Workshop on Symmetries in Robot Learning

at RSS 2023 | July 10th | Daegu, Republic of Korea

Room 324A & Zoom


The recording of the workshop is available at


Robotics researchers have often leveraged geometric or physical structure in the world to make challenging planning and control problems easier. Recent work has applied this idea to robotic learning in order to improve sample efficiency and generalization. One approach is to use geometric deep learning by encoding symmetries in the structure of neural network model architectures. Geometric deep learning has had striking successes in molecular biology, computer vision, particle physics, and elsewhere. The question is how geometric deep learning methods can be employed in robotics.

The goal of the Symmetries in Robot Learning workshop is to bring together robotics researchers with researchers from the geometric deep learning community to discuss this question and shape the vision of the field of symmetry in robot learning. We will encourage researchers with experience in using symmetric learning in robotics to share ideas about how to leverage problem symmetries. At the same time, we will encourage researchers from the equivariant machine learning community to share their experiences in other fields that might inspire new approaches to leveraging structure in robotics.

Invited Speakers

Andy Zeng


He Wang

Peking University

Hao Su

UC San Diego

Tess Smidt


Maani Ghaffari

University of Michigan

Erik Bekkers

University of Amsterdam


09:00 am–09:05 am: Opening remarks

09:05 am–09:35 am: Invited talk, Tess Smidt

Intuition for Data Types and Interactions in Euclidean Symmetry-Equivariant Neural Networks

09:35 am–10:05 am: Invited talk, Hao Su

Learning Generalizable Policies by 3D Reinforcement Learning

10:05 am–10:25 am: Oral presentation 1

Robotic Manipulation Learning with Equivariant Descriptor Fields: Generative Modeling, Bi-equivariance, Steerability, and Locality

10:25 am–11:10 am: Coffee break + Poster session

11:10 am–11:40 am: Invited talk, Maani Ghaffari

A Tour of Recent Symmetry-Preserving Methods for Mobile Robots

11:40 am–12:00 pm: Panel discussion (Tess Smidt, Hao Su, Maani Ghaffari)

12:00 pm–01:30 pm: Lunch break

01:30 pm–02:00 pm: Invited talk, Andy Zeng

From the Symmetries in Words to Actions

02:00 pm–02:50 pm: Contributed paper presentations (5 mins each)

02:50 pm–03:40 pm: Coffee break + Poster session

03:40 pm–04:00 pm: Oral presentation 2

Progressive Learning for Physics-informed Neural Motion Planning

04:00 pm–04:30 pm: Invited talk, Erik Bekkers

Group Equivariant Deep Learning in Convolutional Form

04:30 pm–05:00 pm: Invited talk, He Wang

Title TBD

05:00 pm–05:20 pm: Panel discussion (Andy Zeng, Erik Bekkers, He Wang)

05:20 pm–05:30 pm: Closing remarks

Accepted Papers

Important Dates


Dian Wang

Northeastern University

Robert Platt

Northeastern University

Robin Walters

Northeastern University

Shuran Song

Columbia University

Elise van der Pol

Microsoft Research

Cheng Chi

Columbia University

Event Support

Event Support

Xupeng Zhu

Northeastern University

Program Committee Members

Dominik Bauer, Haojie Huang, Mingxi Jia, David Klee, Chuer Pan, Jung Yeon Park, Mengda Xu, Xiaomeng Xu, Xupeng Zhu

If you have any questions please contact us at rss-sym[at]googlegroups[dot]com.