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While traditional methods relies on depth sensors, the current trend leanstowards utilizing cost-effective RGB images, despite their absence of depthcues. This paper introduces an interesting approach to detect grasping posefrom a single RGB image. To this end, we propose a modular learning networkaugmented with grasp detection and semantic segmentation, tailored for robotsequipped with parallel-plate grippers. Our network not only identifiesgraspable objects but also fuses prior grasp analyses with semanticsegmentation, thereby boosting grasp detection precision. Significantly, ourdesign exhibits resilience, adeptly handling blurred and noisy visuals. Keycontributions encompass a trainable network for grasp detection from RGBimages, a modular design facilitating feasible grasp implementation, and anarchitecture robust against common image distortions. We demonstrate thefeasibility and accuracy of our proposed approach through practical experimentsand evaluations.

Modular Anti-noise Deep Learning Network for Robotic Grasp Detection Based on RGB Images