论文标题
定向专注的机器人抓取合成，并具有增强的抓地图表示
Orientation Attentive Robotic Grasp Synthesis with Augmented Grasp Map Representation
论文作者
论文摘要
物体中固有的形态特征可能会提供多种合理的抓握方向，使人抓取机器人抓握的视觉学习。现有的掌握生成方法被诅咒以构造不连续的掌握图，通过汇总注释每个抓地点截然不同的方向。此外，当前的方法在机器人的角度上会在一个方向上产生掌握候选者，而忽略了其可行性约束。在本文中，我们提出了一种新颖的增强抓地图表示，适用于像素合成的合成，该表示通过将角度空间划分为多个垃圾箱，从而在局部删除了抓握方向。此外，我们介绍了方向的专注于掌握合成（橙色）框架，该框架共同解决了分类为方向箱和角值回归。 bin的方向图进一步作为具有更高抓地力的区域的注意机制，即是实际掌握点的概率。我们报告了Jacquard上最新的94.71％性能，仅使用深度图像的简单U-NET，甚至超过了多模式方法。随后的定性结果具有真正的双手机器人，可以验证橙色在生成多种取向的掌握方面的有效性，因此允许计划可行的grasps。
Inherent morphological characteristics in objects may offer a wide range of plausible grasping orientations that obfuscates the visual learning of robotic grasping. Existing grasp generation approaches are cursed to construct discontinuous grasp maps by aggregating annotations for drastically different orientations per grasping point. Moreover, current methods generate grasp candidates across a single direction in the robot's viewpoint, ignoring its feasibility constraints. In this paper, we propose a novel augmented grasp map representation, suitable for pixel-wise synthesis, that locally disentangles grasping orientations by partitioning the angle space into multiple bins. Furthermore, we introduce the ORientation AtteNtive Grasp synthEsis (ORANGE) framework, that jointly addresses classification into orientation bins and angle-value regression. The bin-wise orientation maps further serve as an attention mechanism for areas with higher graspability, i.e. probability of being an actual grasp point. We report new state-of-the-art 94.71% performance on Jacquard, with a simple U-Net using only depth images, outperforming even multi-modal approaches. Subsequent qualitative results with a real bi-manual robot validate ORANGE's effectiveness in generating grasps for multiple orientations, hence allowing planning grasps that are feasible.
