Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection

Haojie Huang; Dian Wang; Xupeng Zhu; Robin Walters; Robert Platt

Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection

Robotics 2022-11-02 v1 Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning

Authors: Haojie Huang , Dian Wang , Xupeng Zhu , Robin Walters , Robert Platt

Abstract

Given point cloud input, the problem of 6-DoF grasp pose detection is to identify a set of hand poses in SE(3) from which an object can be successfully grasped. This important problem has many practical applications. Here we propose a novel method and neural network model that enables better grasp success rates relative to what is available in the literature. The method takes standard point cloud data as input and works well with single-view point clouds observed from arbitrary viewing directions.

Keywords

robotic grasping point cloud pose estimation

Cite

@article{arxiv.2211.00191,
  title  = {Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection},
  author = {Haojie Huang and Dian Wang and Xupeng Zhu and Robin Walters and Robert Platt},
  journal= {arXiv preprint arXiv:2211.00191},
  year   = {2022}
}

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https://haojhuang.github.io/edge_grasp_page/

Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection

Abstract

Keywords

Cite

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