论文标题
在SE中的有效且准确的候选姿势检测（3）
Efficient and Accurate Candidate Generation for Grasp Pose Detection in SE(3)
论文作者
论文摘要
在非结构化环境中对新物体的掌握是机器人操纵中的关键能力。对于假定grasps位于平面上的2D抓取检测问题，通常会设计一个完全卷积的神经网络，该网络一步一步可以预测整个图像。但是，这对于在SE（3）中假定存在抓握姿势的抓握姿势检测是不可能的。在这种情况下，通常可以通过两个步骤解决问题：抓住候选人的产生和候选分类。由于GRASP候选人分类通常很昂贵，因此问题成为有效识别高质量候选人Grasps的问题之一。本文提出了一种新的Grasp候选生成方法，该方法明显优于主要的3D抓取检测基线。补充材料可在https://atenpas.github.io/psn/上获得。
Grasp detection of novel objects in unstructured environments is a key capability in robotic manipulation. For 2D grasp detection problems where grasps are assumed to lie in the plane, it is common to design a fully convolutional neural network that predicts grasps over an entire image in one step. However, this is not possible for grasp pose detection where grasp poses are assumed to exist in SE(3). In this case, it is common to approach the problem in two steps: grasp candidate generation and candidate classification. Since grasp candidate classification is typically expensive, the problem becomes one of efficiently identifying high quality candidate grasps. This paper proposes a new grasp candidate generation method that significantly outperforms major 3D grasp detection baselines. Supplementary material is available at https://atenpas.github.io/psn/.
