We present a novel approach to robotic grasp planning using both a learned
grasp proposal network and a learned 3D shape reconstruction network. Our
system generates 6-DOF grasps from a single RGB-D image of the target object,
which is provided as input to both networks. By using the geometric
reconstruction to refine the the candidate grasp produced by the grasp proposal
network, our system is able to accurately grasp both known and unknown objects,
even when the grasp location on the object is not visible in the input image.
This paper presents the network architectures, training procedures, and grasp
refinement method that comprise our system. Experiments demonstrate the
efficacy of our system at grasping both known and unknown objects (91% success
rate in a physical robot environment, 84% success rate in a simulated
environment). We additionally perform ablation studies that show the benefits
of combining a learned grasp proposal with geometric reconstruction for
grasping, and also show that our system outperforms several baselines in a
grasping task.

本文介绍了一种基于学习抓取提案网络和学习三维形状重建网络的机器人抓取规划方法，使用几何重建来优化抓取提案网络生成的抓取点，实现了在图像中看不到抓取位置的情况下，对已知和未知物体进行准确抓取。实验结果表明，与其他模型相比，该方法在抓取任务中取得了更高的成功率。

基于图像抓取提案与三维重建的机器人抓取

Robotic Grasping through Combined Image-Based Grasp Proposal and 3D  Reconstruction

This work provides an architecture to enable robotic grasp planning via shape
completion. Shape completion is accomplished through the use of a 3D
convolutional neural network (CNN). The network is trained on our own new open
source dataset of over 440,000 3D exemplars captured from varying viewpoints.
At runtime, a 2.5D pointcloud captured from a single point of view is fed into
the CNN, which fills in the occluded regions of the scene, allowing grasps to
be planned and executed on the completed object. Runtime shape completion is
very rapid because most of the computational costs of shape completion are
borne during offline training. We explore how the quality of completions vary
based on several factors. These include whether or not the object being
completed existed in the training data and how many object models were used to
train the network. We also look at the ability of the network to generalize to
novel objects allowing the system to complete previously unseen objects at
runtime. Finally, experimentation is done both in simulation and on actual
robotic hardware to explore the relationship between completion quality and the
utility of the completed mesh model for grasping.

通过使用 3D 卷积神经网络结构，本研究为机器人抓取规划提供形状补全，同时在实时运行时进行形状补全以规划和执行抓取动作。研究还探讨了形状补全的质量因素，如物体是否存在于训练数据中，使用几个物体模型用于训练网络，以及网络推广到新颖物体的能力等，同时对机器人抓取的效用和补全质量进行了实验验证。