SE(2)-Equivariant Pushing Dynamics Models for Tabletop Object Manipulations

Seungyeon Kim, Byeongdo Lim, Yonghyeon Lee, Frank C. Park
Proceedings of The 6th Conference on Robot Learning, PMLR 205:427-436, 2023.

Abstract

For tabletop object manipulation tasks, learning an accurate pushing dynamics model, which predicts the objects’ motions when a robot pushes an object, is very important. In this work, we claim that an ideal pushing dynamics model should have the SE(2)-equivariance property, i.e., if tabletop objects’ poses and pushing action are transformed by some same planar rigid-body transformation, then the resulting motion should also be the result of the same transformation. Existing state-of-the-art data-driven approaches do not have this equivariance property, resulting in less-than-desirable learning performances. In this paper, we propose a new neural network architecture that by construction has the above equivariance property. Through extensive empirical validations, we show that the proposed model shows significantly improved learning performances over the existing methods. Also, we verify that our pushing dynamics model can be used for various downstream pushing manipulation tasks such as the object moving, singulation, and grasping in both simulation and real robot experiments. Code is available at https://github.com/seungyeon-k/SQPDNet-public.

Cite this Paper

BibTeX
@InProceedings{pmlr-v205-kim23b, title = {SE(2)-Equivariant Pushing Dynamics Models for Tabletop Object Manipulations}, author = {Kim, Seungyeon and Lim, Byeongdo and Lee, Yonghyeon and Park, Frank C.}, booktitle = {Proceedings of The 6th Conference on Robot Learning}, pages = {427--436}, year = {2023}, editor = {Liu, Karen and Kulic, Dana and Ichnowski, Jeff}, volume = {205}, series = {Proceedings of Machine Learning Research}, month = {14--18 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v205/kim23b/kim23b.pdf}, url = {https://proceedings.mlr.press/v205/kim23b.html}, abstract = {For tabletop object manipulation tasks, learning an accurate pushing dynamics model, which predicts the objects’ motions when a robot pushes an object, is very important. In this work, we claim that an ideal pushing dynamics model should have the SE(2)-equivariance property, i.e., if tabletop objects’ poses and pushing action are transformed by some same planar rigid-body transformation, then the resulting motion should also be the result of the same transformation. Existing state-of-the-art data-driven approaches do not have this equivariance property, resulting in less-than-desirable learning performances. In this paper, we propose a new neural network architecture that by construction has the above equivariance property. Through extensive empirical validations, we show that the proposed model shows significantly improved learning performances over the existing methods. Also, we verify that our pushing dynamics model can be used for various downstream pushing manipulation tasks such as the object moving, singulation, and grasping in both simulation and real robot experiments. Code is available at https://github.com/seungyeon-k/SQPDNet-public.} }
Endnote
%0 Conference Paper %T SE(2)-Equivariant Pushing Dynamics Models for Tabletop Object Manipulations %A Seungyeon Kim %A Byeongdo Lim %A Yonghyeon Lee %A Frank C. Park %B Proceedings of The 6th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2023 %E Karen Liu %E Dana Kulic %E Jeff Ichnowski %F pmlr-v205-kim23b %I PMLR %P 427--436 %U https://proceedings.mlr.press/v205/kim23b.html %V 205 %X For tabletop object manipulation tasks, learning an accurate pushing dynamics model, which predicts the objects’ motions when a robot pushes an object, is very important. In this work, we claim that an ideal pushing dynamics model should have the SE(2)-equivariance property, i.e., if tabletop objects’ poses and pushing action are transformed by some same planar rigid-body transformation, then the resulting motion should also be the result of the same transformation. Existing state-of-the-art data-driven approaches do not have this equivariance property, resulting in less-than-desirable learning performances. In this paper, we propose a new neural network architecture that by construction has the above equivariance property. Through extensive empirical validations, we show that the proposed model shows significantly improved learning performances over the existing methods. Also, we verify that our pushing dynamics model can be used for various downstream pushing manipulation tasks such as the object moving, singulation, and grasping in both simulation and real robot experiments. Code is available at https://github.com/seungyeon-k/SQPDNet-public.
APA
Kim, S., Lim, B., Lee, Y. & Park, F.C.. (2023). SE(2)-Equivariant Pushing Dynamics Models for Tabletop Object Manipulations. Proceedings of The 6th Conference on Robot Learning, in Proceedings of Machine Learning Research 205:427-436 Available from https://proceedings.mlr.press/v205/kim23b.html.

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