# Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper
## 1 Introduction
The most demanding task of robotic bin-picking systems is to automatically and autonomously pick items from the bin that are various by shape, color, and weight and have different mechanical properties. The essential steps in robotic bin-picking can be roughly categorized as (1) object detection, (2) object localization, (3) grasp detection, (4) path/trajectory planning, and (5) object manipulation [17]. The first two steps are in the domain of the vision system, which provides the information of the location and orientation of the object to be bin-picked, while the third step means that to manipulate the object successfully, one must first correctly determine object pick-points. The fourth and fifth steps are in the domain of the robotic subsystem, which determines robotic arm dynamics. Determining object pick-points can happen before or during the bin-picking operation, directly before the object manipulation [18]. Since the vision system cannot give complete information regarding the objects being observed, the researchers aim to extract possible grasp candidates from partial observation based on RGB-D images or point-cloud, coupled with various machine-learning techniques [19; 20]. Lately, the most used are deep-learning methods, where multiple types of neural networks are used (e.g., CNN, DCNN, etc. [21]). Those types of determining-grasp candidates can achieve very high accuracy even on previously unseen (novel) objects [22; 23], while usually requiring pretraining and setting the correct hyperparameters, which can also be very time intensive [24]. The second most notable option is to perform mechanical simulations before the bin-picking process in computer simulations or on the physical system, the latter being the most time- and labor-intensive. Since performing experiments on the physical system is not feasible for many objects, computer simulations using multibody physics simulators [25] are practical. A more significant number of correctly determined object pick-points (or grasp candidates) correspond to a higher possibility of correctly picking an item from a bin, given that the vision system provided the correct object location and pose. While rigid multibody simulators are highly capable of performing the grasping-point evaluation, it is usually difficult to implement the numerical algorithms which would enable accurate reproduction of the actual (robotic) system [26]. Numerous open-source simulation engines (ODE [27], BULLET [28], DART [29], SIMBODY [30]) exist to date. However, they usually require extensive programming knowledge and are subject to community maintenance. Additionally, they typically suffer from various failure modes, such as interpenetration or other physically unrealistic interactions between the robotic gripper and the objects, which are more analyzed in detail in [26]. For more information regarding the performance of various multibody physics simulators, the authors point out a study by [PERSON], [PERSON], and [PERSON] [31], in which several different multibody physics engines used in robotics were evaluated. Several other tools have been developed for planning/evaluating robotic gripper grasps, such as GraspIt! [32] and SynGrasp [33]. The latter is oriented more toward multiple DOF robotic grippers, namely, robotic hands. Usually, pick-point evaluation regards static conditions. However, some researchers also evaluate grasp quality according to the robot's trajectory [34]. A GPU-based physics engine, Isaac Gym, is now also being used in pick-point evaluation [35; 36]. With the introduction of the Incremental Potential Contact Model (IPC) [37], [PERSON] et al. [38] note that the common issue of most physics simulators can now be more successfully contained. The IPC avoids interpenetration artifacts, which include false sticking to the object or intersecting behavior. In a practical example by [38], the engine is used to simulate parallel gripper grasping with soft fingertips.