Learning Skills to Navigate without a Master: A Sequential Multi-Policy Reinforcement Learning Algorithm snippet

Learning Skills to Navigate without a Master: A Sequential Multi-Policy Reinforcement Learning Algorithm

General Information

Learning Skills to Navigate without a Master: A Sequential Multi-Policy Reinforcement Learning Algorithm

Duckietown Reinforcement Learning Paper - Sequential path planning
Reinforcement learning (RL) is a rising star approach for developing autonomous robot agents. The essence of RL is training agents based on policies that reward desirable outcomes, which leads to increased adaptability to operational scenarios. Through iterations, robots refine their decision-making, optimizing actions based on rewards and penalties. This method provides robots with the flexibility to handle unpredictable situations, enhancing their efficiency and effectiveness in real-world tasks. To learn about RL with Duckietown, check out the resources below.

Abstract

Solving complex problems using reinforcement learning necessitates breaking down the problem into manageable tasks, and learning policies to solve these tasks. These policies, in turn, have to be controlled by a master policy that takes high-level decisions. Hence learning policies involves hierarchical decision structures. However, training such methods in practice may lead to poor generalization, with either sub-policies executing actions for too few time steps or devolving into a single policy altogether. In our work, we introduce an alternative approach to learn such skills sequentially without using an overarching hierarchical policy. We propose this method in the context of environments where a major component of the objective of a learning agent is to prolong the episode for as long as possible. We refer to our proposed method as Sequential Soft Option Critic. We demonstrate the utility of our approach on navigation and goal-based tasks in a flexible simulated 3D navigation environment that we have developed. We also show that our method outperforms prior methods such as Soft Actor-Critic and Soft Option Critic on various environments, including the Atari River Raid environment and the Gym-Duckietown self-driving car simulator.

Highlights

Here is a visual tour of the work of the authors. 

For all the details, check out the paper link!

Conclusion

In this paper, the authors proposed an algorithm called “Sequential Soft Option Critic” that allows adding new skills dynamically without the need for a higher-level master policy. This can be applicable to environments where a primary component of the objective is to prolong the episode. We show that this algorithm can be used to effectively incorporate diverse skills into an overall skill set, and it outperforms prior methods in several environments.

Learn more

Duckietown is a platform for creating and disseminating 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.

Prof. Bernd Bruegge TUM - portrait

Successful failures: learning robotics with Prof. Bruegge

Prof. Bruegge and his colleagues about his experience using Duckietown and the new 4GB Duckiebots.

Munich, April 3rd: Prof. Bruegge (Brügge) studied computer science at the University of Hamburg and Carnegie Mellon University (CMU), where he also earned his doctorate. Appointed as Professor at the Technical University of Munich (TUM) in 1997, and also associate professor at CMU, Prof. Brügge was on the research committee of Deutsch Telekom and the Munich district. He has served on the board of directors of the Center for Digital Technology and Management, and acted as liaison professor for the Max Weber Foundation and the German National Academic Foundation (2000–2017). 

Failing successfully, an interview with Prof. Bruegge

Prof. Bernd Bruegge - Duckiebot - Crete

Prof. Bruegge and the JASS 2023 summer camp

Prof. Bruegge, Bernd, is an established computer scientist researcher and Professor, having (co)authored hundreds of peer reviewed publications with over 8800 citations at the time of this writing.

Emeritus Professor since 2021, Bernd regularly contributes to the organization and running of the Joint Advanced Student School (JASS)

In this interview we capture his experience from the summer 2022 edition. 

We are thrilled to have this chat with him! 

The Joint Advanced Student School (JASS) is a yearly summer retreat for advanced robotics studies held in Cyprus.
Good morning! Could you introduce yourself?

My name is Bernd Bruegge. I was a Full Professor of Computer Science at the Technical University in Munich, I was at Carnegie Mellon before I joined the Technical University. I was for 20 years at the Computer Science Department at CMU in Pittsburgh, Pennsylvania.

What can you tell us about this JASS school event that took place in Cyprus?

The recent [2023] school was quite an adventure! 

My colleague, Prof. Kirill Krinkin, was in St. Petersburg. We had done all the JASS projects with Kirill and support activities by JetBrains in St. Petersburg. And as you know, it’s a total disaster there, there’s nothing left.

So we organized this school in Cyprus with 10 students from Munich and 10 from Cyprus and St. Petersburg. And once there, we built an advanced Duckiebot system, with focus on context sensitivity.

We looked not only at intersections and turns, but we also had times when the Duckiebots would have to slow down, or stop at repair sites where we had traffic lights. There were not only OCR codes, we used Thread and Matter. Are you familiar with Thread and Matter?

I call this a "successful failure". The challenge in using advanced technology in the real world is that when you explore edge cases, unexpected situations may arise that one would not have considered by just simulating the same scenarios.

Is this a new functionality you added to the system?

Yes, this is actually an add-on we made to Duckietown. It’s a new IP standard that allows to save energy much, much more than Bluetooth. It’s actually better by a factor of 10. We added Thread and Matter capabilities to our Duckietown environment.

Screenshot
Duckiebots on table, student looking
That's fascinating! When did you first encounter Duckietown?

Well, I first heard of Duckietown a long time ago. As you know, I have been at Carnegie Mellon, which competed with MIT and Stanford. And when Kirill told me he would be a visiting professor at MIT, I asked: “What are you doing there?” And he said, “I’m using Duckietown!”

He was interested in robots and robotic technology and observed that this new technology is actually more like an environment and ecosystem than just a robot.

We then came up with a few ideas: first in St. Petersburg, we used a combination of drones and Duckiebots. Our scenario included two airports and a (duckie!) passenger. The drone had to pick up the duckie from one Duckiebot, and the students, divided into three teams, had to develop their own pickup mechanisms.   

Each of them with different approaches. One used magnets, the other one used scoops, and so on. They had to then transport the duckie from airport A to airport B using indoor navigation GPS. There was our first really impressive demo.

Credits: Special thanks to Andreas Jung and Ruth Demmel, and the multimedia team supporting Prof. Bruegge. 

Prof. Bruegge, do you feel you achieve your objectives?

I’d say we didn’t fulfill our technical objectives. The main challenge with Duckiebots was the third, omnidirectional, wheel, which had too much attrition. So, for instance, if we came to drive near a church, the idea would be to slow down the speed. But then we discovered it was difficult to control the Duckiebot at low speed, since when it comes down to a crawl, friction may cause it to stop. So operating at low speeds was not a practical possibility.

But I call this a “successful failure”. The challenge in using advanced technology in the real world is that when you explore edge cases, unexpected situations may arise that one would not have considered by just simulating the same scenarios.

I think the Duckiebot is good at lane-following and following traffic rules. When looking at context sensitivity though the challenges are trickier. So, e.g., we have a construction site with two traffic lights, how to coordinate them so to minimize, e.g., traffic? If you have one Duckiebot coming from one side and one coming from the other, then one traffic light should go green, and the other one should be red. 

But then we also have the idea that you have one traffic site that consists of multiple repair sites. In that situation the Duckiebots, lining up at the red traffic light, should have a green wave to go through each of the traffic lights until they exit the last traffic light.

So what we had in mind was coordination, making sure the Duckiebots were talking to each other. We used Python and the Duckiebots with 4GB NVIDIA Jetson Nanos, and this happened during a five-day course.

Is there anything else you would like to add?

We plan to prepare a special room for students from primary schools, to show them the interplay of autonomous driving and context-sensitive or ubiquitous computing. 

We would like to use the room in multiple ways. So we would like to have the Duckietown road sections, the tiles, interconnected in such a way that that allows us to switch between topographies basically in no time.

We have not been able to do this yet in an easy way, it’s always a scramble. Even if we use the same layout, the yellow tape is breaking or the white tapes are out of sync, and then we have to repair them.

So it would be great if we managed to keep the set up time for schools, especially for children (10 to 12 years), when doing exhibitions here, to under three minutes.

That's a great suggestion, thanks! Do you think Duckietown can be useful for younger learners too?

Yes, I think Duckietown can be very persuasive with young kids. They’re used to Lego or Fischer products, so when they meet the Duckiebot, that’s their first exposure to robots.

We actually have another project in mind, a series of Summer Schools. I’m a retired professor now, I don’t have to teach so I have time for Summer Schools! One will take place in the Dolomites, where the challenge will be a logistic one taking place in a factory. It will include robots moving packages around a warehouse.

Learn more about Duckietown

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

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

Tell us your story

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

Reach out to us!

Giulio Vaccari portrait - Politecnico di Milano

Spearheading Autonomy: Giulio Vaccari and the amazing AEA

Giulio Vaccari tells us how he spearheaded Duckietown at the Milan Polytechnic through the AEA student association, to support autonomy research and networking.

Milan, February 29th: Giulio Vaccari, master student at the time of this interview, tells us how and why he spearheaded the use of Duckietown at the Polytechnic of Milan, one of the most important Italian engineering universities.

Giulio Vaccari with colleagues using Duckietown in a classroom at the Polytechnic of Milan

Spearheading Autonomy: Giulio Vaccari and the amazing student Automation Engineering Association (AEA)

An interview with Giulio Vaccari from the Polytechnic University of Milan

Hello and welcome Mr. Giulio Vaccari! Could you introduce yourself?

My name is Giulio Vaccari, and I’m a master student at the Politecnico di Milano (“Polimi”). I am studying automation and control engineering and I am right now in the process of finishing my thesis with Duckietown. During my five years here, I founded the student Automation Engineering Association, focused on automation engineering. At the “Polimi” the course of control and automation engineer is not super big, now it’s getting bigger. More and more people are joining, but it’s still pretty small compared to, for example, computer science or other bigger courses. The idea of our association was to help with networking, help to understand what are future possible career paths as automation engineers. That’s why together with some friends we decided to found the association some years back.

Very interesting. How does Duckietown fit in all this?

It all started when I met and talked with Jacopo Tani and Vincenzo Polizzi I believe three years ago.

I had some friends, older friends, at ETH Zurich that introduced us to Duckietown as a possible project for our association, that at the time was still pretty small. We were newborn and we liked the idea of start working with Duckietown right away, because we were looking for a project that we could treat like a competition. It’s easier to get more people to know each other and network, when they have a common goal, as there are common topics to discuss.

I wanted something that would help us develop teamwork skills, something around which to build a strong team. Then this idea evolved a little bit and now we have a very focused and skilled team that works with aim of doing good at the Artificial Intelligence Driving Olympics (AI-DO). We did our first competition in 2021, in December, and it went well. Now we are improving. We are still working on it.

Giulio Vaccari with and members of the Automation Engineering student association at the Polytechnic of Milan
Giulio Vaccari and colleagues presenting Duckietown at the Polytechnic of Milan
Could you tell us more about your team's experience with the AI-DO competition?

Sure! We ended up being finalist, I’m very proud of that! In the meantime, I talked with some professors to try to get some help to improve what we were doing with Duckietown, to have some suggestions on what we should focus on. I was lucky enough to meet a Professor, now my thesis advisor, who had already heard about Duckietown and liked it. I was very happy that we were able to bring Duckietown to the Politecnico of Milano. Although the AI-DO are over now, I’m still doing related work, so I’m able to help the team do better while working on my thesis. Duckietown is now part of the laboratory for autonomous driving here at PoliMi, and I feel like managing to get Duckietown to be adopted by the Politecnico di Milano is a pretty big thing that I’m still proud of! 

And you should be! Do you use Duckietown also in the activities of the association?

When it comes to the association, we are focused on AI. We have a team of around 20 people working on different things. We use Duckietown also to do outreach when we have, for example, “associations days”, which are open days when students from high schools come and visit the University. That’s when we showcase Duckietown to show both what we do as an association but also what a control engineer is supposed to do, because it’s very difficult to explain otherwise.

What do you like about Duckietown? Why do you think it is useful for what you do?

Through Duckietown, it’s very easy to implement ideas in the real world. Usually if you need to build something like that from scratch, it will be super difficult. There are a lot of failure points, while with Duckietown we can just deploy it and it just works. This is super cool. We can also test a lot of different control strategies with very low effort. I mean, just the fact of writing the control algorithm, deploying it and it just works is huge. So this makes things super easy. Duckietown is very friendly and easy to use. It’s not something you often see in robotics, which is instead usually very complicated and intimidating. Yes, very intimidating. And Duckietown is even very friendly looking, and this is definitely a big plus!

Do you think people that are using Duckietown, are satisfied with it?

Yes, we had really positive feedback. I mean, everyone is really happy to work with Duckietown and we had so many people applying that we had to limit the team size because that would’ve made it very difficult to manage all those people. So I would say it had a very positive impact in general on the association. People are generally enthusiastic to just see the robots going around.

There are a lot of failure points in robotics, while with Duckietown we deploy our algorithms and it just works. This is super cool. We can test a lot of different control strategies with very low effort.

Vaccari and friends - Polimi
Would you suggest it to colleagues or other student associations?

Yes, for sure. But I see that it’s not only our team; also the Professor that I’m working with is very happy to be able to use it, also as a benchmark for different controls. There are other universities that have their very specific tracks to run their robots, while with Duckietown, it’s super easy to test like, the same strategy in different environments, but still with the same basics. This is a very positive thing also for the university.

Thank you very much for taking the time, we appreciated your story very much! Is there anything else you would like to add?

Right now I’m working on a different subject. So I’m using still Duckietown, but not in a city environment, but in a racing environment. My objective is to set up a racing track and have two robots compete against each other. They are still not competing, but we are getting close. It’s very nice that together with the Duckietown team, we are working on the urban environment, using different kind of materials to create these racing environments. I think it’s very cool.

There are also some issues every now and then. Maybe something that is not super easy to find, something that is very in depth, some APIs. If you want to use them, you need to dive deep. There is a support Slack channel though, and it’s super easy to ask for help. This too I believe is very nice and just adds to the positive experience.

Special Giulio Vaccari content!

We found in our archives the video submission of Giulio Vaccari and his team at AEA on the occasion of the 2021 AI Driving Olympics finals. In this fun, short, video team PoliMi explains the rationale and challenges faced in developing an ML-based agent for the 2021 competition!

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!

Professor Peter Affolter of BFH portrait

Driving autonomy education at BFH with Prof. Affolter

Bern, Switzerland: Prof. Peter Affolter, Head of the automotive department at Berner Fachhochschule (BFH), shares his experience creating a curriculum in autonomy education with Duckietown. Quick links Bern University of Applied Science Prof. Peter Affolter @ BFH Prof. Peter Affolter Driving autonomy

Bern, Switzerland: Prof. Peter Affolter, Head of the automotive department at Berner Fachhochschule (BFH), shares his experience creating a curriculum in autonomy education with Duckietown.

Students controlling Duckiebots at BFH

Driving autonomy education with Duckietown

Prof. Affolter at the Bern University of Applied Sciences introduces autonomy education in the automotive curriculum

Hi! Thank you for taking the time to chat with us. I'd like to start by asking you to introduce yourself, and tell us who you are and what you do.

Certainly! My name is Peter Affolter. I am at the head of the automotive department at the Bern University of Applied Science. I work as a lecturer in autonomous driving, vehicle communications, and related areas.

How did you get to know Duckietown?

The first time I saw it was probably two or three years ago. I came across it by chance while Googling on the internet. I was looking for information about lessons and classes on autonomous driving, a very complicated topic. I was searching for an easy platform, especially hardware, to simulate a simple vehicle. Then, I stumbled upon Duckietown. Initially, it looked a bit childish, but I was surprised by its concept, quality, and comprehensive documentation, including lessons slides for this complex theory. I decided to focus on Duckietown, especially on the massive online open course hosted on edX [Self-Driving Cars with Duckietown]. I also really enjoyed the great support from all the stakeholders and universities involved in this initiative.

Students learning robotics in at BFH with Duckietown
Did you find the "Self-Driving Cars with Duckietown", the massive open online course (MOOC) on edX, helpful?

Yes, at that time, there was only the archive available on edX.org, and I registered there. I couldn’t participate in real-time, but I went through the materials myself. I also contacted the Duckietown staff, and they provided me with support and missing documents. I adapted the content for my own class, focusing on bachelor-level students, parallel to the massive online open course from edX.

Very interesting, so you adapted the materials for your course. How did you set up the class, did you find any challenges?

It was clear to me that I could introduce our students, future automotive engineers, to autonomous driving. I wanted to give them an overview of the potential, limits, obstacles, and areas needing progress. Since they were not programmers or IT technologists, I needed to simplify it. I decided to conduct whole-class courses using video material from the MOOC. I adapted the quizzes and provided them with virtual machines, avoiding Linux and network issues. They could use the vehicles hosted on the BFH server farm through a browser terminal, even utilizing the Duckiebots. It became an infrastructure for exercises from simulation to reality, based on Duckietown classes.

How did the teaching experience go?

The first class had potential for improvement, but we adopted a flipped classroom approach with four lessons a week for 16 weeks. Students studied theory at home, and class time was for exercises, discussions, and additional explanations. The students enjoyed it, and while they didn’t grasp every detail, they felt proud and motivated. There’s still plenty of room for improvement in the next classes. They grasped the feeling and had the chance to work on neural networks, image manipulations, and more. I made good progress, and there’s still plenty of material to explore and implement in future classes.

Honestly, I haven't found another hardware platform as good as Duckietown for my needs. It's a simple platform with a fun approach, well-documented, and with a really reactive community. Even compared to other commercial products, Duckieown stands out. It fulfills all the needs from beginners to experts.

What were your overall impressions introducing autonomy education to your students with Duckietown?

Honestly, I haven’t found another hardware platform as good as Duckietown for my needs. It’s a simple platform with a fun approach, well-documented, and with a really reactive community. Even compared to other commercial products, Duckietown stands out. It fulfills all the needs from beginners to experts.

Thank you again for sharing your experience with us. Best of luck in your next classes!

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!

Duckietown Sky logo

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

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

Learning robot autonomy by flying with Duckietown Sky

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

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

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

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

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

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

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

Duckietown Duckiedrone model DD21 - happy yellow box
Duckietown Duckiedrone model DD21 - what's in the box?

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

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

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

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

Learn more about Duckietown

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

Felix Donoso Duckietown POD

Practical and playful: Duckietown talks with Prof. Donoso

Félix Donoso, Associate Professor at Duoc UC uses Duckietown to teach his students about the science and technology of autonomy in a practical and playful way.

Santiago de Chile, 17 October 2023: Learn how Félix Donoso H., Associate Professor at the School of Computer Science and Telecommunications at the Duoc UC Professional Institute, utilized the Duckietown platform to engage with students and teach them complex concepts in a practical and playful way.

Happy students use Duckietown

Practical and playful, an interview with Prof. Donoso

Hi! Could you introduce yourself?

My name is Félix Donoso H. I hold a Master’s degree in Education, specializing in Teaching for Higher Education. I am also an Engineer in Connectivity and Networks and have a Diploma in Applied Research and Innovation. I have been working in higher education for 8 years. Currently, I am an Associate Professor at the School of Computer Science and Telecommunications at the Duoc UC Professional Institute. I am involved in various projects at the Center for Innovation and Technology Transfer.

We read on the DUOC website about your Duckietown class, could you tell us a little more about it?​
At the Center for Innovation and Technology Transfer of the School of Computer Science and Telecommunications at DuocUC, we have recently implemented Duckietown to offer our students hands-on experience in robotics and autonomous vehicles. The workshops are designed to teach fundamental concepts of control, computer vision, and machine learning, using the Duckietown platform to provide a hands-on and cutting-edge yet accessible experience.
top view of duckiebots on a section of duckietown, rubber duckies scattered around, traffic signs
side view of duckiebots on a section of duckietown, rubber duckies scattered around, traffic signs
What is the pedagogical focus of your learning activities?
The pedagogical approach is and will be eminently practical and collaborative. We want students to not only understand theoretical concepts but also apply them in a real environment. Collaboration among students is essential, fostering teamwork and problem-solving in a practical context.
How did you learn about Duckietown?​
I learned about Duckietown during a robotics course at the University of Chile. The platform was presented as an innovative educational tool, and I was intrigued by its potential to teach complex concepts in an accessible and attractive way.
What is the thing you liked most about using Duckietown in your class?​
What I liked most about using Duckietown was the ability to bring abstract concepts to real life. Students were able to see how their algorithms and codes work in a tangible and entertaining environment, making the concepts easier to understand and more appealing.
How did your students react to the course?​

The students responded very positively.
The practical and playful nature of the course allowed them to actively engage
in learning, and many expressed that the experience with Duckietown has been
one of the most memorable in their education. 

Pablo Zapata, a fourth-year student of Computer Engineering and one of the project members, highlights that “my experience has been incredible, as I entered an area that is not part of our curriculum, and for this reason, we have learned to build robots and work with different components. This project has surprised me a lot because of the
technology we are using, and at the same time, it has greatly enriched us over
time.”

On the other hand, Néstor Carvacho, a second-year student in the Computer Programmer Analyst career, points out that “I have been able to work with cutting-edge technology and, with it, learn new work tools that are not part of my career; like Linux, which will help me
in my future work.”

Duckietown has allowed students from different campuses of the school of computer science and telecommunications at Duoc UC to immerse themselves in the world of robotics and autonomous vehicles, Linux, ROS, and Python, in an accessible and exciting way.

Felix donoso and two colleagues kneeling behind a duckietown setting (duckietown, duckiebots, traffic signs)
How would you recommend we improve the platform?
Although the platform is excellent, it could benefit from more resources and tutorials aimed at different levels of experience. It would also be helpful to have more examples of projects and applications in different fields to inspire students and showcase the versatility of the platform.
Would you like to add anything else?

I would just like to emphasize how valuable the Duckietown platform has been to our institution. It has allowed students from different campuses of the school of computer science and telecommunications at Duoc UC to immerse themselves in the world of robotics and autonomous vehicles, Linux, ROS, and Python, in an accessible and exciting way. I am eager to see how the platform evolves and how we can continue to use it in the future with the practical challenges we are preparing.

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!

Three girls watching Duckiebot - Science museum

Duckietown to join the UK Permanent Science Museum Collection!

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

Duckietown joins UK's Science Museum Group Collection

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

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

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

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

Duckietown in the history books

Joining the permanent collection will:

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

here are some details:

2023-216

E2019.0205.1

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

2023-222

E2019.0205.2

Traffic light kit from Duckietown Project, 2018-2019

2023-502

E2019.0205.3

City expansion pack from Duckietown Project, 2018-2019

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!

Ubuntu laptop terminal interface with hands operating keyboard, Duckiebot and duckies out of focus in foreground

“Self-Driving Cars with Duckietown” MOOC starting soon

Join the first hardware based MOOC about autonomy on edX!

Are you curious about robotics, self-driving cars, and want an opportunity to build and program your own? Set to start on March 22nd, 2020, “Self-Driving Cars with Duckietown” is a hands-on introduction to vehicle autonomy, and the first ever self-driving cars MOOC with a hardware track!

Designed for university-level students and professionals, this course is brought to you by the Swiss Federal Institute of Technology in Zurich (ETHZ), in collaboration with the University of Montreal, the Duckietown Foundation, and the Toyota Technological Institute at Chicago.

Learning autonomy requires a fundamentally different approach when compared to other computer science and engineering disciplines. Autonomy is inherently multi-disciplinary, and mastering it requires expertise in domains ranging from fundamental mathematics to practical machine-learning skills.

This course will explore the theory and implementation of model- and data-driven approaches for making a model self-driving car drive autonomously in an urban environment, while detecting and avoiding pedestrians (rubber duckies)!

In this course you will learn, hands-on, introductory elements of:

  • computer vision
  • robot operations 
  • ROS, Docker, Python, Ubuntu
  • autonomous behaviors
  • modelling and control
  • localization
  • planning
  • object detection and avoidance
  • reinforcement learning.

The Duckietown robotic ecosystem was created at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) in 2016 and is now used in over 90 universities worldwide.

“The Duckietown educational platform provides a hands-on, scaled down, accessible version of real world autonomous systems.” said Emilio Frazzoli, Professor of Dynamic Systems and Control, ETH Zurich, “Integrating NVIDIA’s Jetson Nano power in Duckietown enables unprecedented access to state-of-the-art compute solutions for learning autonomy.”

This massive online open course will be have a hands-on learning approach using, for the hardware track, real robots. You will learn how autonomous vehicles make their own decisions, going from theory to implementation, deployment in simulation as well as on the new NVIDIA Jetson Nano powered Duckiebots.

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

The Duckiebot MOOC Founder’s edition kits are available worldwide, and thanks to OKdo, are now available with free shipping in the United States and in Asia!

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

Enroll now and don’t miss the chance to join in the first vehicle autonomy MOOC with hands-on learning!

Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents

Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents

Why is this important?

As robotics matures and increases in complexity, it is more necessary than ever that robot autonomy research be reproducible.

Compared to other sciences, there are specific challenges to benchmarking autonomy, such as the complexity of the software stacks, the variability of the hardware and the reliance on data-driven techniques, amongst others.

We describe a new concept for reproducible robotics research that integrates development and benchmarking, so that reproducibility is obtained by design from the beginning of the research/development processes.

We first provide the overall conceptual objectives to achieve this goal and then a concrete instance that we have built: the DUCKIENet.

The Duckietown Automated Laboratories (Autolabs)

One of the central components of this setup is the Duckietown Autolab (DTA), a remotely accessible standardized setup that is itself also relatively low-cost and reproducible.

DTAs include an off-the-shelf camera-based localization system. The accessibility of the hardware testing environment through enables experimental benchmarking that can be performed on a network of DTAs in different geographical locations.

The DUCKIENet

When evaluating agents, careful definition of interfaces allows users to choose among local versus remote evaluation using simulation, logs, or remote automated hardware setups. The Decentralized Urban Collaborative Benchmarking Environment Network (DUCKIENet) is an instantiation of this design based on the Duckietown platform that provides an accessible and reproducible framework focused on autonomous vehicle fleets operating in model urban environments. 

The DUCKIENet enables users to develop and test a wide variety of different algorithms using available resources (simulator, logs, cloud evaluations, etc.), and then deploy their algorithms locally in simulation, locally on a robot, in a cloud-based simulation, or on a real robot in a remote lab. In each case, the submitter receives feedback and scores based on well-defined metrics.

Validation

We validate the system by analyzing the repeatability of experiments conducted using the infrastructure and show that there is low variance across different robot hardware and across different remote labs. We built DTAs at the Swiss Federal Institute of Technology in Zurich (ETHZ) and at the Toyota Technological Institute at Chicago (TTIC).

Conclusions

Our contention is that there is a need for stronger efforts towards reproducible research for robotics, and that to achieve this we need to consider the evaluation in equal terms as the algorithms themselves. In this fashion, we can obtain reproducibility by design through the research and development processes. Achieving this on a large-scale will contribute to a more systemic evaluation of robotics research and, in turn, increase the progress of development.

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