This article presents a method for grasping novel objects by learning from experience. Successful attempts are remembered and then used to guide future grasps such that more reliable grasping is achieved over time. To generalise the learned experience to unseen objects, we introduce the dense geometric correspondence matching network (DGCM-Net). This applies metric learning to encode objects with similar geometry nearby in feature space. Retrieving relevant experience for an unseen object is thus a nearest neighbour search with the encoded feature maps. DGCM-Net also reconstructs 3D-3D correspondences using the view-dependent normalised object coordinate space to transform grasp configurations from retrieved samples to unseen objects. In comparison to baseline methods, our approach achieves an equivalent grasp success rate. However, the baselines are significantly improved when fusing the knowledge from experience with their grasp proposal strategy. Offline experiments with a grasping dataset highlight the capability to generalise within and between object classes as well as to improve success rate over time from increasing experience. Lastly, by learning task-relevant grasps, our approach can prioritise grasps that enable the functional use of objects.

中文翻译：

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DGCM-Net：基于增量经验的机器人抓取的密集几何对应匹配网络

本文提出了一种通过从经验中学习来抓取新物体的方法。成功的尝试被记住，然后用于指导未来的抓取，以便随着时间的推移实现更可靠的抓取。为了将学习到的经验推广到看不见的对象，我们引入了密集几何对应匹配网络（DGCM-Net）。这将度量学习应用于对特征空间中附近具有相似几何形状的对象进行编码。因此，为看不见的对象检索相关经验是使用编码的特征图进行的最近邻搜索。DGCM-Net 还使用依赖于视图的归一化对象坐标空间重建 3D-3D 对应关系，将抓取配置从检索到的样本转换为看不见的对象。与基线方法相比，我们的方法实现了相同的抓取成功率。然而，当将经验中的知识与其抓取建议策略相融合时，基线得到了显着改善。具有抓取数据集的离线实验突出了在对象类内和对象类之间进行概括的能力，以及随着经验的增加而提高成功率的能力。最后，通过学习与任务相关的抓取，我们的方法可以优先抓取能够实现对象功能使用的抓取。