  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.
