Instance-wise Grasp Synthesis for Robotic Grasping

Yucheng Xu, Mohammadreza Kasaei, Hamidreza Kasaei, Zhibin Li

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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 2023)
Subtitle of host publicationProceedings
Number of pages7
ISBN (Print)979-8-3503-2365-8
Publication statusPublished - 4-Jul-2023
EventICRA 2023 - International Conference on Robotics and Automation - ExCeL London, London, United Kingdom
Duration: 29-May-20232-Jun-2023


ConferenceICRA 2023 - International Conference on Robotics and Automation
Abbreviated titleICRA
Country/TerritoryUnited Kingdom
Internet address


  • cs.RO

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