Determining the quality of grasps is a complex challenge since it greatly depends on the object geometry, the gripper geometry, dynamic properties, such as friction forces, and the task that will be performed after the object is grasped [4]. Analytical approaches can offer reliable solutions, based on simulated contacts and dynamics. Still, they often rely on known object geometry, material, inertia matrix, etc., and their computation is often time-consuming [1]. Object detection and pose estimation also have to be performed on top of the analytical grasp planning for objects with unknown poses. Additionally, the relevant information about the task which will be performed after the grasping is very hard to formulate, and thus, it is nearly impossible to utilize it in grasp planning with analytical approaches. On the other hand, data-driven approaches for grasp planning can incorporate object detection, pose estimation and grasp planning into a single model [5]. With the use of large datasets, they are able to learn general grasping policies, which can be applied to novel objects as well [2; 5]. Grasp prediction with such models is also significantly faster when compared to analytical methods [5]. However, a large dataset is required to train such models. Collecting a large-scale grasping dataset using physical robots can take up to months and incur great costs [2]. As a result, offline-generated simulation-based synthetic data are usually preferred, where the analytical grasp quality approaches can be utilized for ground-truth generation [5]. [PERSON] and [PERSON] created a simulator called Graspitt for evaluating robotic grasps [1]. In Graspitt, arbitrary gripper and object geometries can be imported. Using a sampling-based method, a large number of grasps can be evaluated for a given gripper-object combination to find the best-quality grasps. It can also be used to simulate and compute the quality of grasps in a dynamic environment, but this is out of the scope of this paper. [PERSON] et al. created the Columbia Grasp Database and a corresponding data-driven approach for grasp planning with the help of the Graspitt simulator [6; 7]. Their approach is based on matching partial object geometry information against a large dataset of 3D models and selecting appropriate grasps from a set of pre-computed grasps for the matched objects. A similar approach of using pre-computed grasps, called Dex-Net 1.0, was introduced by [PERSON] et al. who leveraged the benefits of cloud computing to reduce the application runtime significantly [8]. For measuring the similarity between objects, they used multi-view convolutional neural networks. Later, based on their results in Dex-Net 1.0, [PERSON] et al. compiled a huge synthetic dataset (called Dex-Net 2.0) of 6.7 million point clouds with grasps and associated analytic grasp metrics, where grasps are represented by a planar position, an angle and the depth of the gripper relative to the RGB-D sensor [5]. They also proposed a convolutional neural network architecture called grasp quality convolutional neural network (GQCNN) that could predict the probability of the success of grasps directly from depth image data. They showed that a GQCNN trained on their Dex-Net 2.0 synthetic dataset outperformed other state-of-the-art approaches using point cloud registration while being 3\(\times\) faster. They also demonstrated great precision (99%) on a set of novel objects. One drawback of the previously mentioned methods is that they only consider grasping in isolation. However, the quality of a grasp also greatly depends on the task which will be performed after the grasping is done. [PERSON] et al. demonstrated that fixed grasps can make a robot manipulation task infeasible [9]. They suggest, that in-hand manipulation maneuvers, such as object pivoting based on tactile feedback (described in [10] in detail) might be necessary for a successful task solution, and as such, grasps should be selected, taking this into consideration.