Scaling Robot Learning

With an increased adoption of thousands of robots in various industries comes the question of how to better deploy physical robots “at scale”. In this workshop, we are specifically interested in the challenges connected to “scaling” machine learning for robotic applications. This includes topics on both the algorithmic advancements (self-supervised learning, lifelong learning, multi-task learning, etc), and the systems side (data-acquisition at scale, distributed training, real-world challenges in deploying and maintaining a fleet of thousands of robots). The goal of this workshop is to bring together experts from different communities, industry and academia, to discuss challenges and explore new directions towards scaling robot learning.

The workshop will consist of invited talks, spotlight presentations, a poster session and  interdisciplinary panel discussions. We aim to improve the communication across a diverse set of scientists who are at various stages of their careers. Instead of the common trade-offs between attracting a wider audience with well-known speakers and enabling early-stage researchers to voice their opinion, we encourage each of our senior presenters to share their presentations with a PhD student or postdoc from their lab. We also ask all our presenters - invited and contributed - to add a “dirty laundry” slide, describing limitations of their work. We expect this will aid further discussion in poster and panel sessions in addition to helping junior researchers avoid similar roadblocks along their path.

The focus topics for our workshop include, but are not restricted to:

• Self-supervised learning for robotics, robot vision, reinforcement learning….

• Lifelong learning

• Multi-task learning

• Data acquisition at scale

• Multi robot systems / learning from fleets

• Large scale robotics applications and systems

• Scaling task specification via language/video conditioned policies

Thanks everybody for participating!

Poster Sessions Gather.Town:  https://app.gather.town/app/P6V9fyCw1AxuUGWu/Scaling%20Robot%20Learning                                                                                              Youtube Recordings: https://www.youtube.com/watch?v=EV0gSmmUN5c&list=PLkWItR7T--mfVd4nQy20_WxFmN17xzeOi&index=1

 Zoom:  https://zoom.us/j/4874613097?pwd=UWdrMHVNb1dQbEJGWU54cG40N2kwZz09

Submit your questions for the panel discussions here: https://forms.gle/rfm7fnDpSR31FsmY6

Friday,  27th of May, Easter Time

Room: 108B

8:30 - 8:35 Welcome organizers

8:35 - 9:00 Invited Talk 1 (Dieter Fox)

9:00 - 9:25 Invited Talk 2 (Andy Marchese)  

9:25 - 9:45 Spotlight Sessions

1. Equivariant Reinforcement Learning for Robotic Manipulation

2. Learning Perceptual Concepts by Bootstrapping from Human Queries

9.45 - 10:15 Contributed Papers Discussions

10:15 - 11:00 Coffee Break

11:00 - 11:25 Invited Talk 3 (Jeannette Bohg)

11:25 - 12:10 QA + Panel 1 (Dieter Fox, Andy Marchese, Jeannette Bohg)

13:00 - 13:25 Invited Talk 5 (Kendra Byrne)

13:25 - 13:50 Invited Talk 6 (Stephanie Tellex)

13:50 - 14:45 Spotlight Sessions:

1. Jump-Start Reinforcement Learning

2. Meta-Learning Regrasping Strategies for Physical-Agnostic Objects

3. Sim-to-Real Transfer for High-Speed Quadrotor Flight

4. Sample Efficient Grasp Learning Using Equivariant Models

5. Equivariant Transporter Network

6. R3M: A Universal Visual Representation for Robot Manipulation

7. SORNet: Spatial Object-Centric Representations for Sequential Manipulation

14:45 - 15:15 Contributed Papers Discussions

15:15 - 16:00 Coffee Break

16:00 - 16:25 Invited Talk 7 (Yuning Chai)

16:25 - 16:50 Invited Talk 8 (Sean Wilson)

16:50 - 17:35 QA + Panel 2 (Kendra Byrne, Stephanie Tellex, Yuning Chai, Sean Wilson)

17:35 - 17:45 Award & Concluding remarks

We encourage participants to submit their research in the form of a single PDF. Submissions may be up to 4 pages in length, including figures, excluding references and any supplementary material. Please use the ICRA conference template. Accepted papers will be presented in a poster session and selected papers as spotlight talks. All submitted contributions will go through a single blind review process. The contributed papers will be made available on the workshop’s 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 conference.

Google is sponsoring a Best Paper Award of $1,000 to be shared by all authors of the awarded paper.

Submission Website: https://cmt3.research.microsoft.com/ICRAWSRL2022

LaTeX Template: http://ras.papercept.net/conferences/support/tex.php