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arXiv · 2202.09468

Sample Efficient Grasp Learning Using Equivariant Models

Xupeng Zhu·Dian Wang·Ondrej Biza·Guanang Su·Robin Walters·Robert Platt
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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 $\mathrm{SE}(2)$. In this paper, we recognize that the optimal grasp function is $\mathrm{SE}(2)$-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.

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Xupeng Zhu, Dian Wang, Ondrej Biza, Guanang Su, Robin Walters, Robert Platt. 2022-02-18. Sample Efficient Grasp Learning Using Equivariant Models. https://arxiv.org/abs/2202.09468

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