Constraining the approach direction of grasps is important when picking objects in confined spaces, such as when emptying a shelf. Yet, such capabilities are not available in state-of-the-art data-driven grasp sampling methods that sample grasps all around the object. In this work, we address the specific problem of training approach-constrained data-driven grasp samplers and how to generate good grasping directions automatically. Our solution is GoNet: a generative grasp sampler that can constrain the grasp approach direction to lie close to a specified direction. This is achieved by discretizing SO(3) into bins and training GoNet to generate grasps from those bins. At run-time, the bin aligning with the second largest principal component of the observed point cloud is selected. GoNet is benchmarked against GraspNet, a state-of-the-art unconstrained grasp sampler, in an unconfined grasping experiment in simulation and on an unconfined and confined grasping experiment in the real world. The results demonstrate that GoNet achieves higher success-over-coverage in simulation and a 12%-18% higher success rate in real-world table-picking and shelf-picking tasks than the baseline.

翻译：在狭窄空间（如清理货架）中抓取物体时，约束抓取接近方向至关重要。然而，现有基于数据驱动的抓取采样方法通常在全物体周围采样抓取姿态，缺乏此类约束能力。本文针对如何训练接近方向约束的数据驱动抓取采样器，以及如何自动生成优质抓取方向这一特定问题展开研究。我们提出GoNet：一种能够将抓取接近方向约束在特定方向附近的生成式抓取采样网络。该方案通过将SO(3)空间离散化为多个区间，并训练GoNet从这些区间生成抓取姿态来实现约束。运行时，系统会选择与观测点云的第二大主成分方向对齐的区间。在无约束抓取仿真实验以及无约束/约束抓取真实世界实验中，我们将GoNet与当前最优的无约束抓取采样网络GraspNet进行基准对比。结果表明，GoNet在仿真中实现了更高的成功率-覆盖度指标，在真实世界的桌面抓取和货架抓取任务中，成功率较基线方法提升12%-18%。