Structure of attention mechanism block.

<div><p>Robotic grasping is crucial in manufacturing, logistics, and service robotics, but existing methods struggle with object occlusion and complex arrangements in cluttered scenes. We propose the Generative Residual Attention Network (GR-AttNet), based on the Generative Residual Conv...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Shengbang Zhou (22784000) (author)
مؤلفون آخرون:	Zhongsheng Liu (2476672) (author), Chuanqi Li (137459) (author), Dong Chen (448284) (author), Shutian Liu (3981278) (author), Yadong Liu (285952) (author)
منشور في:	2025
الموضوعات:	Biotechnology Infectious Diseases Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified outperforming several state highly practical solution 163 &# 8201 148 &# 8201 severe occlusion cases existing methods struggle 8 million parameters attnet achieves 98 attnet ), based experiments show gr cornell grasping dataset robotic grasping reducing parameters object occlusion jacquard dataset art methods attnet offers service robotics processing speeds complex arrangements cnn ). cluttered scenes challenges remain boosting efficiency
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Structure of attention mechanism block.

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