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.

通过使用单个 RGB 图像进行模块化学习网络来检测抓取姿势，本研究提出了一种有趣的方法，适用于配备平板夹具的机器人，不仅识别可抓取的物体，还将先前的抓取分析与语义分割融合，从而提高抓取检测精度，并且在处理模糊和嘈杂的视觉情况时表现出弹性，通过实际实验和评估证明了我们提出的方法的可行性和准确性。

基于 RGB 图像的机器人抓取检测的模块化抗噪声深度学习网络

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

We introduce a neural implicit representation for grasps of objects from
multiple robotic hands. Different grasps across multiple robotic hands are
encoded into a shared latent space. Each latent vector is learned to decode to
the 3D shape of an object and the 3D shape of a robotic hand in a grasping pose
in terms of the signed distance functions of the two 3D shapes. In addition,
the distance metric in the latent space is learned to preserve the similarity
between grasps across different robotic hands, where the similarity of grasps
is defined according to contact regions of the robotic hands. This property
enables our method to transfer grasps between different grippers including a
human hand, and grasp transfer has the potential to share grasping skills
between robots and enable robots to learn grasping skills from humans.
Furthermore, the encoded signed distance functions of objects and grasps in our
implicit representation can be used for 6D object pose estimation with grasping
contact optimization from partial point clouds, which enables robotic grasping
in the real world.

该研究介绍了一种支持多个机器手抓取对象的神经隐式表示方法， 允许在不同机器手之间传递抓取技能，并可用于 6D 对象姿态估计以及机器人在真实世界中的抓取。