Tag: announcement

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

Posted on | 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
Advanced perception and racing leagues

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.

Get the LF hardware kit
A DB21 Duckietown in a Duckietown equipped with Autolab infrastructure.

Lane following with intersections 🚙 💻

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

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

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

Get the LFI hardware kit
Duckiebot facing a lane following with intersections (LFI) challenge

Lane following with vehicles 🚙 💻

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

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

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

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! 

Participate in AI-DO 2021
Get AI-DO hardware kit
Join our Slack

Thank you to our generous sponsors!

Amazon Web Services (AWS)

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

Posted on | 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!

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 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)

Get the LF kit

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)

Get the LFP kit

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)

Get the LFVM kit

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.

  • Nov. 9, 2020
ROS baseline

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

  • Nov. 11, 2020
Local development

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

  • Nov. 13, 2020
RL baseline

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

  • Nov. 16, 2020
IL baseline

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

  • Nov. 18, 2020
Synch Webinar Calendar

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.

Participate in AI-DO 5 now
Get your AI-DO hardware kit
Join our Slack Community

Thank you to our generous sponsors!

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

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

Posted on | by Ivano Marocchi

Duckietown and NVIDIA partnership for accessible AI and robotics education

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

Read the full NVIDIA announcement here.

Meet the NVIDIA powered Duckiebot

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

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

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

A great team

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

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

Learn more

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

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

Round 3 of the the AI Driving Olympics is underway!

Posted on | by Liam Paull

The AI Driving Olympics (AI-DO) is back!

We are excited to announce the launch of the AI-DO 3, which will culminate in a live competition event to be held at NeurIPS this Dec. 13-14.

The AI-DO is a global robotics competition that comprises a series of events based on autonomous driving. This year there are three events, urban (Duckietown), advanced perception (nuScenes), and racing (AWS Deepracer).  The objective of the AI-DO is to engage people from around the world in friendly competition, while simultaneously benchmarking and advancing the field of robotics and AI. 

Check out our official press release.

  • Learn more about the AI-DO competition here.

If you've already joined the competition we want to hear from you! 

 Share your pictures on facebook and twitter. 

Duckietown Workshop at RoboCup Junior

Posted on | by Jacopo Tani

Duckietown Workshop at RoboCup Junior

In collaboration with the RoboCup Federation, the Duckietown Foundation will be offering workshops at RoboCup 2019 in Sydney, Australia, providing a hands-on introduction to the Duckietown platform.

We will be hosting three one-day workshops as part of RoboCup 2019 from July 4-6, 2019  for teachers, students, and independent learners who are interested in finding out more about the Duckietown platform. Attendance is completely free and everyone is welcome to apply, even if you are not participating in RoboCup. There are no formal requirements, though basic familiarity with GNU/Linux and shell usage is recommended. 

If you would like to apply to attend a workshop, please complete this form. 

We will have Duckiebots and Duckietowns for participants to use. However, you are more than welcome to bring your own Duckiebots, available for purchase at https://get.duckietown.com. 

Congratulations to the winners of the second edition of the AI Driving Olympics!

Posted on | by Jacopo Tani

Team JetBrains came out on top on all 3 challenges

It was a busy (and squeaky) few days at the International Conference on Robotics and Automation in Montreal for the organizers and competitors of the AI Driving Olympics. 

The finals were kicked off by a semifinals round, where we the top 5 submissions from the Lane Following in Simulation leaderboard. The finalists (JBRRussia and MYF) moved forward to the more complicated challenges of Lane Following with Vehicles and Lane Following with Vehicles and Intersections. 

Results from the AI-DO2 Finals event on May 22, 2019 at ICRA

If you couldn’t make it to the event and missed the live stream on Facebook, here’s a short video of the first run of the JetBrains Lane Following submission.

Thanks to everyone that competed, dropped in to say hello, and cheered on the finalists by sending the song of the Duckie down the corridors of the Palais des Congrès. 

A few pictures from the event

Don't know much about the AI Driving Olympics?

It is an accessible and reproducible autonomous car competition designed with straightforward standardized hardware, software and interfaces.

Get Started

Step 1: Build and test your agent with our available templates and baselines

Step 2: Submit to a challenge

https://challenges.duckietown.com

Check out the leaderboard

View your submission in simulation

Step 3: Run your submission on a robot

https://get.duckietown.com/

in a Robotarium

AI-DO Robotarium Evaluations Underway

Posted on | by Liam Paull

We have started evaluating the submissions in our “robotarium”:

Duckiebot onboard camera feedRobotarium watchtower camera feed

To queue your submissions for robotarium evaluation, please follow these instructions:

You need to use the –challenge option to specify 3 challenges: the two simulated ones (testing and validation) and the hardware one:

  • dts challenges submit –challenge aido2-LF-sim-validation,aido2-LF-sim-testing,aido2-LF-real-validation
  • dts challenges submit –challenge aido2-LFV-sim-validation,aido2-LFV-sim-testing,aido2-LFV-real-validation
  • dts challenges submit –challenge aido2-LFV-sim-validation,aido2-LFVI-sim-testing,aido2-LFVI-real-validation

We will evaluate submissions by participants that are in the top part of the leaderboard in the simulated testing challenge.

The robotarium evaluations are limited, and we will do them in a round robin strategy for each user. We aim to evaluate all in the top 10 of the simulated challenge; and then more if there is the possibility.

Participants can have multiple submissions in the “real” challenges. We will evaluate first according to “user priority” or by most recent. The priority is settable through the web interface by using the top right button.

Deadlines

The challenges will close May 21 at 8pm Montreal (EDT) time. Please check the server timestamp for the precise time in your time zone.

Round 2 of the the AI Driving Olympics is underway!

Posted on | by Jacopo Tani

The AI-DO is back!

We are excited to announce that we are now ready to accept submissions for AI-DO 2, which will culminate in a live competition event to be held at ICRA 2019 this May 20-22.

The AI Driving Olympics is a global robotics competition that comprises a series of challenges based on autonomous driving. The AI-DO provides a standardized simulation and robotics platform that people from around the world use to engage in friendly competition, while simultaneously advancing the field of robotics and AI. 

Check out our official press release.

The finals of AI-DO 1 at NeurIPS, December 2018

We want to see your classical robotic and machine learning based algorithms go head to head on the competition track. Get started today!

Want to learn more or join the competition? Information and get started instructions are here.

If you've already joined the competition we want to hear from you! 

 Share your pictures on facebook and twitter. 

 Get involved in the community by:

asking for help

offering help

AI-DO 1 at NeurIPS report. Congratulations to our winners!

Posted on | by Jacopo Tani

The winners of AIDO-1 at NeurIPS

duckie-only-transparent

There was a great turnout for the first AI Driving Olympics competition, which took place at the NeurIPS conference in Montreal, Canada on Dec 8, 2018. In the finals, the submissions from the top five competitors were run from  five different locations on the competition track. 

Our top five competitors were awarded $3000 worth of AWS Credits (thank you AWS!) and a trip to one of nuTonomy’s offices for a ride in one of their self-driving cars (thanks APTIV!) 

2000px-Amazon_Web_Services_Logo.svg
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WINNER

Team Panasonic R&D Center Singapore & NUS

(Wei Gao)


Check out the submission.

The approach: We used the random template for its flexibility and created a debug framework to test the algorithm. After that, we created one python package for our algorithm and used the random template to directly call it. The algorithm basically contains three parts: 1. Perception, 2. Prediction and 3. Control. Prediction plays the most important role when the robot is at the sharp turn where the camera can not observe useful information.

2nd Place

Jon Plante


Check out the submission.

The approach:  “I tried and imitate what a human does when he follows a lane. I believe the human tries to center itself at all times in the lane using the two lines as guides. I think the human implicitly projects the two lines into the horizon and where they intersect is where the human directs the vehicle towards.”

 

3rd Place

Vincent Mai


Check out the submission.

The approach: “The AI-DO application I made was using the ROS lane following baseline. After running it out of the box, I noticed a couple of problems and corrected them by changing several parameters in the code.”

 

 

Jacopo Tani - IMG_20181208_163935

4th Place

Team JetBrains

(Mikita Sazanovich)


Check out the submission.

The approach: “We used our framework for parallel deep reinforcement learning. Our network consisted of five convolutional layers (1st layer with 32 9×9 filters, each following layer with 32 5×5 filters), followed by two fully connected layers (with 768 and 48 neurons) that took as an input four last frames downsampled to 120 by 160 pixels and filtered for white and yellow color. We trained it with Deep Deterministic Policy Gradient algorithm (Lillicrap et al. 2015). The training was done in three stages: first, on a full track, then on the most problematic regions, and then on a full track again.”

5th Place

Team SAIC Moscow

(Anton Mashikhin)


Check out the submission.

The approach: Our solution is based on reinforcement learning algorithm. We used a Twin delayed DDPG and ape-x like distributed scheme. One of the key insights was to add PID controller as an additional  explorative policy. It has significantly improved learning speed and quality

A few photos from the day