Xu, Y; Kasaei, M; Kasaei, H; Li, Z; (2023) Instance-wise Grasp Synthesis for Robotic Grasping. In: Proceedings - IEEE International Conference on Robotics and Automation. (pp. pp. 1744-1750). IEEE: London, UK.

Preview

Text GraspingPipeline_Yucheng_ICRA2023.pdf - Accepted Version Download (3MB) | Preview

Abstract

Generating high-quality instance-wise grasp con-figurations 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 Single-Stage Grasp (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.

Download activity - last month

Export as JSONXMLCSV

Download activity - last 12 months

Export as JSONXMLCSV

Downloads by country - last 12 months

Export as CSVXMLJSON

Archive Staff Only

View Item