Category: Events
Upcoming events (meetings, conferences, etc.)
Massachusettes Institute of Technology: first BCI hackathon
Cambridge, MA, USA – McGovern Institute, 24-25 February 2024: Over 100 participants took part to the first MIT BCI hackathon, competing in teams to control Duckiebots using brain computer interfaces.
Controlling Duckiebots using brain computer interfaces
Over 100 participants gathered at the Massachusetts Institute of Technology for the first BCI hackathon organized by Dr. Federico Claudi. The participants tried to control a Duckiebot using only brain computer interfaces, and competed in a series of tasks.
BCI is the field of research that studies how to measure, amplify, filter and utilize electrical signals from the brain to interact with external devices.
What made this hackathon distinctive was the hands-on challenge, where participants were tasked with controlling a physical robot. This not only tested participants’ technical skills but also showcased their ability to tackle real-world problems through innovative BCI applications.
The task teams competed on was having Duckiebots (DB21-J4) navigate a road loop as fast as possible while avoiding Duckies. Here is an example:
The hardware used in this competition was an X.on EEG headset, and Duckiebots for control. Also, the winning team’s solution will be soon made available as a reproducible Learning Experience with Duckietown – stay tuned!
The Duckiebot is a DIY, Raspberry Pi-based robot powered by Nvidia and designed for introducing learners to autonomous technologies.
If you would like to contribute in developing accessible BCI LXs with Duckietown, and support the dissemination of BCI research, e.g. reach out to us at [email protected].
Learn more about Duckietown
The Duckietown platform enables state-of-the-art robotics and AI learning experiences.
It is designed to help teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of human knowledge.
Duckiedrone: how to fly a Raspberry Pi-based autonomous quadcopter
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
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.
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.
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.
AI Driving Olympics 2021: Urban League Finalists
AI Driving Olympics 2021 - Urban League Finalists
LF
), lane following with vehicles (LFV
) and lane following with intersections, (LFI
).
To account for differences between the real world and simulation, this edition finalists can make one additional submission to the real
challenges to improve their scores.
Finalists are the authors of AI-DO 2021 submissions in the top 5 ranks for each challenge.
This year’s finalists are: LF
- András Kalapos
- Bence Haromi
- Sampsa Ranta
- ETU-JBR Team
- Giulio Vaccari
LFV
- Sampsa Ranta
- Adrian Brucker
- Andras Beres
- David Bardos
LFI
- András Kalapos
- Sampsa Ranta
- Adrian Brucker
- Andras Beres
The deadline for submitting the “final” submissions is Dec. 9th, 2 pm CET. All submissions received after this time will count towards the next edition of AI-DO.
Don’t forget to join the #aido channel on the Duckietown Slack for updates!
Congratulations to all the participants, and best of luck to the finalists!
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.
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.
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!
“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!
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!
AI Driving Olympics 5th edition: results
AI-DO 5: Urban league winners
This year’s challenges were lane following (LF
), lane following with pedestrians (LFP
) and lane following with other vehicles, multibody (LFV_multi
).
Let’s find out the results in each category:
LF
- Andras Beres 🇭🇺
- Zoltan Lorincz 🇭🇺
- András Kalapos 🇭🇺
LFP
- Bea Baselines 🐤
- Melisande Teng 🇨🇦
- Raphael Jean 🇨🇦
LFV_multi
- Robert Moni 🇭🇺
- Márton Tim 🇭🇺
- Anastasiya Nikolskay 🇷🇺
Congratulations to the Hungarian Team from the Budapest University of Technology and Economics for collecting the highest rankings in the urban league!
Here’s how the winners in each category performed both in the qualification (simulation) and in the finals running on real hardware:
Andras Beres - Lane following (LF) winner
Melisande Teng - Lane following with pedestrians (LFP) winner
Robert Moni - Lane following with other vehicles, multibody (LFV_multi) winner
AI-DO 5: Advanced Perception league winners
Great participation and results in the Advanced Perception league! Check out this year’s winners in the video below:
AI-DO 5 sponsors
Many thanks to our amazing sponsors, without which none of this would have been possible!
Stay tuned for next year AI Driving Olympics. Visit the AI-DO page for more information on the competition and to browse this year’s introductory webinars, or check out the Duckietown massive open online course (MOOC) and prepare for next year’s competition!
AI-DO 5 competition leaderboard update
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
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
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 “Self-Driving cars with Duckietown” Massive Open Online Course on edX
"Self-Driving Cars with Duckietown" hands-on MOOC on edX
We are launching a massive open online course (MOOC): “Self-Driving Cars with Duckietown” on edX, and it is free to attend!
This course is made possible thanks to the support of 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.
This course combines remote and hands-on learning with real-world robots. It is offered on edX, the trusted platform for learning, and it is now open for enrollment.
Learning autonomy
Participants will engage in software and hardware hands-on learning experiences, with focus on overcoming the challenges of deploying autonomous robots in the real world.
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.
Pedestrian detection
MOOC Factsheet
- Name: Self-driving cars with Duckietown
- Start: March 2021
- Platform: edX
- Cost: free to attend
Prerequisites
- Basic Linux, Python, Git
- Elements of linear algebra, probability, calculus
- Elements of kinematics, dynamics
- Computer with native Ubuntu installation
What you will learn
- Computer Vision
- Robot operations
- Object Detection
- Localization, planning and control
- Reinforcement Learning
Why Self-driving cars with Duckietown?
Teaching autonomy requires a fundamentally different approach when compared to other computer science and engineering disciplines, because it is multi-disciplinary. Mastering it requires expertise in domains ranging from fundamental mathematics to practical machine-learning skills.
Robot Perception
Robots operate in the real world, and theory and practice often do not play well together. There are many hardware platforms and software tools, each with its own strengths and weaknesses. It is not always clear what tools are worth investing time in mastering, and how these skills will generalize to different platforms.
Duckiebot Detection
Learning through challenges
Progressing through behaviors of increasing complexity, participants uncover concepts and tools that address the limitations of previous approaches. This allows to get Duckiebots to actually do things, while gradually re-iterating concepts through various technical frameworks. Simulation and real-world experiments will be performed using a Python, ROS, and Docker based software stack.
Robot Planning
(Hidden) This line and everything under this line are hidden
This course combines remote and hands-on learning with real-world robots.
It is offered on edX, the trusted platform for learning, and it is now open for enrollment.
Learning activities will support the use of NVIDIA Jetson Nano powered Duckiebots.