Instance-wise Grasp Synthesis for Robotic Grasping

Yucheng Xu, Mohammadreza Kasaei, Hamidreza Kasaei, Alex Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publication2023 IEEE International Conference on Robotics and Automation (ICRA)
Number of pages7
ISBN (Electronic)9798350323658
ISBN (Print)9798350323665
Publication statusPublished - 4 Jul 2023
Event2023 IEEE International Conference on Robotics and Automation - London, United Kingdom
Duration: 29 May 20232 Jun 2023


Conference2023 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2023
Country/TerritoryUnited Kingdom
Internet address


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