Generating high-quality instance-wise grasp configurations provides critical information of how to grasp specific objects in a multi-object environment and is of high importance for robot manipulation tasks. This work proposed a novel \textbf{S}ingle-\textbf{S}tage \textbf{G}rasp (SSG) synthesis network, which performs high-quality instance-wise grasp synthesis in a single stage: instance mask and grasp configurations are generated for each object simultaneously. Our method outperforms state-of-the-art on robotic grasp prediction based on the OCID-Grasp dataset, and performs competitively on the JACQUARD dataset. The benchmarking results showed significant improvements compared to the baseline on the accuracy of generated grasp configurations. The performance of the proposed method has been validated through both extensive simulations and real robot experiments for three tasks including single object pick-and-place, grasp synthesis in cluttered environments and table cleaning task.

翻译：高质量地生成面向实例的抓取构型，为多物体环境中如何抓取特定物体提供了关键信息，对机器人操作任务具有重要意义。本文提出了一种新颖的**单阶段抓取**（SSG）合成网络，该网络在单一阶段内实现高质量的面向实例抓取合成：同时为每个物体生成实例掩码和抓取构型。基于OCID-Grasp数据集，我们的方法在机器人抓取预测任务上优于现有最先进方法，并在JACQUARD数据集上取得了具有竞争力的表现。基准测试结果表明，与基线方法相比，该方法在生成抓取构型的准确率上有显著提升。通过大量仿真实验和真实机器人实验，验证了所提方法在三个任务中的性能：包括单物体拾取与放置、杂乱环境中的抓取合成以及桌面清理任务。