The Duckietown path to robotics: an interview with Prof. Francesco Maurelli

Posted on July 4, 2022 | by Ivano Marocchi

Jacobs University, Bremen, June 1, 2022: Francesco Maurelli, professor at the Jacobs University of Bremen, talks about how Duckietown impacted his work and his academic career.

The Duckietown path to robotics: an interview with Prof. Francesco Maurelli

Francesco Maurelli, professor at the Jacobs University of Bremen, Germany, shares in the interview below his interaction with Duckietown.

Quick links

Let’s start by simply asking your name, who you are, where you work, what you do for a living.

Hi, Federico. Hi, everyone. I am Francesco Maurelli and I’m a professor in robotics at Jacobs University in Bremen.

When was the first time you came across Duckietown in your life? Describe your first contact with Duckietown for us.

Well, that was in my team. I was there as a Marie Curie scholar from Europe. I met Andrea and Liam, and learned about this new initiative. I was interested so I spoke in depth with them and with the students who took the course. I then looked at the videos and thought it was a great setup because it brings robotics closer to the students in a fun way by reducing the access barrier. Many people think that robotics is very hard, which is true. I’m not saying it’s easy, but on the other hand, there are easier paths to access robotics. Additionally the element of gamification makes people happy when they work with Duckietown. I found that students want to get involved regardless of the course work, they just like the concept.

Thank you. Is there a specific thing that maybe you did using Duckietown in your life, a project, a program or some sort of ecosystem?

We have had three different initiatives based on Duckietown.

The first one, called Jacobs Robotics, was an extracurricular activity for students. I would meet interested students outside of class time, it wasn’t linked to academic credit. It was just for fun and for learning. Among the different robotics platforms, we had a group working on Duckietown. This was the initial step in using Duckietown at our university.

Then the second step was to embed Duckietown in the official curriculum. We have a bachelor’s program in robotics and intelligence systems, and I’m its program manager. We were rewriting and updating some parts of it as we underwent a new wave of accreditation. So I took the opportunity to redesign some aspects of the program and in this process decided to embed Duckietown at Bachelor level. I’ve introduced it at Ross Lab in simulation in the fall of the second year, in the third semester, and then we have a robotics project based on Duckietown in the spring, ofthe second year, (i.e. in the fourth semester). That means that when students start their third year, they already have an understanding of ROS, they have knowledge of Duckietown and they work with real systems. This means that they can do a much better thesis, even if it’s a Bachelor level, we can improve the average level. When I joined the University, the first month of the thesis was lost on students learning to use Ross, for example. Now we are a step ahead.

The third part is the research application. It’s not only a matter of having fun with students, or delivering courses to students, but also doing my own research. I have a project which is funded by the German Research Foundation, DFG, it is in collaboration with the psychology Department. The psychologists want to look at the characteristics that humans assign to entities to identify the “self”. We as roboticists are going to develop and program different robotic behaviors, which the behavioral scientist from the psychology department will analyze. In a nutshell, we will prepare different videos illustrating the same actions performed in different ways. A very basic example would be moving in a city at constant speed or moving in a city at variable speeds. Our partners in the psychology department will show these videos to the study participants and collect user feedback through questionnaires to determine which behaviors they think are more alike a self determined behavior.

This is extremely interesting. Thank you very much. Have you ever heard about the MOOC course?

Yes, actually. In fact I suggested to our students to look at the MOOC. Of course, it is set up in a different way with respect to our course, but it can be and it has been a useful tool for students to review some of the material through a different viewpoint. So it’s definitely a valuable learning material which is available to the community.

“It’s not only a matter of having fun with students, or delivering courses to students, but also doing my own research.”

Francesco Maurelli

Okay. My last question is would you suggest Duckietown to other people, colleagues or your students? And why?

Absolutely. I see that from my own experience, students like it and they learn a lot about robotics. All the different concepts ranging from control to localization to computer vision can be applied in Duckietown. So in our projects, in our robotics projects, different groups of students develop different ideas. And I see that they are enjoying themselves and they are learning. So it’s definitely a plus.

Thank you very much.

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!

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.

Tell us your story

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

Learning Autonomy in practice with Vincenzo Polizzi

Posted on March 16, 2022 | by Ivano Marocchi

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.

Quick links

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."

Vincenzo Polizzi

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."

Vincenzo Polizzi

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.

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!

Posted on November 1, 2021 | by Ivano Marocchi

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.

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.

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.

Leaderboards: LFVI_multi_full-sim-validation

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!

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?

AI-DO 5 leaderboard update

Posted on December 9, 2020 | by Jacopo Tani

AI-DO 5 pre-finals update

With the fifth edition of the AI Driving Olympics finals day approaching, 1326 solutions submitted from 94 competitors in three challenges, it is time to glance over at the leaderboards!

Leaderboards updates

This year’s challenges are lane following (LF), lane following with pedestrians (LFP) and lane following with other vehicles, multibody (LFV_multi). Learn more about the challenges here. Each submission can be sent to multiple challenges. Let’s look at some of the most promising or interesting submissions.

The Montréal menace

Raphael Jean at Mila / University of Montréal is a new entrant for this year.

An interesting submission: submission #12962

All of raph’s submissions.

The submissions from the cold

Team JetBrains from Saint Petersburg was a winner of previous editions of AI-DO. They have been dominating the leaderboards also this year.

Interesting submissions: submission #12905

All of JetBrains submissions: JBRRussia1.

BME Conti

PhD student Robert Moni (BME-Conti) from Hungary.

Interesting submissions: submission #12999

All submissions: timur-BMEconti

Deadline for submissions

The deadline for submitting to the AI-DO 5 is 12am EST on Thursday, December 10th, 2020. The top three entries (more if time allows) in each simulation challenge will be evaluated on real robots and presented at the finals event at NeurIPS 2020, which happens at 5pm EST on Saturday, December 12.

Join the AI Driving Olympics now!

Test your skills in the urban league challenges and qualify for the finals at NeurIPS!

AI-DO 5

AI-DO 5 Update

Posted on December 5, 2020 | by Andrea Censi

AI-DO 5 Update

AI-DO 5 is in full swing and we want to bring you some updates: better graphics, more maps, faster and more reliable backend and an improved GUI to submit to challenges!

Challenges visualization

We updated the visualization. Now the evaluation produces videos with your name and evaluation number (as below).

Challenges updates

We fixed some of the bugs in the simulator regarding the visualization (“phantom robots” popping in and out).

We updated the maps in the challenges to have more variety in the road network; we put more grass and trees to make the maps more joyful!

Faster and more reliable backend

The server was getting slow given the number of submissions, and sometime the service was unavailable. We have revamped the server code and added some backend capacity to be more fault-tolerant. It is now much faster!

Thanks so much to the participants that helped us debug this problem!

More evaluators

We brought online many more CPU and GPU evaluators. We now encourage you to submit more often as we have a lot more capacity.

Submit to testing challenges

We also remind you that the challenges on the front page are the validation challenges, in which everybody can see the output. However what counts for winning are the testing challenges!

To do that you can use dts challenges submit with the –challenges option

Or, you can use a new way using the website that we just implemented, described below.

Submitting to other challenges

Step 1: Go to your user page, by clicking “login” and then going to “My Submissions”.

Step 2: In this page you will find your submissions grouped by “component”.

Click the component icon as in the figure.

Step 3: The page will contain some buttons that allow you to submit to other challenges that you didn’t submit to yet.

Join the AI Driving Olympics, 5th edition, starting now!

Posted on November 5, 2020 | by Ivano Marocchi

Compete in the 5th AI Driving Olympics (AI-DO)

The 5th edition of the Artificial Intelligence Driving Olympics (AI-DO 5) has officially started!

Duckietown 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).

The AI-DO 5 will be in conjunction with NeurIPS 2020 and have two leagues: Urban Driving and Advanced Perception.

Urban driving league challenges

This year’s Urban League includes a traditional AI-DO challenge (LF) and introduces two new ones (LFP, LFVM).

Lane Following (LF)

The most traditional of AI-DO challenges: have a Duckiebot navigate a road loop without intersection, pedestrians (duckies) or other vehicles. The objective is traveling the longest path in a given time while staying in the lane.

Lane following with Pedestrian (LFP)

The LFP challenge is new to AI-DO. It builds upon LF by introducing static obstacles (duckies) on the road. The objectives are the same as for lane following, but do not hit the duckies!

Lane Following with Vehicles, multi-body (LFVM)

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. Except this year there’s a twist. In this year’s novel multi-body variant, all vehicles on the road are controlled by the submission.

Getting started: the webinars

We offer a short webinar series to guide contestants through the steps for participating: from running our baselines in simulation as well as deploying them on hardware. All webinars are 9 am EST and free!

Introduction

Learn about the Duckietown project and the Artificial Intelligence Driving Olympics.

ROS baseline

How to run and build upon the “traditional” Robotic Operation System (ROS) baseline.

Local development

On the workflow for developing and deploying to Duckiebots, for hardware-based testing.

RL baseline

Learn how to use the Pytorch template for reinforcement learning approaches.

IL baseline

Introduction to the Tensorflow template, use of logs and simulator for imitation learning.

Advanced sensing league challenges

Previous AI-DO editions featured: detection, tracking and prediction challenges around the nuScenes dataset.

For the 5th iteration of AI-DO we have a brand new lidar segmentation challenge.

The challenge is based on the recently released lidar segmentation annotations for nuScenes and features an astonishing 1,400,000,000 lidar points annotated with one of 32 labels.

We hope that this new benchmark will help to push the boundaries in lidar segmentation. Please see https://www.nuscenes.org/lidar-segmentation for more details.

Furthermore, due to popular demand, we will organize the 3rd iteration of the nuScenes 3d detection challenge. Please see https://www.nuscenes.org/object-detection for more details.

AI-DO 5 Finals event

The AI-DO finals will be streamed LIVE during 2020 edition of the Neural Information Processing Systems (NeurIPS 2020) conference in December.

Learn more about the AI-DO here.

Thank you to our generous sponsors!

The Duckietown Foundation is grateful to its sponsors for supporting this fifth edition of the AI Driving Olympics!

IROS2020: Watch The Workshop on Benchmarking Progress in Autonomous Driving

Posted on October 28, 2020 | by Ivano Marocchi

What a start for IROS 2020 with the "Benchmarking Progress in Autonomous Driving" workshop!

The 2020 edition of the International Conference on Intelligent Robots and Systems (IROS) started great with the workshop on “Benchmarking Progress in Autonomous Driving”.

The workshop was held virtually on October 25th, 2020, using an engaging and concise format of a sequence of four 1.5-hour moderated round-table discussions (including an introduction) centered around 4 themes.

The discussions on the methods by which progress in autonomous driving is evaluated, benchmarked, and verified were exciting. Many thanks to all the panelists and the organizers!

Here are the videos of the various sessions.

Opening remarks

Theme 1: Assessing progress for the field of autonomous vehicles (AVs)

Moderator: Andrea Censi

Invited Panelists:

Emilio Frazzoli (ETH Zürich / Motional)
Alex Kendall (Wayve)
Jane Lappin (Chair, Transportation Research Board Standing Committee on Vehicle-Highway Automation)
Atul Acharya (Director, AV Strategy at AAA Northern California)

Theme 2: How to evaluate AV risk from the perspective of real world deployment (public acceptance, insurance, liability, …)?

Moderator: Jacopo Tani

Invited Panelists:

Bryant Walker Smith (USC Faculty of Law)
Luigi Di Lillo (Swiss Re Insurance)
John Leonard (MIT)

Theme 3: Best practices for AV benchmarking

Moderator: Liam Paull

Invited Panelists:

Fabio Bonsignorio (Heron Robots)
Michael Milford (QUT)
Oscar Beijbom (Motional)
Marcelo H. Ang Jr (National University of Singapore)

Theme 4: Do we need new paradigms for AV development?

Moderator: Matt Walter

Invited Panelists:

Raquel Urtasun (U of Toronto / Uber ATG)
Edwin Olson (May Mobility)
Ram Vasudevan (University of Michigan)
Sertac Karaman (MIT / Optimus Ride)

Closing remarks

You can find additional information about the workshop here.

The Workshop on Benchmarking Progress in Autonomous Driving at IROS 2020

Posted on October 21, 2020 | by Liam Paull

The IROS 2020 Workshop on Benchmarking Autonomous Driving

Duckietown has also a science mission: to help develop technologies for reproducible benchmarking in robotics.

The IROS 2020 Workshop on Benchmarking Autonomous Driving provides a platform to investigate and discuss the methods by which progress in autonomous driving is evaluated, benchmarked, and verified.

It is free to attend.

The workshop is structured into 4 panels around four themes.

Assessing Progress for the Field of Autonomous Driving
How to evaluate AV risk from the perspective of real world deployment (public acceptance, insurance, liability, …)?
Best practices for AV benchmarking
Algorithms and Paradigms

The workshop will take place on Oct. 25, 2020 starting at 10am EDT.

Invited Panelists

We have a list of excellent invited panelists from academia, industry, and regulatory organizations. These include:

Emilio Frazzoli (ETH Zürich / Motional)
Alex Kendall (Wayve)
Jane Lappin (National Academy of Sciences)
Bryant Walker Smith (USC Faculty of Law)
Luigi Di Lillo (Swiss Re Insurance),
John Leonard (MIT)
Fabio Bonsignorio (Heron Robots)
Michael Milford (QUT)
Oscar Beijbom (Motional)
Raquel Urtasun (University of Toronto / Uber ATG).

Please join us...

Please join us on October 25, 2020 starting at 10am EST for what should be a very engaging conversation about the difficult issues around benchmarking progress in autonomous vehicles.

For full details about the event please see here.