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.

Round 3 of the the AI Driving Olympics is underway!

Posted on October 29, 2019 | 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.

Press Release AI-DO 3

Posted on October 29, 2019 | by Liam Paull | 1 Comment

[pdfviewer width=”600px” height=”849px” beta=”false”]https://www.duckietown.com/wp-content/uploads/2019/10/AIDO3-Press-release-EN.pdf[/pdfviewer]

AI-DO Robotarium Evaluations Underway

Posted on May 16, 2019 | by Liam Paull

Autolab evaluations underway

We have started evaluating the submissions in our Duckietown “Robotarium” (aka Autolab):

Duckiebot onboard camera feed

Robotarium 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.

Update to Dynamics Model in Duckietown Simulator

Posted on May 13, 2019 | by Liam Paull

We have implemented an improved dynamics model in the simulator. If you are using the simulator to:

Train your agent with reinforcement learning
Generate data for imitation learning
Test and debug your submission

then you may want to retrain/retest with the new dynamics model. This model is much closer to the true Duckiebot and should permit much easier transfer from simulation to the real robot hardware.

AI-DO I Interactive Tutorials

Posted on October 30, 2018 | by Liam Paull

The AI Driving Olympics, presented by the Duckietown Foundation with help from our partners and sponsors is now in full swing. Check out the leaderboard!

We now have templates for ROS, PyTorch, and TensorFlow, as well as an agnostic template.

We also have baseline implementation using the classical pipeline, imitation learning with data from both simulation and real Duckietown logs, and reinforcement learning.

We are excited to announce that we will be hosting a series of interactive tutorials for competitors to get started. These tutorials will be streamed live from our Facebook page.

See here for the full tutorial schedule.

AI Driving Olympics Ready for Submissions

Posted on October 14, 2018 | by Liam Paull

Submissions to the AI Driving Olympics are officially open for the Lane Following task evaluated in simulation.

To make a submission, follow the instructions in the AIDO-book.

For example, a good place to start is the Quick Start Guide.

For discussion please check out the AIDO forums.

Las Olimpiadas AI Driving en NIPS 2018

Posted on August 9, 2018 | by Liam Paull

Autores:

Andrea Censi Liam Paull, Jacopo Tani, Julian Zilly, Thomas Ackermann, Oscar Beijbom, Berabi Berkai, Gianmarco Bernasconi, Anne Kirsten Bowser, Simon Bing, Pin-Wei David Chen, Yu-Chen Chen, Maxime Chevalier-Boisvert, Breandan Considine, Andrea Daniele, Justin De Castri, Maurilio Di Cicco, Manfred Diaz, Paul Aurel Diederichs, Florian Golemo, Ruslan Hristov, Lily Hsu, Yi-Wei Daniel Huang, Chen-Hao Peter Hung, Qing-Shan Jia, Julien Kindle, Dzenan Lapandic, Cheng-Lung Lu, Sunil Mallya, Bhairav Mehta, Aurel Neff, Eryk Nice, Yang-Hung Allen Ou, Abdelhakim Qbaich, Josefine Quack, Claudio Ruch, Adam Sigal, Niklas Stolz, Alejandro Unghia, Ben Weber, Sean Wilson, Zi-Xiang Xia, Timothius Victorio Yasin, Nivethan Yogarajah, Yoshua Bengio, Tao Zhang, Hsueh-Cheng Wang, Matthew Walter, Stefano Soatto, Magnus Egerstedt, Emilio Frazzoli,

Publicado en RSS Workshop on New Benchmarks, Metrics, and Competitions for Robotic Learning

Link: Disponible aquí

Die AI-Fahrolympiade auf der NIPS 2018

Posted on August 7, 2018 | by Liam Paull

Autoren:

Veröffentlicht auf dem RSS-Workshop über neue Benchmarks, Metriken und Wettbewerbe für Robotisches Lernen.

Link: Verfügbar hier

The AI Driving Olympics at NIPS 2018

Posted on August 2, 2018 | by Liam Paull

General Information

The AI Driving Olympics at NIPS 2018
Andrea Censi Liam Paull, Jacopo Tani, Julian Zilly, Thomas Ackermann, Oscar Beijbom, Berabi Berkai, Gianmarco Bernasconi, Anne Kirsten Bowser, Simon Bing, Pin-Wei David Chen, Yu-Chen Chen, Maxime Chevalier-Boisvert, Breandan Considine, Andrea Daniele, Justin De Castri, Maurilio Di Cicco, Manfred Diaz, Paul Aurel Diederichs, Florian Golemo, Ruslan Hristov, Lily Hsu, Yi-Wei Daniel Huang, Chen-Hao Peter Hung, Qing-Shan Jia, Julien Kindle, Dzenan Lapandic, Cheng-Lung Lu, Sunil Mallya, Bhairav Mehta, Aurel Neff, Eryk Nice, Yang-Hung Allen Ou, Abdelhakim Qbaich, Josefine Quack, Claudio Ruch, Adam Sigal, Niklas Stolz, Alejandro Unghia, Ben Weber, Sean Wilson, Zi-Xiang Xia, Timothius Victorio Yasin, Nivethan Yogarajah, Yoshua Bengio, Tao Zhang, Hsueh-Cheng Wang, Matthew Walter, Stefano Soatto, Magnus Egerstedt, Emilio Frazzoli
ETH Zurich, Zurich, Switzerland
The Springer Series on Challenges in Machine Learning, https://doi.org/10.1007/978-3-030-29135-8_3

Learn more

Duckietown is a platform for creating and disseminating robotics and AI learning experiences.

It is modular, customizable and state-of-the-art, and designed to teach, learn, and do research. From exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge, Duckietown evolves with the skills of the user.