Learning for Agile Robotics

Learning for Agile Robotics Workshop at CoRL 2022

We're pleased to share that our workshop has been accepted as a full day workshop!

Scroll down for our call for papers!

We have seen tremendous progress and successes in applying learning to robotics over the last decade. Recently, learning based approaches have emerged for developing dynamic robots such as quadrupeds, ping-pong and drones. However, learning based robots are yet to demonstrate the capability to be as agile as humans or animals, like the traditionally nonlinear optimization based controls based agents have. Learning agile skills needs to overcome challenges such as modeling and adaptation in fast changing environments, low latency and high frequency perception and control, larger sim-to-real gap, need for wider safety margins, operating at hardware limitations and many more.

In this workshop, we plan to invite researchers working on making robots drive fast, run, fly, play sports, catch, juggle, etc. using machine learning to share their experience. The goals of the workshop include:

  • Fostering collaboration between a diverse group of researchers and practitioners working on a wide range of agile robotic domains (e.g. agile locomotion, ground vehicles, drones, ping-pong, catching, juggling, etc.).

  • Understanding the current limitations and inefficiencies in our methods and how modern ML architectures (Transformers, graph NNs and recurrent neural networks for example) and approaches such as generative modeling (e.g. GANs, VAEs, AR models, diffusion models, etc.), unsupervised learning, meta-learning, representation learning, domain adaptation, offline RL, etc. can be used to overcome the challenges in agility.

  • Learning common (or contrasting!) ML ideas across traditional robotics approaches and modern ML based approaches and as well as across diverse application domains; spanning various technical topics such as sensor fusion, algorithms (perception, learning, planning, state estimation, control) and systems and how it affects the agent learning.

  • Raising awareness amongst the robot learning community about the rich set of problems in learning for agile robotics.

Call for Papers

Learning based robots have yet to demonstrate the capability to be as agile as humans or animals. Learning agile skills necessitates overcoming challenges such as modeling and adaptation in fast changing environments, low latency and high frequency perception and control, large sim-to-real gaps, a need for wider safety margins, operating at hardware limitations and more.

In the CoRL 2022 Workshop on Learning for Agile Robotics, we invite researchers using machine learning to make robots move fast to share their research.

Topics of interest include but are not limited to:

  • Robots that run, jump, drive, fly, play sports, catch, juggle, dance, ....

  • Sensor fusion for fast movements

  • System design for agile robots

  • Algorithms (e.g. perception, learning, planning, state estimation, control)

  • Work that sheds light on current limitations and inefficiencies in learning for agile robotics and how modern ML architectures and approaches can be used to overcome these challenges.


Submission process

Submission website: OpenReview: CoRL Agility Workshop


Submissions should use the CoRL 2022 template and be 4 pages (plus as many pages as needed for references).
Following the main CoRL conference we use OpenReview and the review process will be
double blind.


Accepted papers and eventual supplementary material will be made available on the workshop website. However, this does not constitute an archival publication and no formal workshop proceedings will be made available, meaning contributors are free to publish their work in archival journals or conferences.


Note that we will accept submissions accepted to other conference or journal proceedings at the time of submission.


A note on demos: We encourage participants to also bring their robots for demos. Demo sessions are organized by the main CoRL committee. See https://corl2022.org/call-for-demos/ for additional information.


Review criteria

We will reject submissions that are not focused on agile robot learning.

Important Dates

Submissions opening: September 7, 2022
Demo application deadline (via main conference): September 20th, 2022
Submission deadline: October 16, 2022 (midnight AoE)
Notification of acceptance: October 28, 2022
Date of Workshop: December 15, 2022 (full day)

Invited Speakers


(alphabetical order)

Pulkit Agrawal - MIT

Heni Ben Amor - ASU

Johannes Betz - UPenn

David B. D’Ambrosio - Google

Ken Goldberg - UC Berkeley

Matthew Gomblay - Gatech

Laura Graesser - Google

David Howard - QUT

Scott Kuindersma - Boston Dynamics

Giuseppe Loianno - NYU

Jitendra Malik - UC Berkeley

Hae-Won Park - KAIST

Jan Peters - TU Darmstadt

Davide Scaramuzza - U Zurich

Angela Schoellig- U Toronto

Amirreza Shaban - U Washington

Guanya Shi - Caltech/CMU

Xuesu Xiao - GMU

Schedule (Tentative)

8:00-8:30: Breakfast

8:30-8:40: Intro to the workshop

8:40-9:10: Keynote #1

9:10-10:30: Short talk session #1

10:30-11:00: Coffee break, poster

11:00-11:30: Keynote #2

11:30-12:30: Panel discussion

12:30-13:30: Lunch break

13:30-14:00: Poster, demo

14:00-14:30: Main CoRL Opening session

14:30-15:00: Keynote #3

15:00-15:30: Coffee break, poster, demo

15:30-16:20: Short talk session #2

16:20-17:20: Brainstorm session

17:20-17:30: Closing remarks, announcements.

Evening: Swag, bar and party

Organizers


Alphabetical order, please contact the organizers at organizers@agilerobotscorl2022.com.


Anima Anandkumar - Caltech

Ken Caluwaerts - Google

Alejandro Escontrela - UC Berkeley / Google

Chuchu Fan - MIT

Ken Goldberg - UC Berkeley

Laura Graesser - Google

Atil Iscen - Google

Chase Kew - Google

Pannag Sanketi - Google

Davide Scaramuzza - University of Zurich

Jie Tan - Google

Sarah Tang - Waymo

Xuesu Xiao - George Mason University / Everyday Robots

Yuxiang Yang - University of Washington

Wenhao Yu - Google

Tingnan Zhang - Google