailed exploration of both past and ongoing research in this domain, we illuminate the promising trajectory for the advancement of autonomous driving technology with small-scale cars. We initially enumerate the presently predominant small-scale car platforms widely employed in academic and educational domains and present the configuration specifics of each platform. Similar to their full-size counterparts, the deployment of hyper-realistic simulation environments is imperative for training, validating, and testing autonomous systems before real-world implementation. To this end, we show the commonly employed universal simulators and platform-specific simulators.

Furthermore, we provide a detailed summary and categorization of tasks accomplished by small-scale cars, encompassing localization and mapping, path planning and following, lane-keeping, car following, overtaking, racing, obstacle avoidance, and more. Within each benchmarked task, we classify the literature into distinct categories: end-toend systems versus modular systems and traditional methods 20 versus ML-based methods. This classification facilitates a nuanced understanding of the diverse approaches adopted in the field. The collective achievements of small-scale cars are thus showcased through this systematic categorization. Since this paper aims to provide a holistic review and guide, we also outline the commonly utilized in various well-known platforms. This information serves as a valuable resource, enabling readers to leverage our survey as a guide for constructing their own platforms or making informed decisions when considering commercial options within the community.

We additionally present future trends concerning small-scale car platforms, focusing on different primary aspects. Firstly, enhancing accessibility across a broad spectrum of enthusiasts: from elementary students and colleagues to researchers, demands the implementation of a comprehensive learning pipeline with diverse entry levels for the platform. Next, to complete the whole ecosystem of the platform, a powerful car body, varying weather conditions, and communications issues should be addressed in a smart city setup. These trends are anticipated to shape the trajectory of the field, contributing significantly to advancements in real-world autonomous driving research.
While we have aimed to achieve maximum comprehensiveness, the expansive nature of this topic makes it challenging to encompass all noteworthy works. Nonetheless, by illustrating the current state of small-scale cars, we hope to offer a distinctive perspective to the community, which would generate more discussions and ideas leading to a brighter future of autonomous driving with small-scale cars.”

Project Authors

Dianzhao Li is a research assistant at the Technische Universität Dresden, Dresden, Germany.

Paul Auerbach is with Barkhausen Institut gGmbH, Dresden, Germany

Ostap Okhrin is Chair of Statistics and Econometrics at the Institute of Economics and Transport, School of Transportation, Technische Universitat Dresden in Germany.

Learn more

Duckietown is a platform for creating and disseminating robotics and AI learning experiences.

It is modular, customizable and state-of-the-art, and designed to teach, learn, and do research. From exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge, Duckietown evolves with the skills of the user.

End-to-end Deep RL (DRL) systems: in autonomous driving environments that rely on visual input for vehicle control face potential security risks, including:

State Adversarial Perturbations: Subtle alterations to visual input that mislead the DRL agent, causing incorrect decision-making.
Reward Tampering: Manipulation of the reward signal to misguide the learning process, leading the agent to adopt unsafe or inefficient policies.

These vulnerabilities can compromise the safety and reliability of self-driving vehicles.

Massachusettes Institute of Technology: first BCI hackathon

Posted on May 20, 2024 | by Duckietown Admin

Cambridge, MA, USA – McGovern Institute, 24-25 February 2024: Over 100 participants took part to the first MIT BCI hackathon, competing in teams to control Duckiebots using brain computer interfaces.

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Controlling Duckiebots using brain computer interfaces

Over 100 participants gathered at the Massachusetts Institute of Technology for the first BCI hackathon organized by Dr. Federico Claudi. The participants tried to control a Duckiebot using only brain computer interfaces, and competed in a series of tasks.

BCI is the field of research that studies how to measure, amplify, filter and utilize electrical signals from the brain to interact with external devices.

What made this hackathon distinctive was the hands-on challenge, where participants were tasked with controlling a physical robot. This not only tested participants’ technical skills but also showcased their ability to tackle real-world problems through innovative BCI applications.

The task teams competed on was having Duckiebots (DB21-J4) navigate a road loop as fast as possible while avoiding Duckies. Here is an example:

The hardware used in this competition was an X.on EEG headset, and Duckiebots for control. Also, the winning team’s solution will be soon made available as a reproducible Learning Experience with Duckietown – stay tuned!

The Duckiebot is a DIY, Raspberry Pi-based robot powered by Nvidia and designed for introducing learners to autonomous technologies.

If you would like to contribute in developing accessible BCI LXs with Duckietown, and support the dissemination of BCI research, e.g. reach out to us at [email protected].

Learn more about Duckietown

The Duckietown platform enables state-of-the-art robotics and AI learning experiences.

It is designed to help teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of human knowledge.

Duckiedrone: how to fly a Raspberry Pi-based autonomous quadcopter

Posted on October 16, 2023 | by Duckietown Admin

Boston, 20 October 2023: Duckietown Sky and the Duckietown drone, a Raspberry Pi-based autonomous quadcopter, are discussed with the Aerial ROS community, a group of experts working to define the future of software architectures for quadcopters.

Quick links

Learning robot autonomy by flying with Duckietown Sky

Following an invitation to the Aerial ROS workgroup community meeting, Duckietown staff was delighted to present the Duckietown Sky initiative, the current Duckiedrone design, a DIY Raspberry Pi-based autonomous quadcopter, and future plans for both hardware and courseware development.

The goal of the ROS (Robotic Operating System) aerial robotics working group is to gather drone enthusiasts within the ROS community and facilitate the sharing of ideas and discussion of issues regarding autonomous robotic platforms operating in the air.

Duckietown Sky, a National Science Foundation-funded educational effort in collaboration with Brown University started in 2019, is an integral component of the Duckietown education vision, representing the commitment to fostering robot autonomy education in all its forms. Beyond self-driving cars (Duckiebots) and smart cities (Duckietowns), Duckietown highlights what is common despite the different applications of robot autonomy. From ground to sky, whether it drives, flies, or blinks, Duckietown is a platform to learn, explore and innovate when it comes to robot autonomy.

With the focus on quadcopters, Duckietown Sky offers MOOC-style learning experiences tailored for undergraduate and senior high school students. Flight is exciting!

The program’s design criteria revolves around achieving state-of-the-art autonomy ground-up using off-the-shelf components, with a Raspberry Pi as core computational unit, for its wide-spread applications and large community. Duckiedrones, now at the second hardware design iteration moving towards the third, aim to provide students with hands-on learning experiences covering from the basics, such as soldering, to pretty advanced algorithmic cornerstones of autonomy such an UKFs (Unscented Kalman Filters) and SLAM (Simultaneous Localization and Mapping).

Aspiring engineers should however be prepared for preliminary requirements like soldering, have access to a laptop or base station, and an internet connection for setting the working environment up.

From a box of parts to a Raspberry Pi-based autonomous quadcopter

The Duckietown Sky experience is an exciting journey that begins with a simple box of parts and culminates in the creation of an autonomously flying drone. In the Duckietown spirit of democratizing access to the science and technology of autonomy through accessible platforms, the happy-yellow Duckiebox includes almost everything needed to get flying.

We encourage instructors, students and practitioners to check the development roadmaps for both our hardware design and courseware, outlined in our presentation, and reaching out without hesitation to provide comments or feedback!

Building on the extensive experience of the Duckietown team in massive open online courses (check out the Self-Driving Cars with Duckietown MOOC: the world’s first robot autonomy MOOC with hardware), we look to prepare a series of short online courses. These courses will be led by Professors from Brown, as well as other universities, and will provide an ever broader audience with the opportunity to explore the fascinating world of robot autonomy: from the science and technology to the tools and workflows, to real-world applications presented by industry and academic leaders.

Want to try the Duckietown Sky experience yourself, build a DYI Raspberry Pi-based autonomous quadcopter, or teach an aerial autonomy class at your high school or at university level? Follow the steps below to begin now.

Learn more about Duckietown

Duckietown enables state-of-the-art robotics and AI learning experiences. It is designed to help teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of human knowledge.

Three girls watching Duckiebot - Science museum

Duckietown to join the UK Permanent Science Museum Collection!

Posted on October 7, 2023 | by Duckietown Admin

Boston, October 2023: several Duckietown objects officially joined the Science Museum Group permanent Collection: the United Kingdom’s national archive of science, technology, engineering, and medicine!

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Duckietown joins UK's Science Museum Group Collection

Duckietown is a trailblazer in providing accessible solutions for teaching and learning state-of-the-art autonomy, helping in the dissemination of the science and technology of modern robotics.

In 2019, the Science Museum of London picked up on the project and included Duckietown items: a Duckiebot (DB19), a segment of Duckietown, and a Duckietown traffic light, in their “Driverless: Who is in control?” exhibition.

Following the success of this exhibition, the Board of Directors of the Science Museum started considering including the DUckietown items in the permanent collection of their institution.

We are proud to announce that, in August 2023, these Duckietown items have officially joined the Science Museum Group Collection – the UK’s national collection of science, technology, engineering and medicine.

Duckietown in the history books

Joining the permanent collection will:

“ensure that the items – as rare and representative objects – are acquired, conserved, preserved and stored in order that they may be accessible to current and future generations for interpretation, loans to other institutions, research, education, and sometimes display in temporary or permanent exhibitions.”

here are some details:

2023-216

E2019.0205.1

Duckiebot – small autonomous robot from Duckietown Project, 2018-2019

2023-222

E2019.0205.2

Traffic light kit from Duckietown Project, 2018-2019

2023-502

E2019.0205.3

City expansion pack from Duckietown Project, 2018-2019

Group photo of the Duckietown Duckiedrone Summer Academy at MassRobotics - 2023

Exploring the skies at the summer Duckiedrone academy

Posted on July 27, 2023 | by Ivano Marocchi

Boston, 7-11 July 2023: Congratulations to the fourth cohort of students of the Duckiedrone summer academy, hosted by Massrobotics, Brown University and Duckietown with the generous support of Amazon Robotics!

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Exploring the skies at the summer Duckiedrone academy

As the sun shines high, the summer Duckiedrone academy, a program which sees the cooperation of Duckietown, Amazon Fulfillment Technology and Robotics, MassRobotics and Brown University, has attracted high school students from the greater Boston area to dive into the world of autonomous aerial vehicles.

The Duckiedrone is a DIY, open, Raspberry Pi-based quadcopter kit designed for introducing learners to autonomous flight. Comes with a polished undergraduate-level course and the support of the Duckietown international community.

Students learned how to build, program, and fly a drone starting from a box of components, in addition to participating in workshops held by industry professionals such as Stephanie Tellex, Associate Professor of Computer Science at Brown University, and Andrea Francesco Daniele, Chief Technology Officer at Duckietown.

In recent years autonomous robots have started revolutionizing many industries, and drones are playing an important role in this ongoing trend with applications from agriculture to inspection, surveillance, and warehouse management.

These versatile flying machines are a gateway to the fundamentals of robot autonomy, especially (but not only!) for younger learners. Seeing a machine fly on its own is exciting!

The Duckiedrone comes with step-by-step instructions for assembly, calibration, manual and autonomous operations. Students learn from the basics of mechatronics, such as soldering and handling of electrical circuits, to elements of autonomy including sensor calibration, middlewares (ROS), PID control, online filtering and simultaneous localization and mapping (SLAM) using Python and interactive Jupyter notebooks.

Learn more about Duckietown

The Duckietown platform enables state-of-the-art robotics and AI learning experiences.

It is designed to help teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of human knowledge.

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

Posted on November 11, 2022 | by Ivano Marocchi

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.”

"I’m thrilled that ETH, with UMontreal, the Duckietown Foundation, and the Toyota Technological Institute in Chicago, are collaborating to bring this course in self-driving cars and robotics to the 35 million learners on edX. This emerging technology has the potential to completely change the way we live and travel, and the course provides a unique opportunity to get in on the ground floor of understanding and using the technology powering autonomous vehicles."

"The new NVIDIA Jetson Nano is the ultimate starter AI computer for educators and students to teach and learn AI at an incredibly affordable price. Duckietown and its EdX MOOC are leveraging Jetson to take hands-on experimentation and understanding of AI and autonomous machines to the next level."

"The Duckietown educational platform provides a hands-on, scaled down, accessible version of real world autonomous systems. 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!

Prof. Liam Paull - University de Montrèal

When rubber duckies meet the road: an interview with Prof. Liam Paull

Posted on May 10, 2022 | by Ivano Marocchi

UdM, Montréal, May 5, 2022: Liam Paull, professor at the University of Montreal and one of Duckietown’s founders, talks about his role and experiences with Duckietown.

When rubber duckies meet the road: an interview with Prof. Liam Paull

Liam Paull, professor at the University of Montreal in Quebec, and one of the very founders of Duckietown, shares below his unique perspective about Duckietown’s journey and its origin.

Quick links

Good morning, Liam.

Hello.

Thank you very much for accepting to have this little chat.
Could you tell us something about you?

Sure. So my name is Liam Paull. I’m a professor at the University of Montreal in Quebec, Canada. I teach in the computer science Department, And I do research on robotics.

Ok and when was the first time you “came across” Duckietown?

Well, I’m actually one of the creators of Duckietown, So I didn’t come across it as much! The origin story of Duckietown is kind of interesting, But I probably forgot some of the details. It must have been about 2015. And myself, Andrea Censi, and a few others were interested to get more teaching experience. We were all postdocs or research scientists at MIT at the time. I guess we started brainstorming ideas, and then roughly around that time, I switched positions at MIT. I was previously a postdoc in John Leonard’s group working on marine robotics, and then I switched to become part of Danielle Lerous lab and lead an autonomous driving project. And so somehow the stars just aligned. That the right topic for this class that we would teach would be autonomous driving. Yeah, the Ducky thing is kind of a separate thing. Actually, Andrea had started this other thing that was making videos for people to publicize their work at a top robotics conference Called the international conference robotics automation, and somehow had the idea that every single video that was submitted should have a rubber Ducky in it. And this was for scale or something.
There was some kind of reason behind it I sort of forget. But anyway, so the branding kind of caught fire.
When we were building the class, we agreed the one constraint was that there should be duckies involved somehow, and the rest is kind of history!

What’s your relationship with Duckietown today? Like, do you use it in particular for some activities, your daily work or some project? Yeah, for sure. I guess I use it in a number of ways. Maybe the first way is that I teach a class every fall called autonomous vehicles, Where the Duckietown platform is the platform that we use for the experiments and labs in the class. So just like the original class, Every student gets a robot that they assemble, and then we learn about computer vision and autonomous driving and all the good stuff related to robotics. But I also use the platform for some amount of research. Also in my group, I believe that there’s a lot of interesting research directions that come from a kind of standardized, small scale, accessible autonomous driving platform like this. Recently, most of the work that we’ve been doing in terms of research has been about training agents in simulation and then deploying them in the real world. So this isa nice setup for that because we have a simulator that’s very easy, fast and lightweight to train in, and then we have the environment that’s also really accessible. So, yeah, so we’ve been doing some research on that front.

So would you recommend Duckieown to colleagues or students of yours? And if yes, why. Of course. I think that’s what’s nice. Going back to the original motivation behind building Duckietowng and some of the tenets: thee guiding principle for us was this idea that to learn robotics, you have to get your hands on a robot. And we are also very adamant that it should be that every student should have their own robot. With teams of robots or going into the lab and only being able to use the robot at certain hours. It’s something funny.

You don’t develop the same kind of personal relationship. It sounds weird, but it’s true. Like when you have your own thing that you’re working with every day, you have some kind of bond with that thing, and you develop some kind of love or hate or whatever the case may be depending on how things are going on that particular day. So I think that with this set up, we have a platform where we’ve scaled things down and made things cost effective, to be able to do that. We built an engaging, experimental platform where it’s totally, I think, reasonable for most University budgets to be able to get their hands on the hardware.

"I believe that there's a lot of interesting research directions that come from a standardized, small scale, accessible autonomous driving platform like Duckietown."

Liam Paull

The other big piece is the actual teaching materials that we’ve developed. And I think that we have some good stuff. It could be better. Some stuff could be better, but that’s where we also need the community to come in. I mean, if we have this standardized platform and lots of people start using it and building educational experiences around the platform, then the entire thing just starts to get better and better for everybody. And it just grows into a very nice thing where you can also pick and choose the pieces that you want to include for your particular class, and you can customize the experience of what your class is going to look like using all of the resources that are out there. Also, the other part that I’ve really tried to cultivate, this is sort of a new thing. When we ran the first class at MIT, it was really an isolated thing. But in the subsequent iterations of the class, like myself and others have been in different places around the world, whether it’s Matt Walter at TTIC or Jacopo and Andrea at ETH. So we tried to turn the class into this kind of global experience, where you feel like you’re part of something that’s bigger than just the class that you’re taking at the specific University. And I think students really like that. We’ve experimented with different models where people do projects with other students from other universities or even just feeling part of the global community. I think it’s a very fun and engaging. Students are so connected these days. They’re so plugged in. They like this aspect of feeling like there’s a bit more of a broad social aspect, too. So I think these are some of the elements that this platform project experience brings to the table that I don’t see replicated and too many other setups.

Anything else you would like to add about Duckietown and it’s uses?

I didn’t mention specifically about the MOOC. One of the core missions of this project from the onset has been that it’s accessible. Both in terms of hardware but also in terms of software. And part of what that means to us Is that no matter where you are, no matter who you are, you should be able to get the hardware and you should be able to use the educational resources to learn. And part of the motivation for that Was that we saw that while we were at MIT. When you’re at a place like MIT you are extremely privileged and if you come from a background of less privilege, you see the discrepancy. In some sense, it’s palpable. Part of that, I guess, was that we don’t even necessarily want it to be a prerequisite that students should be enrolled in universities in order to be able to address the platform. So we built this massive online open source course through edx, which is also an open source provider Where people can, regardless of their background or regardless of their situation, they can sign up for this thing, and it’s a creative set of materials that also have exercises to interact with the robot that anybody can do, Regardless of whether they’re at a University or not.
I think this is the next step for us in making the platform accessible to all, and we’re going to continue to run iterations of this thing. But I also think that this is an exciting objective that very much fits in the mission of what we’re trying to do with this project.

This was great thank you for your time!

Awesome. Great. Thank you for your time. Bye.

Learn more about Duckietown

The Duckietown platform offers robotics and AI learning experiences.

Duckietown is modular, customizable and state-of-the-art. It is designed to teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge.

Tell us your story

Are you an instructor, learner, researcher or professional with a Duckietown story to tell? Reach out to us!

EdTech awards 2021: Duckietown finalist in 3 categories!

Posted on July 1, 2021 | by Ivano Marocchi

Duckietown reaches the finals in the EdTech Awards 2021

The EdTech awards are the largest and most competitive recognition program in all of education technology.

The competition, led by the EdTech digest, recognizes the biggest names in edtech – and those who soon will be, by identifying all over the world the products, services and people that bet promote education through the use of technology, for the benefit of learners.

The 2021 edition has brought a big surprise to Duckietown, as it was nominated as a finalist in 3 different categories:

Cool Tool Award: as robotics (for learning, education) solution;
Cool Tool Award: as higher education solution;
Trendsetter Award: as a product or service setting a trend in education technologies.

Although a final is just a starting point, we are proud of the hard work done by the team in this particularly difficult year of pandemic and lockdowns, and grateful to you all for the incredible support, constructive feedback and contributions!

(hidden) Want to learn more about us?

Duckietown and NVIDIA work together for accessible AI and robotics education: Meet the NVIDIA powered Duckiebot

Posted on October 6, 2020 | by Ivano Marocchi

Duckietown and NVIDIA partnership for accessible AI and robotics education

NVIDIA GTC, October 6, 2020: Duckietown and NVIDIA align efforts to push the boundaries of accessible, state-of-the-art higher-education in robotics and AI. The tangible outcome is a brand new “Founder’s edition” Duckiebot, which will be broadly available from January 2021, powered by the new NVIDIA Jetson Nano 2GB platform.

Read the full NVIDIA announcement here.

Meet the NVIDIA powered Duckiebot

Autonomy is already changing the world. Duckietown and NVIDIA recognize the importance of hands-on education in robotics and AI to empower everybody today to understand and design the next generations of autonomy.

The result of this collaboration is a new NVIDIA powered Duckiebot, using the novel Jetson Nano 2GB board, that will enable local execution of machine learning agents in the Duckietown ecosystem.

To celebrate this special occasion, the Duckiebot has been redesigned to include: new sensors (time of flight, IMU, encoders), a new custom-designed battery providing real time diagnostics (state of charge, remaining autonomy and other health metrics), and fun accessories like a screen to visualize key metrics. All of this while keeping the price accessible for anyone willing to experience the challenges of a real-life robotic ecosystem.

A great team

“The new NVIDIA Jetson Nano 2GB is the ultimate starter AI computer for educators and students to teach and learn AI at an incredibly affordable price.” said Deepu Talla, Vice President and General Manager of Edge Computing at NVIDIA. “Duckietown and its edX MOOC are leveraging Jetson to take hands-on experimentation and understanding of AI and autonomous machines to the next level.”

“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.”

Learn more

To know more about the technical specifications of the new NVIDIA powered Duckiebot, or to pre-order yours, visit the Duckietown project shop here.

The new Duckiebot will be also used in the “Self-driving Cars with Duckietown” Massive Online Open Course (MOOC) that will be held in March 2021 on edX. You can find more information about the MOOC here.