Most state-of-the-art data-driven grasp sampling methods propose stable and collision-free grasps uniformly on the target object. For bin-picking, executing any of those reachable grasps is sufficient. However, for completing specific tasks, such as squeezing out liquid from a bottle, we want the grasp to be on a specific part of the object's body while avoiding other locations, such as the cap. This work presents a generative grasp sampling network, VCGS, capable of constrained 6 Degrees of Freedom (DoF) grasp sampling. In addition, we also curate a new dataset designed to train and evaluate methods for constrained grasping. The new dataset, called CONG, consists of over 14 million training samples of synthetically rendered point clouds and grasps at random target areas on 2889 objects. VCGS is benchmarked against GraspNet, a state-of-the-art unconstrained grasp sampler, in simulation and on a real robot. The results demonstrate that VCGS achieves a 10-15% higher grasp success rate than the baseline while being 2-3 times as sample efficient. Supplementary material is available on our project website.

翻译：当前最先进的数据驱动抓取采样方法通常在目标物体上均匀生成稳定且无碰撞的抓取。在零件拣选任务中，执行任意可达抓取即可满足需求。然而，对于完成特定任务（如从瓶中挤出液体），我们需要抓取位于物体特定部位（如瓶身）并避开其他区域（如瓶盖）。本文提出生成式抓取采样网络VCGS，能够实现受约束的六自由度（6-DoF）抓取采样。此外，我们还整理了一个用于训练和评估约束抓取方法的新数据集CONG，包含2889个物体上合成渲染的点云及随机目标区域抓取，共计超过1400万训练样本。我们在仿真环境和真实机器人上将VCGS与当前最先进的无约束抓取采样器GraspNet进行对比。结果表明，VCGS的抓取成功率比基线方法高10-15%，同时采样效率提升2-3倍。补充材料可见于项目官网。