# Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper
## 2 Related Works
The authors also replicated the results on their physical counterparts, where they achieved between an 86% and 80.5% successful grasp rate for single and multiple objects compared to the algorithm performance in simulation. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [22] developed a \"Dexterity Network\" (Dex-Net) 4.0, which was designed to learn the grasping policy for a given set of robotic grippers (2-F and vacuum) by training on synthetic datasets using domain randomization with analytic models of physics and geometry. The policy assumes the following: (i) quasistatic physics (e.g., inertial terms are negligible) with Coulomb friction, (ii) objects are rigid and made of nonporous material, (iii) the robot has a single overhead depth sensor with known performance characteristics, and (iv) the robot has two end effectors with known geometry. They combine the Grasp Quality Convolutional Neural Networks (GQ-CNNs) for each gripper to plan grasps for objects in a given point cloud. On their physical counterpart, they achieved 95% reliability on several of 25 novel objects. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [23] developed a framework for high-DOF multifingered grasping in clutter that can also be used in various parallel-jaw and multifingered robot hands. It uses simulation-based learning that uses depth and geometry alone (i.e., no texture) to allow accurate domain transfer to real scenes. It does not require any database of grasp examples. Instead, it uses a policy gradient formulation and a learned attention mechanism to generate full 6-DOF grasp poses and all finger joint angles to pick up objects in dense clutter given a single-depth image. They achieved up to 96.7% of grasp success in single-seen objects for two robotic grippers. [PERSON] et al. [47] developed a method that can predict the appropriate grasping point-pair of an unknown object for a specific task with a much lower training cost. They implemented a two-stage predictor, where, firstly, using a Sobel operator on RGB-D data, they proposed robust grasp candidates. Secondly, they used a region-based predictor trained by semisupervised learning. Experimental results demonstrated that the proposed region-based grasping detection method can find an accurate grasp configuration of a new emergent object and achieve an average success rate of 91.5% by using fewer than 100 training samples. [PERSON] and [PERSON] [42] proposed a novel two-stage grasp detection method based on visual rotation object detection and point cloud spatial feature scoring. They transformed the depth image to point cloud and proposed a grasp detection method to be made on the point cloud, rather than on RGB image. Since this approach uses the neural network, it requires pretraining prior to operation. They tested their approach on various household and 3D printed objects of different shapes, with the minimum score of 87.3% grasp attempts. [PERSON] et al. [48] developed a grasp detector based on a Feature Pyramid Network (FPN) for a 2-F parallel gripper. The input for the grasp detector is the RGB-D image obtained from the camera. Their grasp detector works in two stages: first, the detector generates horizontal candidate grasp areas, whilst in the second stage, it refines those poses to predicted rotated grasp poses. The grasp detector achieves up to 93.3% accuracy on a selected object set. While the most recent approaches rely primarily on \"deep-learning\" approaches, our simulation model adapts the combination of an analytical and empirical approach for the systematic pick-point determination. This could be considered the slower and more time-consuming approach in many cases. Still, compared to \"black-box\" methods, the simulation model transparency remains high throughout the pick-point determination process. Additionally, most of the reviewed works from the literature focus on 2-F parallel gripper configurations, while the adaptive grippers are discussed less commonly or rarely.