6-DoF object-agnostic grasping in unstructured environments is a critical yet challenging task in robotics. Most current works use non-optimized approaches to sample grasp locations and learn spatial features without concerning the grasping task. This paper proposes GraNet, a graph-based grasp pose generation framework that translates a point cloud scene into multi-level graphs and propagates features through graph neural networks. By building graphs at the scene level, object level, and grasp point level, GraNet enhances feature embedding at multiple scales while progressively converging to the ideal grasping locations by learning. Our pipeline can thus characterize the spatial distribution of grasps in cluttered scenes, leading to a higher rate of effective grasping. Furthermore, we enhance the representation ability of scalable graph networks by a structure-aware attention mechanism to exploit local relations in graphs. Our method achieves state-of-the-art performance on the large-scale GraspNet-1Billion benchmark, especially in grasping unseen objects (+11.62 AP). The real robot experiment shows a high success rate in grasping scattered objects, verifying the effectiveness of the proposed approach in unstructured environments.

基于图神经网络的GraNet框架在无结构环境下实现了针对6维自由度的无对象依赖抓取，通过建立点云场景的多级图并通过图神经网络传播特征，提高了多尺度下的特征嵌入能力，从而实现了高效抓取的空间分布特征建模。该方法在大规模的GraspNet-1Billion基准测试中取得了最先进的性能，尤其在抓取未见过的物体上的平均精准度提高了11.62个百分点，实际机器人实验也证明了该方法在无结构环境中对分散物体的高成功率。

GraNet: 多层次图网络在杂乱场景中的6自由度抓取姿态生成