In this paper, we propose an end-to-end grasp evaluation model to address the
challenging problem of localizing robot grasp configurations directly from the
point cloud. Compared to recent grasp evaluation metrics that are based on
handcrafted depth features and a convolutional neural network (CNN), our
proposed PointNetGPD is lightweight and can directly process the 3D point cloud
that locates within the gripper for grasp evaluation. Taking the raw point
cloud as input, our proposed grasp evaluation network can capture the complex
geometric structure of the contact area between the gripper and the object even
if the point cloud is very sparse. To further improve our proposed model, we
generate a larger-scale grasp dataset with 350k real point cloud and grasps
with the YCB object set for training. The performance of the proposed model is
quantitatively measured both in simulation and on robotic hardware. Experiments
on object grasping and clutter removal show that our proposed model generalizes
well to novel objects and outperforms state-of-the-art methods. Code and video
are available at
\href{https://lianghongzhuo.github.io/PointNetGPD}{this https URL}

本论文提出了一种端到端的夹持评估模型 (PointNetGPD)，可以直接从三维点云中定位机器人夹持配置，采用 350k 点云与夹持数据集来训练，并经过在仿真和实际机器人硬件上的实验测试，结果表明该模型具有较好的泛化性并优于现有的方法。