In this letter, we propose a Double Strand robotic grasp detection Network (DSNet), that combines a transformer branch and a U-Net branch within an encoder-decoder structure. The DSNet is designed to reconcile differences between these two approaches and provide access to both local and global resources. We have strategically incorporated bidirectional bridges with cross-attention mechanisms at the bottleneck points of each branch. These bridges facilitate the retrieval of abstract semantic data and reciprocally transfer it to the alternate branch, preserving both local features and global representations. To validate the performance of the DSNet, we utilized complete RGB-D information as input. The DSNet achieves an impressive accuracy rate of 98.31% and 95.7% on the Cornell and Jacquard grasping datasets. We used a 6DoF AUBO i5 robot to perform full-angle grasping of unknown objects, thereby confirming the reliability of the model.

中文翻译：

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DSNet：基于交叉注意力的双链机器人抓取检测网络

在这封信中，我们提出了一种双链机器人抓取检测网络（DSNet），它将变压器分支和 U-Net 分支结合在编码器-解码器结构中。 DSNet 旨在协调这两种方法之间的差异，并提供对本地和全球资源的访问。我们在每个分支的瓶颈点战略性地结合了具有交叉注意机制的双向桥。这些桥有助于检索抽象语义数据并将其相互传输到备用分支，从而保留局部特征和全局表示。为了验证 DSNet 的性能，我们使用完整的 RGB-D 信息作为输入。 DSNet 在 Cornell 和 Jacquard 抓取数据集上实现了 98.31% 和 95.7% 的令人印象深刻的准确率。我们使用6DoF AUBO i5机器人对未知物体进行全角度抓取，从而证实了模型的可靠性。