This work addresses the problem of learning approach-constrained data-driven grasp samplers. To this end, we propose GoNet: a generative grasp sampler that can constrain the grasp approach direction to a subset of SO(3). The key insight is to discretize SO(3) into a predefined number of bins and train GoNet to generate grasps whose approach directions are within 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：本文研究了学习约束抓取方向的数据驱动抓取采样器问题。为此，我们提出GoNet：一种生成式抓取采样器，可将抓取逼近方向约束在SO(3)的子集内。关键思路是将SO(3)离散化为预定数量的区间，并训练GoNet生成逼近方向位于这些区间内的抓取姿态。在运行时，选择与观测点云的第二大主成分对齐的区间。GoNet与当前最先进的无约束抓取采样器GraspNet进行了基准对比，在仿真环境中的无约束抓取实验以及真实世界中的无约束和约束抓取实验中进行了测试。结果表明，与基线方法相比，GoNet在仿真中实现了更高的成功-覆盖率，在真实世界的桌面拾取和货架拾取任务中成功率提高了12%-18%。