This article contains heuristics for following:

本文包含以下启发式方法：

• Parallel jaw grasps.下颌平行抓紧。
• Suction grasps.吸气。
• Linear push policies for improving parallel jaw grasps.线性推动策略可改善平行颚的抓握力。
• Toppling policies for improving suction grasps.改善吸力控制的主要政策。

Grasping is one of the fundamental subtask of a robotic manipulation pipeline. Both learning based and physics / geometry based grasping methods can benefit from grasp sampling heuristics in this article. Even if you are using a large arm farm to teach your robots the skills of grasping, you can save your robots quite a lot of time with these heuristics. This article summarizes the most common grasp sampling heuristics used in literature.

抓取是机器人操纵管线的基本子任务之一。 本文中的基于学习和基于物理/几何的抓取方法都可以从抓取采样启发式方法中受益。 即使您正在使用大型武装农场来教您的机器人掌握技巧 ，您也可以通过这些启发式方法为您的机器人节省大量时间。 本文总结了文献中最常见的抓取采样启发式方法。

Some of the common ways to use these heuristics are:

使用这些启发式方法的一些常见方法是：

• Generating labels for learning based grasp planners (offline): 6-DOF GraspNet [4] uses these samplers for evaluation with physics based simulation. Grasps that retain the object between the gripper are considered successful after a predefined shaking motion. DexNet [2][3] evaluates these grasps based on analytic quasi-static grasp wrench space (GWS) analysis. Both methods score these sampled grasps based on how good they are in resisting disturbances. These scores are used as labels for training the grasp planners.

  为学习型抓手计划者生成标签(离线)： 6自由度GraspNet [4]使用这些采样器进行基于物理的仿真评估。 在预定义的摇动之后，将物体保持在抓具之间的抓握被认为是成功的。 DexNet [2] [3]基于准静态抓紧扳手空间(GWS)分析对这些抓握进行评估。 两种方法都基于它们在抵抗干扰方面的出色程度对这些采样的抓取力进行评分。 这些分数用作培训掌握计划者的标签。

• During grasp synthesis (inference): DexNet [2][3] uses these sampled grasps as seeds for Cross Entropy Method (CEM), and optimizes grasps based on predicted grasp quality from GQ-CNN (Grasp Quality Convolutional Network). Traditional geometric methods, prune these candidate grasps if they are kinematically infeasible or if they result in collision between gripper and other objects or environment. The best of these samples are picked for execution.

  在抓取合成(推断)期间： DexNet [2] [3]将这些采样的抓取用作交叉熵方法(CEM)的种子，并基于GQ-CNN(抓取质量卷积网络)的预测抓取质量来优化抓取。 传统的几何方法会修剪这些候选对象，如果它们在运动学上不可行，或者导致抓手与其他对象或环境之间发生碰撞。 从这些样本中最好的样本进行执行。

We will summarize the details of heuristics for each type of grippers used for manipulation.

我们将总结用于操纵的每种类型的抓取器的启发式方法的详细信息。

平行下颚抓 (Parallel jaw grasps)

Parallel jaw grasps jam the object between the grippers (Most often the grippers have rubber on them to increase the size of friction cones and thus the robustness of the grasp). Typically, the success of parallel jaw grasp depends on local geometry around the grasp point like if the grasp fits inside the gripper, friction btw gripper and object surface, mass of the object.

平行的下颌抓握器会夹住抓具之间的物体(大多数情况下，抓具上都带有橡胶以增加摩擦锥的大小，从而增加抓握的坚固性)。 通常，平行下颌抓取的成功取决于抓取点周围的局部几何形状，例如抓取是否适合抓取器内部，抓取器与物体表面的摩擦力，物体的质量。

Force Closure: If the contact points on the object are such that forces applied on those points don’t result in slippage and can resist gravity then force closure ( object doesn’t move with respect to the gripper ) is achieved, the grasp is considered successful.

力封闭：如果物体上的接触点不会对物体施加力而导致打滑，并且可以抵抗重力，则可以实现力封闭(物体相对于抓具不会移动)，则应考虑抓紧成功。

Parametrization: Parallel Jaw Grasps are typically parametrized by 6-DOF pose of the gripper with initial configuration of open gripper.

参数化：平行下颌抓握通常是通过夹具的6自由度姿势和开放式夹具的初始配置来参数化的。

A Billion ways to grasp [1] summarizes several heuristics for parallel jaw grippers and evaluates their precision and coverage w.r.t a uniform sampler.

十亿种掌握方法[1]总结了平行颚式抓爪的几种启发式方法，并通过统一的采样器评估了它们的精度和覆盖率。

Assumption: Access to the 3D triangle mesh or 3D point cloud of the object so that surface normals can be computed.

假设：访问对象的3D三角形网格或3D点云，以便可以计算表面法线。

Here are the two most effective heuristics that are purely based on geometry:

以下是两个完全基于几何的最有效的启发式方法：

Approach based samplers:

基于方法的采样器：

These methods are characterized by approach vector of the gripper (red-dashed line) which typically aligns with normal to the palm (purple axis).

这些方法的特征在于抓手的接近向量(红色虚线)，通常与手掌法线(紫色轴)对齐。

Pseudo code for approach based sampler:

基于方法的采样器的伪代码：

Antipodal based samplers:

基于对立采样器：

These methods sample directly on the space of possible contact points and try to exploit the grasps that create force closure.

这些方法直接在可能的接触点的空间上采样，并尝试利用产生力闭合的抓紧力。

Pseudo code for antipodal grasp sampler:

对偶采样器的伪代码：

Billion ways to grasp [1] evaluates grasps based on two metrics:

十亿种把握方式[1]根据两个指标评估把握：

• Robust coverage: Percent of robust grasps (still successful in a small ϵ-neighborhood) sampled w.r.t oracle uniform sampler. This is very similar to recall.

  稳健的覆盖率：稳健的掌握率(在一个小的ϵ邻域中仍然成功)从oracle统一采样器中采样的百分比。 这与召回非常相似。

• Precision: Percent of the successful grasps among the sampled.

  精度：样本中成功掌握的百分比。

As seen by the conclusion of Billion ways to grasp[1] from the tables, if you have a limited sampling budget antipodal sampling scheme provides both highest coverage and precision. However, asymptotically misses several ground truth grasps. These correspond to small scale features on objects and along the edges of objects.

从表中的“十亿种方法”的结论可以看出[1]，如果您的采样预算有限，则对映采样方案可以提供最高的覆盖率和精度。 然而，渐进地错过了一些地面真理的掌握。 这些对应于对象上以及沿着对象边缘的小比例特征。

Visual illustration of what these sampled successful grasps and robust successful grasps look like. Each point is the grasp center and notice how robust grasps are clustered around object parts that fit nicely inside the gripper.

这些采样的成功掌握和稳健的成功掌握的视觉示意图看起来像。 每个点都是抓地力中心，注意坚固的抓地力如何聚集在非常适合抓手内部的对象部分周围。

吸盘 (Suction grippers)

Suction grippers form vacuum seal on the surface of the object and if that vacuum force is sufficient to resist the gravity and external wrenches, the grasp is robust. Typically suction grasp success depends on surface porousness, local geometry, mass and payload capacity of the suction gripper. These grippers are most popular for pick and place of objects in warehouse order fulfillment. DexNet 4.0 [6] which is one of the best published bin-picking system that uses composite policy between suction and parallel jaw grasps, chooses suction grasps for about 82% of attempts.

吸气夹具在物体表面形成真空密封，如果该真空力足以抵抗重力和外部扳手，则抓握力很强。 通常，抽吸抓取成功与否取决于抽吸抓具的表面Kong隙度，局部几何形状，质量和有效载荷容量。 这些抓取器最常用于在仓库订单履行中拾取和放置对象。 DexNet 4.0 [6]是使用率最高的垃圾收集系统之一，它在吸力和平行下颚抓地力之间采用了复合策略，大约有82％的尝试选择了吸力抓地力。

Parametrization: Suction grasps are typically parameterized by point p on the object surface and approach vector v as illustrated below.

参数化：吸气抓取通常由对象表面上的点p和逼近向量v参数化 ，如下所示。

Planarity Centroid Heuristic:

平面质心启发式：

Since successful suction grasps prefer planar non-porous surfaces, these heuristics try to find sufficiently planar surfaces on the object that are closer to COM (Center of Mass). Approach vectors are chosen along the surface normal because large motion tangential to surface might result in vacuum seal breakage.

由于成功的抽吸抓取更喜欢平面的无Kong表面，因此这些启发式方法试图在物体上找到更靠近COM(质心)的足够平面的表面。 沿表面法线选择接近向量，因为与表面相切的大运动可能会导致真空密封破裂。

Pseudo code for planarity centroid heuristic:

平面性质心启发式的伪代码：

Some examples of successful suction grasps on 3D meshes are visualized below.

下面是3D网格上成功吸取的一些示例。

DexNet 3.0 [3] evaluates suction grasps in physical robot trials based on two metrics:

DexNet 3.0 [3]根据两个指标评估物理机器人试验中的吸力抓地力：

• Average Precision: Area under the precision / recall curve. How good is the heuristic in scoring high quality grasps ?

  平均精度：精度/召回曲线下的面积。 高质量抓取得分的启发式方法有多好？

• Success Rate: Fraction of the grasps that were successful.

  成功率：成功把握的分数。

As can be seen from the table above, Planarity Centroid Heuristic does quite well compared to even learnt method DexNet 3.0 [3] on basic and typical objects.

从上表可以看出，相对于基本对象和典型对象的学习方法DexNet 3.0 [3]，Planarity Centroid Heuristic的性能非常好。

Some of the failure cases of suction grasps are categorized as below:

吸力抓握的一些失败案例分类如下：

自适应采样器： (Adaptive sampler:)

These methods use heuristics that exploit the geometry to generate seed samples (described above) and further optimize the grasp according to grasp quality metric. Most often these are blackbox optimization technique such as cross entropy method (CEM) that doesn’t exploit object geometry. Although CEM is an optimization algorithm used in many areas, I would still consider it a heuristic since it doesn’t exploit the object geometries while sampling.

这些方法使用启发式技术，该启发式技术利用几何形状生成种子样本(如上所述)，并根据抓地质量指标进一步优化抓地力。 最常见的是黑箱优化技术，例如不利用对象几何形状的交叉熵方法(CEM)。 尽管CEM是许多领域中使用的优化算法，但我仍然认为它是一种启发式方法，因为它在采样时不会利用对象的几何形状。

Additional assumption: Access to grasp quality function such as DexNet 2.0 / DexNet 3.0 Grasp Quality Network (GQ-CNN) or ability to evaluate quality of grasps in realtime based on GWS.

附加假设：可访问抓取质量功能(例如DexNet 2.0 / DexNet 3.0抓握质量网络(GQ-CNN))或基于GWS实时评估抓握质量的能力。

交叉熵法(CEM) (Cross Entropy Method (CEM))

If you were familiar with CEM, you may have noticed the use of GMM instead of Gaussians and this is because distribution of grasps on most objects are multi-modal.

如果您熟悉CEM，则可能已经注意到使用GMM而不是高斯模型，这是因为大多数对象上的控制点分布是多模式的。

Some examples of applying CEM method to DexNet 2.0 (parallel jaw grasps )and DexNet 3.0 (suction grasps) grasp quality functions to generate most robust grasps.

将CEM方法应用于DexNet 2.0(平行下颌抓紧)和DexNet 3.0(抽吸抓紧)的一些示例可抓握质量函数以生成最可靠的抓握。