Efficient and robust grasp pose detection is vital for robotic manipulation. For general 6 DoF grasping, conventional methods treat all points in a scene equally and usually adopt uniform sampling to select grasp candidates. However, we discover that ignoring where to grasp greatly harms the speed and accuracy of current grasp pose detection methods. In this paper, we propose "graspness", a quality based on geometry cues that distinguishes graspable areas in cluttered scenes. A look-ahead searching method is proposed for measuring the graspness and statistical results justify the rationality of our method. To quickly detect graspness in practice, we develop a neural network named cascaded graspness model to approximate the searching process. Extensive experiments verify the stability, generality and effectiveness of our graspness model, allowing it to be used as a plug-and-play module for different methods. A large improvement in accuracy is witnessed for various previous methods after equipping our graspness model. Moreover, we develop GSNet, an end-to-end network that incorporates our graspness model for early filtering of low-quality predictions. Experiments on a large-scale benchmark, GraspNet-1Billion, show that our method outperforms previous arts by a large margin (30+ AP) and achieves a high inference speed. The library of GSNet has been integrated into AnyGrasp, which is at https://github.com/graspnet/anygrasp_sdk.

翻译：高效稳健的抓取姿态检测对机器人操作至关重要。在通用六自由度抓取任务中，传统方法对场景中的所有点进行同等处理，通常采用均匀采样来选取抓取候选。然而，我们发现忽略抓取位置会严重损害现有抓取姿态检测方法的速度与精度。本文提出“抓取性”——一种基于几何线索的质量度量，用于区分杂乱场景中的可抓取区域。我们提出一种前瞻搜索方法来度量抓取性，统计结果验证了该方法的合理性。为实现实际应用中的快速抓取性检测，我们开发了级联抓取性模型神经网络来近似该搜索过程。大量实验验证了抓取性模型的稳定性、泛化性与有效性，使其可作为即插即用模块应用于不同方法。在配备我们的抓取性模型后，多种现有方法的准确率均获得显著提升。此外，我们开发了GSNet端到端网络，该网络集成抓取性模型以早期过滤低质量预测。在大规模基准数据集GraspNet-1Billion上的实验表明，我们的方法以显著优势超越现有技术（提升超过30个AP点），同时实现较高的推理速度。GSNet代码库已集成至AnyGrasp项目，项目地址为https://github.com/graspnet/anygrasp_sdk。