Sun Qi, He Lili
School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Sensors (Basel). 2024 Dec 3;24(23):7735. doi: 10.3390/s24237735.
In robotic grasping tasks, shape similarity has been widely adopted as a reference in grasp positions prediction for unknown objects. However, to the best of our knowledge, the issue "do similar objects have similar grasp positions?" has not been quantitatively analyzed before. This work aims to confirm or disprove the question by analyzing the relationship between the object shape similarity and grasp positions similarity. To this end, we constructed a similarity-estimation plane (SE-Plane), whose horizontal and vertical axes indicate the objects similarity and grasp similarity, respectively. Then, the proof of the issue is equal to the confirmation of the inference that "the points with higher objects similarity accordingly own higher grasps similarity in the proposed SE-Plane". We adopted several classical shape descriptors and two kinds of widely recognized deep neural network (DNN) architectures as objects similarity strategies. Furthermore, we employed the widely adopted intersection-over-union (IoU) of grasp anchors to measure the grasp similarity between objects. The experiments were carried out on a dozen objects with commonly seen primitive shapes selected from two well-known open grasp datasets: Cornell and Jacquard. It was found that the IoU values of grasp anchors are generally proportional to those of objects similarity in the SE-Plane. In addition, we obtained several primitive shapes from the commonly seen shapes, which are more suitable references in grasp positions prediction for unknown objects. We also constructed a realistic object dataset that included the objects with commonly seen primitive shapes. With the IoU prediction strategy learned from Cornell and Jacquard, the IoU predicted for realistic objects yielded similar results in the proposed SE-Plane. These discussions indicate that "similar objects have similar grasp positions" is reasonably correct. The proposed SE-Plane presents a new strategy to measure the relationship between objects similarity and grasp similarity.
在机器人抓取任务中,形状相似度已被广泛用作未知物体抓取位置预测的参考依据。然而,据我们所知,“相似物体是否具有相似的抓取位置?”这一问题此前尚未得到定量分析。这项工作旨在通过分析物体形状相似度与抓取位置相似度之间的关系来证实或反驳这一问题。为此,我们构建了一个相似度估计平面(SE-平面),其横轴和纵轴分别表示物体相似度和抓取相似度。那么,对该问题的证明就等同于对“在所提出的SE-平面中,物体相似度较高的点相应地具有较高的抓取相似度”这一推断的确认。我们采用了几种经典的形状描述符和两种广泛认可的深度神经网络(DNN)架构作为物体相似度策略。此外,我们使用广泛采用的抓取锚点的交并比(IoU)来衡量物体之间的抓取相似度。实验是在从两个著名的开放抓取数据集:康奈尔和雅卡尔中选取的十几个具有常见基本形状的物体上进行的。结果发现,在SE-平面中,抓取锚点的IoU值通常与物体相似度的值成正比。此外,我们从常见形状中获得了几种基本形状,它们在未知物体抓取位置预测中是更合适的参考。我们还构建了一个包含具有常见基本形状物体的真实物体数据集。利用从康奈尔和雅卡尔中学到的IoU预测策略,在所提出的SE-平面中,对真实物体预测的IoU产生了相似的结果。这些讨论表明“相似物体具有相似的抓取位置”是合理正确的。所提出的SE-平面提出了一种测量物体相似度与抓取相似度之间关系的新策略。