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

Exploring the skies at the summer Duckiedrone academy

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!

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.

Duckiedrone DD21

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.

Prof. Das- IIT Jaidpur

Robotics education and research at IIT Jodhpur: an interview with Prof. Debasis Das

IIT Jodhpur, June 03, 2023: Prof. Debasis Das of IIT Jodhpur, Rajasthan, India, tells us about his experience in bringing Duckietown to his lab to support the teaching, research, and outreach efforts of his team in the fields of autonomous vehicles, robotics, computer vision, machine learning.

Robotics education and research at IIT Jodhpur: an interview with Prof. Debasis Das

Hello Professor Das, thank you for your time in taking our questions. How did you learn about Duckietown?

I learned about the Duckietown project through a variety of sources, including online platforms like social media, discussion forums, and academic publications, where many researchers and robotics enthusiasts have shared information related to the project. Additionally, some of my collaborators have also mentioned the project and its educational and research goals. Through these sources, I gained an understanding of what the Duckietown project is and the impact it has had in the field of robotics education and research.

This is great to hear! Starting from the education aspects, what classes are you teaching at IIT Jodhpur using Duckietown?

Duckietown is being used for a variety of classes and educational programs, primarily in the fields of computer science and engineering at IIT Jodhpur. Most recently we have utilized the platform to teach the following classes:

  • Vehicular Ad Hoc Networks: This class focuses on the design, development, and testing of autonomous vehicles using the Duckietown platform to simulate real-world scenarios.
  • Robotics and Mobility: This class teaches students the principles of mobility and the role of machine learning for decision-making, using the Duckietown platform for practical exercises.
VANET lab team at IIT Jodhpur - Prof. Debasis Das

What has your and your students’ experience been in these courses? 

We have found that hands-on learning experiences, such as those provided by Duckietown, are more effective in increasing students’ knowledge and capacity to recall topics than standard lecture-based teaching approaches. 

Furthermore, the dynamic and fascinating elements unique to Duckietown increase students’ passion for and interest in their assignments.

We have received large positive feedback for Duckietown from students and researchers who have used it at IIT Jodhpur, with many appreciating its entertaining and challenging nature. The platform’s scalability and ease of use across a wide range of disciplines and programs are commendable. Students also appreciate the opportunity to learn programming and problem-solving with real-world robotics difficulties. 

Though there may be some disparities in how each student utilizes the platform and what they gain in terms of education and enjoyment, the data thus far suggests that Duckietown can be a helpful and fun resource for students working in robotics and related fields.

"The Duckietown platform has been a valuable resource in supporting our research activities. We have used it as a tool for engaging with the broader community and promoting interest in science, technology, engineering, and STEM fields, including hosting workshops, competitions, and other events that showcase the capabilities of autonomous vehicles and provide opportunities for hands-on learning and exploration."

Visit to Prof. Das Duckietown research lab at IIT Jodhpur

Are you using Duckietown to support your research activities too?

Yes, the Duckietown platform has been a valuable resource in supporting our research activities. We have used the platform to test and evaluate novel algorithms and methods for autonomous vehicle design and control, and to investigate issues such as computer vision, machine learning, and control systems.

Are there other ways Duckietown has helped you conduct teaching, research, and outreach efforts?

We have used Duckietown as a tool for engaging with the broader community and promoting interest in science, technology, engineering, and mathematics (STEM) fields. We have hosted workshops, competitions, and other events that showcase the capabilities of autonomous vehicles and provide opportunities for hands-on learning and exploration.

Lab visit to VANET@IITJ - outreach effort using Duckietown

Would you suggest Duckietown to your colleagues?

Yes, I would definitely recommend Duckietown to my colleagues who are interested in teaching and researching areas such as autonomous vehicles, robotics, computer vision, machine learning, and control systems.

Duckietown provides a realistic and scalable environment for testing and assessing novel algorithms and methods for autonomous vehicle design and control, as well as a platform for engaging students and the general public in learning and discovery in these domains.

"We have received large positive feedback for Duckietown from students and researchers who have used it at IIT Jodhpur, with many appreciating its entertaining and challenging nature. The platform's scalability and ease of use across a wide range of disciplines and programs are commendable."

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.

Tell us your story

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

Shima Akbari - La sapienza

Shima Akbari graduates with Duckietown at La Sapienza University in Rome

“La Sapienza” University of Rome, April 28, 2023: Shima Akbari, a Ph.D. student at the Italian National Program in Autonomous Systems, shares her experience working with Duckietown for her master’s thesis on lane following control for mobile robots.

Shima's work on control strategies for lane following

Hi, thank you for joining us today. Could you introduce yourself?

Certainly. My name is Shima Akbari, and I have a degree in Control Engineering from “La Sapienza” University of Rome. Currently, I am a Ph.D. student in the Italian National Program in Autonomous Systems.

You recently obtained your master’s degree, congratulations! You worked on lane following control approaches for mobile robots using Duckietown. How did you discover Duckietown?

I first learned about Duckietown while working on my master’s thesis. I have always been passionate about control and autonomy, particularly in mobile robots. However, I didn’t want to limit my project to theoretical calculations or computer simulations. I wanted to have a practical component to my work. When I shared this with my supervisor, Professor Oriolo, he introduced me to Duckietown and suggested that I conduct my experiments in this environment. So I implemented the lane following control in the Duckietown environment as part of my master’s thesis, using both the Duckietown simulator called Duckietown Gym, and the Duckiebots, which are the robots used in Duckietown. I thoroughly enjoyed every minute of conducting my tests.

It’s great to hear you enjoyed working with Duckiebots! Tell us a little more about your project and what was your experience like.

My thesis focus was on computer vision based control. I used OpenCV, the well known computer vision library, and the camera mounted on the Duckiebots to extract lane lines from the streets in Duckietown. Based on information extracted from these features, I implemented control laws that enabled the Duckiebot to drive on the streets inside lanes.

To familiarize myself with the platform, I started by taking the Duckietown massive open online course on edX and completed the assignments and homework on my own. One of the modules was about implementing a PID controller for lateral position and another on steering rate control. I enjoyed the Braitenberg vehicles activity too, but my favorite project was on obstacle avoidance, obstacle detection, and computer vision.

"In engineering, true learning comes from practical implementation, and Duckietown offers that opportunity effectively."

You are studying in a field that is statistically dominated by male presence. What are your thoughts on this?

It’s indeed the case. According to recent statistics, only 16% of women are in engineering compared to 84% of men. While I acknowledge this disparity, I believe that women are just as capable as men in engineering or any other field. Moreover, I think that the situation is improving over time. If we look at statistics from 10 to 20 years ago, the percentage of women in engineering was even lower.

What would you say to a young woman who wants to study engineering and may be discouraged by the statistics?

I would tell her that statistics and other people’s opinions should not deter her from pursuing her interest in engineering or any other subject. She should follow her dreams and not be discouraged by external factors.

 

Thank you for this thought, we hope this interview will help it reach as many women thinking about pursuing engineering careers out there as possible.

Absolutely. I would recommend Duckietown to anyone interested in learning about autonomous systems, regardless of their background or gender. It provides an excellent opportunity to learn about autonomy and control in a practical and user-friendly way. In engineering, true learning comes from practical implementation, and Duckietown offers that opportunity effectively.

What would you consider to be the unique value or appeal of Duckietown? What makes it special?

I would say that the simplicity of Duckietown is its most appealing aspect. The robots are designed to be simple and easy to use, and working with them is a lot of fun. Additionally, Duckietown has excellent support, with comprehensive documentation and manuals that are written in a detailed, step-by-step manner. Even if you don’t have a strong background in tools like Linux or Docker, you can still make progress by reading and following the documentation

"Duckietown is simple and has excellent support, with comprehensive documentation and manuals that are written in a detailed, step-by-step manner. Even if you don't have a strong background in concepts like Linux or Docker, you can still make progress by reading and following the documentation."

Thank you very much for taking the time to share your experience with us, we really appreciate it. Is there anything else you would like to add?

I’d just like to express how amazing it was for me to be introduced to and work with Duckietown. I would highly recommend it to others as well.

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.

Tell us your story

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

Lei Yang - University of Nevada

University of Nevada: Professor Lei Yang uses Duckietown based RET

University of Reno, March 7, 2023: Lei Yang, Associate Professor in the Department of Computer Science and Engineering of the University of Nevada in Reno shares with us his experience conducting a program called Research Experience for Teachers (RET) focused on “Integrating Big Data into Robotics”. 

Professor Lei Yang tells us about conducting a Duckietown based Research Experience for Teachers

Professor Lei Yang shares with us his relationship with Duckietown and how it performed used in a K-12 teachers research experience led by the Computer Science and Engineering department of the University of Nevada in Reno.

Good morning Professor and thank you for finding the time to speak to me.

Good morning, thank you.

How did you come across Duckietown the first time? When did you discover it?

Well, we needed a specific platform for our project, and a collaborator from Europe told us about this platform he was very familiar with. He let us know that it was a great platform, and that we should have a look at it. We accepted and ended up using Duckietown and suiting it to our project.

Could you tell us more about this project?

For three years now at the University of Nevada, Reno’s Computer Science and Engineering department (CSE) we’ve been conducting a program called Research Experience for Teachers (RET) focused on “Integrating Big Data into Robotics”. It’s a six-week course, through which participants can gain hands-on robotics experience that can be later applied in classrooms, in a fun way. The main idea is trying to provide a research experience to K-12 teachers. That’s why we proposed the idea of using Duckietown to teach K-12 teachers. We asked ourselves. what is the state of the art in terms of data analytics, machine learning? I think Duckietown is a very good education platform for teachers. We make use of the very good materials provided by Duckietown and I’m very satisfied with its implementation.
We purchased a Duckietown set for each participant and let them bring the hardware back to their school. Some teachers started their very own robotics clubs! They basically utilize that as an additional platform for their students.

What would you say are the characteristics of Duckietown that make it useful for you?

Our RET program involves all K-12 teachers, and one of the main goals of our program is to work with these teachers to develop curriculum modules suitable for their students. We have teachers from different levels, but we find that actually middle school and high school teachers are kind of more suited for this program. Duckietown is freely available and includes curricula that can be adapted for all levels of education. It is tangible, it is accessible,  and looks fun!

"I think Duckietown is a very good education platform for teachers. We make use of the very good materials provided by Duckietown and I’m very satisfied with its implementation."

It’s also easy to deal with. We can find all the materials online, and it is hands-on as I already mentioned. People like hands on activities, it’s good for kids. The duckies also serve to present robotics as less intimidating, making it easier to teach the harder, underlying concepts. I think that’s very nice: it can be used to teach optimization, control theory, these are fundamental things. I think this is a platform that can suit people with different levels of background and also an easy way to start one’s journey into robotics.

What does the future hold?

I think we’ve done a great job this year, and the teachers liked our project. I can see a significant improvement compared to the first year. This is a three-year project, so this year was the last for the program. After the program expired, we submitted another proposal to continue utilizing Duckietown, and to integrate new things like blockchain technology and other new ideas into this program so hopefully we’ll be using this platform in the future as well.

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.

Tell us your story

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

How Duckietown set “Frank” Chude Qian on the path to autonomous vehicles

University of Toronto, February 3, 2022: “Frank” Chude Qian, A Master Student at the University of Toronto, shares with us his experience with Duckietown.

How Duckietown set Chude (Frank) Qian on the road to autonomous vehicles

“Frank” Chude Qian is a Master’s student at the University of Toronto. He first encountered Duckietown at the International Conference on Robotics and Automation (ICRA) in 2019, and from that moment he decided that autonomous vehicles would have been his path forward.

Hello and thank you so much for having accepted to have this brief chat with us! Tell us about yourself and what you do.

Definitely! My name is Frank, and I’m currently a student at the University of Toronto. I’m in the second year of my program. I started as an Engineering Master’s student, and then I switched over to a Master of Science. My main focus is on developing the second generation of the University of Toronto’s autonomous vehicles that participate in the SAE Auto Drive Challenge. My work will end with the transition to the new vehicle, which will happen next year.

Thank you. Could you describe to us your first approach with Duckietown?

Yeah, definitely. So I was actually at ICRA 2019 wandering around and figuring out what to do with my life. I was taking part in another competition where I saw the Duckietown setup at the ICRA challenge [AI-DO 2]. It looked like great fun, I really loved the idea of how the project is and how it’s designed as a global initiative. You have people from different parts of the world trying to do the same thing, which I found inspiring.

Compared to the actual large-scale autonomous vehicles, Duckietown is an affordable option to learn mobility, and I really liked it. After I came back from ICRA, I just started looking at Duckietown and the AI Driving Olympics competition in more detail.

Frank Chude Qian and Jacopo Tani at Ai-DO 2, ICRA 2019

Nice! And how did that go?

In 2019, I started looking into what we could do with Duckiebots at my at Case Western Reserve University, where I was doing my undergrad studies. After learning about the AI-DO challenges, I was like, well, I’ll give it a try! It’s a challenge. I’m pretty competitive. So it took some trying and it ended up, I would say, good enough for where I was back then.

The other thing I looked into was Duckietown’s large code base for demonstrations because I was mainly working on answering the question: “what can you do with limited computing power for a system?”. I tested out the demos, and the Autolab idea, and tried to work on some improvements.

Back in 2019, there wasn’t a lot of work being done on that, unfortunately, but a good part of what was available had been tested, and the documentation was well-proofread. I then took over as a team lead for University of Toronto’s Autonomous Vehicle team, a role less involved with the project, but I still used Duckietown as a great introduction idea, of course.

We have a lot of students who are joining our team with almost no background in autonomous driving, and the Duckietown materials serve as a very good introduction idea to basically educate the younger students on the concept of autonomous vehicles.

Another thing I must say I learned a lot from is the Duckietown challenge and the evaluation side of the AI Driving Olympics, the evaluation server, and the idea of automated evaluation. I think I really gained a lot of experience and knowledge in testing and evaluating thanks to the Duckietown project.

Also along the way, I did another course project on a new baseline for the AI Driving Olympics or for what we call the conditional behavior cloning baseline. So that became another cross-project.

"It’s not only the cost-effectiveness but also the scalability of Duckietown and the potential it has to make a difference in key industrial sectors of the future."

It is great to hear that Duckietown helped you get comfortable with real self-driving cars. Is there anything else you would like to add?

Actually, yes. I know Duckietown is also planning on expanding its materials to the K-12 education side of things. I think that’s a great idea to get more students and younger folks excited about autonomous vehicles. And I think that one day autonomous vehicles will be more and more popular on the road, and the job market for developing and maintaining autonomous vehicles is going to be huge.

I really like the effort, and in fact, that’s probably something I’ll try to do: once I graduate from my current program, I’ll try to hop back on and further the development effort of expanding it to K-12 education.

Would you recommend Duckietown to students or colleagues?

Yeah, definitely! And I want to even sort of move it a step further.: for those students who want to get into autonomous vehicle research or development, but maybe their university doesn’t have much funding support for these kinds of programs, Duckietown is such a great project to just adopt.

You just start with the initial concept, and I’ve seen amazing research done thanks to Duckietown. I personally tried a couple of ideas, too. The one thing Duckietown can provide that nothing else can, as far as I can tell, at the same cost range is the development of multi-robot collaboration and the swarm robotics idea.

I think both of these features just provide great advantages for researchers and for students. You know, it’s a Jetson Nano plus some hardware. Or you can use the Raspberry Pi version. I think for students in the universities which don’t provide as much funding this could be a great starting point. And I personally learned so much throughout those projects, and ultimately that lead me to where I am today. So, yeah, definitely I would recommend it.

What would you say is the biggest quality of Duckietown?

I think from my experience with the AI-DO, it’s not only the cost effectiveness, but also the scalability of Duckietown and the potential it has to make a difference in key industrial sectors of the future.

Thank you very much!

Note from the editors: a few months after this interview, Frank started working as Software Simulation Developer at General Motors. Congratulations, Frank!

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.

Tell us your story

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

University of Nevada doing IoT with Duckietown

University of Nevada doing IoT with Duckietown

Here’s an extract from Nevada Today’s article “Integrating big data into robotics with Duckietown”, written by Kaeli Britt. 

For the third year, the University of Nevada, Reno’s Computer Science & Engineering (CSE) department conducted a Research Experience for Teachers (RET) program focused on “Integrating Big Data into Robotics.”

Through the six-week program, participants were able to gain hands-on robotics experience that can be applied in classrooms later, in a fun, nontraditional way.

Duckietown, an engineering and robotics/artificial intelligence (AI) project, focuses on accessible and engaging styles of learning. The project started at the Massachusetts Institute of Technology in 2016 as a graduate class, where they created a video “Duckumentary” highlighting the background and purpose of the research project but also its adaptability for varying age groups.

This year’s University project was taught by Ph. D. candidate and instructor Amirhesam Yazdi as well as CSE associate professor and principal investigator Lei Yang.

Participants were able to learn how to assemble the robots, build and design the track, and program the robots and the track.

“Duckietown is a freely available robotics platform and curricula for all levels of education. It is tangible, accessible, and fun. It has mobile robots and roads, constructed from exercise mats and tape,” Yang said. “The mobile robots are built from off-the-shelf parts and using open-source software and the curricula, such as lectures and exercises are provided on the Duckietown website. These unique features set Duckietown apart from other engineering, robotics and/or AI projects.”

Tell us your story

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

Introduction to robotics at the University of Massachusetts Lowell using Duckietown

University of Massachusetts, Lowell, December 20, 2022: Paul Robinette, Assistant Professor at the University of Massachusetts Lowell (UML), shares with us his Duckietown teaching experience.

Introduction to robotics at the University of Massachusetts Lowell using Duckietown

Paul Robinette is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Lowell. He shares his experience, and that of his students, using Duckietown for teaching throughout the years. His “Fundamentals of Robotics” (EECE 4560/5560) course with Duckietown platform has been repeating since 2019.
Paull Robinette and Duckietown

Thank you for finding the time to talk with us! Could you introduce yourself?

My name is Paul Robinette [Linkedin] and I’m a Professor of computer engineering at the University of Massachusetts Lowell [website].

When was your first experience with Duckietown? 

My first experience with Duckietown would have been when I worked at MIT as a research scientist, just after Duckietown was run. I didn’t have a chance to see it live there, but I did talk with several of the postdocs who worked on it as it ran. I also saw it live for the first time at ICRA 2019.

Do you use Duckietown or did you use Duckietown in the past for some specific project or activity?

Sure! For the last three years, I’ve been using Duckietown robots in my class every semester. Primarily I use the Duckiebots to teach ROS and basic robot skills through the Duckietown system and infrastructure. I leverage the development infrastructure heavily and some of the course materials as well.

That sounds great! Can you tell us more about your ongoing class?

The class I teach every semester so far is called Fundamentals of Robotics [2022 class page], and we go over the basics of robotics, starting with multi-agent processing or multi-process systems, like most robots are these days, some basic networking problems, etc. The Duckiebots are perfect for that because they have Docker containers on board which have multiple different networks running. They have to work with the computer system, so it’s always at least interfacing with the laptop. The robots can be used with a laptop, with a router, you can have multiple robots out at once, and they give the students a really good sense of what moving real robots around feels like. We have students start by implementing some open loop control systems, then have them design their own lane detector, similar to the Duckietown [perception] demo, and then have them design their own lane controller again, similar to the Duckietown [lane following] demo.

Are your students appreciating using Duckietown? Would you consider it a success?

Yes, especially the newer version. The DB21s are great robots for using their class applications and infrastructure. The software infrastructure has made it pretty easy to set up our own Git repositories for the robots and be able to run them. In this way, students can run them at home or on campus.

Would you suggest Duckietown to your students or colleagues?

Yes, I’d suggest Duckietown, especially if people want to run an introductory robotics class and have every student purchase their own robot, or have the University provide the robots for all. Duckietown is much more affordable than any other robot system that could be used for this same purpose.

"In my class we go over the basics of robotics, starting with multi-agent processing or multi-process systems, like most robots are these days, and the Duckiebots are perfect for that"

It is great to hear Duckietown addresses your needs so well. What would you say is the advantage that Duckietown has when compared to other systems?

I’d say the expense is probably the biggest advantage right now. It’s a nice platform and very capable for what we wanted to do. At this point, the fact that it’s affordable for students to purchase on their own or for us to purchase a bunch of them is definitely the biggest advantage for us. You guys also have a really quick response time if we have any problems. It’s nice to be able to talk directly with the development team and work with them to set up the systems so that I can run them in my class as I need to.

Thank you very much for your time!

Learn more about Duckietown

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

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!

Prof. Liam Paull - University de Montrèal

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

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.

LiamPaull-640x427

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."

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!

Vincenzo Polizzi - ETH

Learning Autonomy with Vincenzo Polizzi

ETHZ, Zurich, March 11, 2022: How Vincenzo discovered his true professional passion as a student using Duckietown.  

Learning Autonomy in practice with Vincenzo Polizzi

Vincenzo Polizzi studied robotics, systems and control at the Swiss Federal institute of Technology (ETH Zurich). Vincenzo shares below his experience with Duckietown. Starting off as a student, becoming a Teaching Assistant and onto how he uses Duckietown to power his own research as he moves from academia to industry.

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Could you tell us something about yourself?

I’m Vincenzo Polizzi , I studied automation engineering at the Politecnico di Milano, and I currently study robotics, systems and control at the Swiss Federal Institute of Technology (ETH Zurich).

You use Duckietown. Could you tell us when you first came into contact with the project, and what attracted you to Duckietown?

Sure! I learned about Duckietown my first year during a master’s program at ETH, where there was a course called: “Autonomous mobility on demand, from car to fleet” where I saw these cars, these robots. And I asked myself “what is this thing?”. It seemed very interesting. The first thing that struck me was that it did not look theoretical, but clearly practical.

"It captures you with simplicity and then you stay for the complexity."

So the idea of a practical aspect interested you?

Yes, during the presentation, it was clear that the course was based on projects the student had to carry out, where one could practice what they had learned theoretically in other classes.
I come from a scientific high school, and I studied automation engineering in Milan. In both my study experiences, I was used to learning concepts theoretically. For example, in the control system for a plant you design on paper in university, you don’t really face the complexity of implementing it on a real object.

I have to say that I have always been very passionate about robotics and informatics. In fact, even in high school, I was building these little robots,I participated in the robotics competition Rome Cup held by Fondazione Mondo Digitale, and there were these robots that were similar in shape to those of Duckietown, but where the scientific content was completely different. So in Duckietown, I saw something similar to what I was doing in my free time. I wanted to see exactly how it was inside, and there I discovered a whole other world that is obviously much more scientific than what a normal high school student could imagine by themself. However, initially I was curious to see a course where one can practice all the knowledge they have gradually acquired. It is not just about writing an equation and finding a solution but making things work.

What is your relationship with Duckietown, how long have you been using it? How do you interact with the Duckietown ecosystem? How do you use it, what do you do with it?

These are interesting questions because I started as a student and then managed to see what’s behind Duckietown. I was attending the course Duckietown held at ETH in 2019. The class was limited to 30 students, I was really excited to be part of it. I met many excellent students there, some of whom I am still in touch with today.

When I started the course, I immediately told myself, “Duckietown is a great thing. If all universities used Duckietown, this would be a better world.” I liked the class a lot, then I also had the opportunity of being a TA. The TAship was an important step because I learned more than during the course. One thing is to live the experience as a student who has to take exams, complete various projects, etc. You need a deeper understanding to organize an activity. You have to take care of all the details and foresee the parts of the exercise that can be harder or simpler for the students. This experience helped me a lot. For example, I did an internship in Zurich where we had to develop a software infrastructure for a drone, and I found myself thinking, “wow this can be done with Duckietown, we can use the same technologies.” I noticed that even in the industry, often we see the use of the same technologies and tools that you can learn about thanks to Duckietown. Of course, maybe a company has its own customized tools, probably well optimized for its products. Perhaps it uses some other specific tool but let’s say you already know more or less what these tools are about. You know because in Duckietown, you have already seen how a robotics system should work and the pieces it is composed of. Duckietown has given me a huge boost with the internship and my Master’s thesis at NASA JPL. Consider that my thesis was on a system of multi drones, so I used, for example, Docker as a tool to simulate the different agents. With Duckietown, I acquired technical knowledge that I used in many other projects, including work.

Do you still use it today?

The last project I did with Duckietown is DuckVision. I know we could have thought of a better name. With one of my Duckietowner friends, Trevor Phillips, we enhanced the Duckiebot perception pipeline with another camera, a stereocamera made by Luxonis and Open CV called OAKD (OpenCV AI Kit with depth). This sensor is not just a simple camera, but it also mounts a VPU, Visual Processing Unit. Namely, it can analyze and make inferences on the images that the camera acquires onboard. It can perform object detection and tracking, gesture recognition, semantic segmentation, etc. There are plenty of models freely available online that can run on the OAK-D. We have integrated this sensor in the Duckietown ecosystem, using a similar approach used in the MOOC “Self-Driving Cars with Duckietown”, we created a small series of tutorials where you can just plug the camera on the robot, run our Docker container and have fun! With this project, we passed the first phase of the OpenCV AI Competition 2021. The idea behind the project was to increase the Duckiebot understanding of the environment, by using the depth information, the robot can have a better representation of its surroundings and so, for example, a better knowledge of its position. Also, in our opinion, the OAK-D in Duckietown can boost the research in autonomous vehicles and perception.
I would like to add something about the use of Duckietown, I have seen this project both as a student and from behind the scenes and I really understood that by using this platform you really learn a lot of things that are useful not only in the academic field but can also be very useful in the working environment with the practical knowledge that is often difficult to acquire during school. And in this regard I thought then given my history, I am Sicilian but I studied in Milan and then I went to Zurich, I asked myself what can I bring as a contribution of my travels, so I thought about using Duckietown in some universities here in Sicily in the universities of Palermo and Messina. And also, at the Polytechnic of Milan, for example, they have already begun to use it and have participated in the AIDO and have also placed well, they ended up among the finalists, so there is a lot of interest in this project.

Did you receive a positive response every time you proposed Duckietown?

Yes, and then there is a huge enthusiasm on the part of the students. I spoke with student associations first, then with the professors etc. but when the students see Duckietown for the first time, they are always really enthusiastic about using it.

"There is something that captures you in some way, and then just opens up a world when you start to actually see how all the systems are implemented. This is the nice thing in my opinion, you can decide the level of complexity you want to achieve."

The duck was a great idea!

Absolutely right! The duck was a great idea, yes. I like contrasts, you see a super simple friendly thing that hides a state-of-the-art robotics platform. Even in the students I saw this reaction, because the duck is the first thing you see, it looks like a game, something to play with, this is the first impact, then when you start you get curious. It captures you with simplicity and then you stay for the complexity.

Would you suggest Duckietown to friends and colleagues?

Sure! There is something that captures you and opens up a world when you start to see how all the systems are implemented. This is the nice thing in my opinion, you can decide the level of complexity you want to achieve. It’s a platform that looks like something to play with, a game or something, but in reality there is a huge potential, in terms of knowledge that everyone can acquire, it’s something that you can not easily find elsewhere. I also think it offers great support, such as educational material, exercises that are of high quality. You can learn a lot of different aspects of robotics, in my opinion. You can do control, you can do the machine learning part, perception . There’s really a world to explore. You can see everything there is about robotics. But you can also just focus on one aspect that maybe you’re more passionate about. So yes, I would recommend it because you can learn a lot, and as a student myself I would recommend it to my fellow colleagues.

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!

Join the AI Driving Olympics, 6th edition, starting now!

The 2021 AI Driving Olympics

Compete in the 2021 edition of the Artificial Intelligence Driving Olympics (AI-DO 6)!

The AI-DO serves to benchmark the state of the art of artificial intelligence in autonomous driving by providing standardized simulation and hardware environments for tasks related to multi-sensory perception and embodied AI.

Duckietown traditionally hosts AI-DO competitions biannually, with finals events held at machine learning and robotics conferences such as the International Conference on Robotics and Automation (ICRA) and the Neural Information Processing Systems (NeurIPS). 

AI-DO 6 will be in conjunction with NeurIPS 2021 and have three leagues: urban driving, advanced perception, and racing. The winter champions will be announced during NeurIPS 2021, on December 10, 2021!

Urban driving league

The urban driving league uses the Duckietown platform and presents several challenges, each of increasing complexity.

The goal in each challenge is to develop a robotic agent for driving Duckiebots “well”. Baseline implementations are provided to test different approaches. There are no constraints on how your agents are designed.

Each challenge adds a layer of complexity: intersections, other vehicles, pedestrians, etc. You can check out the existing challenges on the Duckietown challenges server.

AI-DO 2021 features four challenges: lane following (LF), lane following with intersections (LFI), lane following with vehicles (LFV) and lane following with vehicles and intersections, multi-body, with full information (LFVI-multi-full).

All challenges have a simulation and hardware component (🚙,💻), except for LFVI-multi-full, which is simulation (💻) only.

The first phase (until Nov. 7) is a practice one. Results do not count towards leaderboards.

The second phase (Nov. 8-30) is the live competition and results count towards official leaderboards. 

Selected submissions (that perform well enough in simulation) will be evaluated on hardware in Autolabs. The submissions scoring best in Autolabs will access the finals.

During the finals (Dec. 1-8) one additional submission is possible for each finalist, per challenge.

Winners (top 3) of the resulting leaderboard will be declared AI-DO 2021 winter champions and celebrated live during NeurIPS 2021. We require champions to submit a short video (2 mins) introducing themselves and describing their submission.

Winners are invited to join (not mandatory) the NeurIPS event, on December 10th, 2021, starting at 11.25 GMT (Zoom link will follow).   

Overview
🎯Goal: develop robotic agents for challenges of increasing complexity
🚙Robot: Duckiebot (DB21M/J)
👀Sensors: camera, wheel encoders
Schedule
🏖️Practice: Nov. 1-7
🚙Competition: Nov. 8-30
🏘️Finals: Dec. 1 – 8
🏆Winners: Dec. 10
Rules
🏖️Practice: unlimited non-competing submissions
🚙Competition: best in sim are evaluated on hardware in Autolabs
🏘️Finals: one additional submission for Autolabs
🏆Winners: 2 mins video submission description for NeurIPS 2021 event.

The challenges

Lane following 🚙 💻

LF – The most traditional of AI-DO challenges: have a Duckiebot navigate a road loop without intersection, pedestrians (duckies) nor other vehicles. The objective is to travel the longest path in a given time while staying in the lane, i.e., not committing driving infractions.

Current AI-DO leaderboards: LF-sim-validation, LF-sim-testing.

Previous AI-DO leaderboards: sim-validation, sim-testing, real-validation.

A DB21 Duckietown in a Duckietown equipped with Autolab infrastructure.

Lane following with intersections 🚙 💻

LFI – This challenge builds upon LF by increasing the complexity of the road network, now featuring 3 and/or 4-way intersections, defined according to the Duckietown appearance specifications. Traffic lights will not be present on the map. The objective is to drive the longest distance while not breaking the rules of the road, now more complex due to the presence of traffic signs.

Current AI-DO leaderboards: LFI-sim-validation, LFI-sim-testing.

Previous AI-DO leaderboards: sim-validation, sim-testing.

Duckiebot facing a lane following with intersections (LFI) challenge

Lane following with vehicles 🚙 💻

LFV – In this traditional AI-DO challenge, contestants seek to travel the longest path in a city without intersections nor pedestrians, but with other vehicles on the road. Non-playing vehicles (i.e., not running the user’s submitted agent) can be in the same and/or opposite lanes and have variable speed.

Current AI-DO leaderboards: LFV-sim-validation, LFV-sim-testing.

Previous AI-DO leaderboards: (LFV-multi variant): sim-validation, sim-testing, real-validation.

Lane following with vehicles and intersections (stateful) 💻

LFVI-multi-full – this debuting challenge brings together roads with intersections and other vehicles. The submitted agent is deployed on all Duckiebots on the map (-multi), and is provided with full information, i.e., the state of the other vehicles on the map (-full). This challenge is in simulation only.

Getting started

All you need to get started and participate in the AI-DO is a computer, a good internet connection, and the ambition to challenge your skills against the international community!  

We provide webinars, operation manuals, and baselines to get started.

May the duck be with you! 

Thank you to our generous sponsors!