Robotics: Science and Systems XVIII

Sample Efficient Grasp Learning Using Equivariant Models

Xupeng Zhu, Dian Wang, Ondrej Biza, Guanang Su, Robin Walters, Robert Platt

Abstract:

In planar grasp detection, the goal is to learn a function from an image of a scene onto a set of feasible grasp poses in SE(2). In this paper, we recognize that the optimal grasp function is SE(2)-equivariant and can be modeled using an equivariant convolutional neural network. As a result, we are able to significantly improve the sample efficiency of grasp learning, obtaining a good approximation of the grasp function after only 600 grasp attempts. This is few enough that we can learn to grasp completely on a physical robot in about 1.5 hours. Code is available at https://github.com/ZXP-S-works/ SE2-equivariant-grasp-learning.

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Bibtex:

  
@INPROCEEDINGS{Zhu-RSS-22, 
    AUTHOR    = {Xupeng Zhu AND Dian Wang AND Ondrej Biza AND Guanang Su AND Robin Walters AND Robert Platt}, 
    TITLE     = {{Sample Efficient Grasp Learning Using Equivariant Models}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2022}, 
    ADDRESS   = {New York City, NY, USA}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2022.XVIII.071} 
}