Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection
Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection
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DOI：
10.1109/icra48891.2023.10160728
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Hao-zhe Huang;Dian Wang;Xu Zhu;R. Walters;Robert W. Platt
中科院分区：
文献类型：
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作者：
Hao-zhe Huang;Dian Wang;Xu Zhu;R. Walters;Robert W. Platt
在给定点云输入的情况下，6-DOF抓取姿态检测的问题是在SE(3)中识别一组能够成功抓取对象的手姿。这一重要问题有许多实际应用。在这里，我们提出了一种新的方法和神经网络模型，使得相对于文献中可用的成功率能够更好地掌握成功率。该方法以标准点云数据为输入，能很好地处理从任意观察方向观察到的单视点云。有关视频和代码，请访问https://haojhuang.github.io/edge_grasp_page/.
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. Videos and code are available at https://haojhuang.github.io/edge_grasp_page/.