# Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper
## 2 Related Works
Additionally, a more descriptive results analysis was added. Furthermore, the gripper closing is conducted with the force controller realized in MATLAB/Simulink, which also contains functions for optimizing the required time needed for the execution of a single simulation. Lastly, we provided several measures, based on which we proposed our own set of grasp success metrics. Those are used for the selection of best pick-points and further evaluation. [PERSON], [PERSON], and [PERSON] [26] discussed possible grasping failures associated with simulating the grasping of rigid objects of four open-source physics engines in the GAZEBO simulator. They exposed that while rigid body simulators are highly capable, it is tricky to implement the numerical algorithms that would enable accurate reproduction of the actual (robotic) system. Usually, this is reflected by the fact that the objects start to slip after some time from the simulated robot's grasp. They identified several failure modes, which may occur during the simulation of quasirigid objects with rigid robots, namely: (1) slip associated with too-low grasping force or friction coefficient, (2) iterative method nonconvergence, (3) rounding errors, (4) regularization errors, (5) constraint stabilization, (6) imprecise contact information, and (7) tangential drift. Of course, these problems can be contained to some extent, however, usually at the expense of increased simulation time. Additionally, they found that while some simulation engines (e.g., SIMBODY) proved more resistant to some types of errors, the simulation time drastically increased, rendering the simulations unusable for evaluating a higher number of pick-points. [PERSON] et al. [43] presented a part-based grasp-planning approach that can generate grasps, which are also applicable to similar, novel objects. They first segmented multiple object meshes according to their shape and volumetric information. Second, they labeled specific parts (segments) of the object according to the robot task the gripper is supposed to perform. Lastly, they used the \"Simox grasp planning toolbox\" to generate stable grasps by aligning an initially specified hand approach direction with a randomly chosen object surface normal. Finally, they tested their method using the humanoid robot ARMAR-III, equipped with a robotic hand. Further, [PERSON] et al. [44] presented an approach to transfer grasp configurations from prior examples to novel objects, assuming that the novel and original objects have the same topology and use similar shapes. First, they performed 3D segmentation on the sample objects using geometric and semantic shape characteristics. Next, a grasp space was calculated for each part, and the corresponding grasps were calculated for novel objects using bijective contact mapping. In the case of detection-based methods, [PERSON] et al. [45] presented a practical solution to the problem of robot picking in an online shopping warehouse by employing the centroid normal approach method (CNA) on a cost-effective dual-arm robotic system with two grippers. Scene point clouds are matched with the grasping techniques and grippers (2-F and vacuum) by performing visible surface analysis. It does not employ any mechanical model of the object in question since it derives all the needed information for picking from the actual scene. In the Amazon Challenge 2017, for a given object set, they were successful, from 69% to 77.5%, in grasping previously unknown objects. [PERSON] et al. [46] presented an AdaGrasp policy, which is designed to select the most appropriate robotic gripper and its pose for various objects based on cross-convolution between the shape encodings of the gripper and the scene. The policy matches the scene and the gripper geometry under different grasp poses, where a good overlap of the gripper geometry to a 3D geometry of the grasped object will lead to a successful grasp. It uses the \"Pybullet\" simulation environment to evaluate the grasp quality.