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

Grasping in cluttered environments is a fundamental but challenging robotic
skill. It requires both reasoning about unseen object parts and potential
collisions with the manipulator. Most existing data-driven approaches avoid
this problem by limiting themselves to top-down planar grasps which is
insufficient for many real-world scenarios and greatly limits possible grasps.
We present a method that plans 6-DOF grasps for any desired object in a
cluttered scene from partial point cloud observations. Our method achieves a
grasp success of 80.3%, outperforming baseline approaches by 17.6% and clearing
9 cluttered table scenes (which contain 23 unknown objects and 51 picks in
total) on a real robotic platform. By using our learned collision checking
module, we can even reason about effective grasp sequences to retrieve objects
that are not immediately accessible. Supplementary video can be found at
this https URL

本研究提出了一种基于部分点云观察的，用于在杂乱场景中规划指定物体 6 自由度抓取的方法，其性能比基线方法高出 17.6%，能够成功清理包含 23 个未知物体和 51 次抓取操作的 9 个杂乱桌面场景，并利用我们学习到的碰撞检查模块来推理有效的抓取顺序，以检索不易到达的物体。