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:生成高质量的实例- 抓取配置( SSG) 合成网络, 在一个阶段进行高质量的实例- 抓取合成: 每个对象同时生成实例掩码和抓取配置。 我们的方法优于基于 OCID- Grasp 数据集的机器人抓取预测的最新水平, 并在 JACQUARD 数据集上以竞争方式进行 。 基准结果显示, 与生成的抓取配置精度基线相比, 有了显著的改进 。 拟议方法的性能通过广泛的模拟和真实的机器人实验来验证, 这三项任务包括: 单个对象的选取和定位, 在布满的环境中抓取合成, 以及清理表格任务 。