Learning robotic grasps from visual observations is a promising yet
challenging task. Recent research shows its great potential by preparing and
learning from large-scale synthetic datasets. For the popular, 6
degree-of-freedom (6-DOF) grasp setting of parallel-jaw gripper, most of
existing methods take the strategy of heuristically sampling grasp candidates
and then evaluating them using learned scoring functions. This strategy is
limited in terms of the conflict between sampling efficiency and coverage of
optimal grasps. To this end, we propose in this work a novel, end-to-end
\emph{Grasp Proposal Network (GPNet)}, to predict a diverse set of 6-DOF grasps
for an unseen object observed from a single and unknown camera view. GPNet
builds on a key design of grasp proposal module that defines \emph{anchors of
grasp centers} at discrete but regular 3D grid corners, which is flexible to
support either more precise or more diverse grasp predictions. To test GPNet,
we contribute a synthetic dataset of 6-DOF object grasps; evaluation is
conducted using rule-based criteria, simulation test, and real test.
Comparative results show the advantage of our methods over existing ones.
Notably, GPNet gains better simulation results via the specified coverage,
which helps achieve a ready translation in real test. We will make our dataset
publicly available.

本文提出了一种新颖的端到端 “Grasp Proposal Network (GPNet)”，基于离散但规则的 3D 网格角上的 “抓握中心锚点”，用于预测从单个和未知相机视图观察到的物体的多样化 6-DOF 抓握，这有助于实现更好的仿真结果和更好的实际测试结果。

Grasp Proposal Networks: 机器人抓取视觉学习的端到端解决方案

Grasp Proposal Networks: An End-to-End Solution for Visual Learning of  Robotic Grasps

We consider the problem of detecting robotic grasps in an RGB-D view of a
scene containing objects. In this work, we apply a deep learning approach to
solve this problem, which avoids time-consuming hand-design of features. This
presents two main challenges. First, we need to evaluate a huge number of
candidate grasps. In order to make detection fast, as well as robust, we
present a two-step cascaded structure with two deep networks, where the top
detections from the first are re-evaluated by the second. The first network has
fewer features, is faster to run, and can effectively prune out unlikely
candidate grasps. The second, with more features, is slower but has to run only
on the top few detections. Second, we need to handle multimodal inputs well,
for which we present a method to apply structured regularization on the weights
based on multimodal group regularization. We demonstrate that our method
outperforms the previous state-of-the-art methods in robotic grasp detection,
and can be used to successfully execute grasps on two different robotic
platforms.

运用深度学习方法解决机器人抓取问题，通过多模态组规则化结构化正则化权重来处理多模态输入，实现了比之前最先进方法更好的效果，可成功在两个不同的机器人平台上执行抓取。