Modular Anti-noise Deep Learning Network for Robotic Grasp Detection
  Based on RGB Images
Zhaocong Li
While traditional methods relies on depth sensors, the current trend leans
towards utilizing cost-effective RGB images, despite their absence of depth
cues. This paper introduces an interesting approach to detect grasping pose
from a single RGB image. To this end, we propose a modular learning network
augmented with grasp detection and semantic segmentation, tailored for robots
equipped with parallel-plate grippers. Our network not only identifies
graspable objects but also fuses prior grasp analyses with semantic
segmentation, thereby boosting grasp detection precision. Significantly, our
design exhibits resilience, adeptly handling blurred and noisy visuals. Key
contributions encompass a trainable network for grasp detection from RGB
images, a modular design facilitating feasible grasp implementation, and an
architecture robust against common image distortions. We demonstrate the
feasibility and accuracy of our proposed approach through practical experiments
and evaluations.