# Robotic System for Post Office Package Handling
## 2 Related Work  
For several decades, robotic systems have played a crucial role in automating various systems [4]. These systems are traditionally deployed in static and known environments where robotic motions can be fully preprogrammed or rely on simple input devices for an adjustment in the grasping position, typically varying only in two dimensions [5]. Robotic systems in such environments have several prerequisites that are frequently not feasible to comply with due to different limitations of the manufacturing plant. A typical limitation is the availability of space to divide and position objects on one level, which is also the case for the problem addressed in this article. In the post office parcel-handling context, the sorting process can be fully automated; however, it requires a substantially large space in the factory or even redesigning the whole process, where an industrial robot system, if used at all, could then be deployed with a rather simple 2D vision system [6; 7]. In situations where space cannot be allocated to create structured conditions for the robotic system, uncertainty inevitably arises in the robot's workspace. This makes it challenging to grasp objects positioned in this region and necessitates the use of so-called smart industrial robot control [8]. While there are many application areas for smart industrial robot control [9], grasping objects in unstructured or weakly structured environments [10] remains one of the greatest challenges in modern robotics [8]. The grasping pipeline typically relies on an additional computer vision system that realizes a computer-vision-based control. This control method can be composed of several different approaches in both system architecture design level and learning strategies [11; 12]. Different scenarios or combinations of conditions in the region of interest have been the main drive in the development of various approaches to tackle the grasping problem, mainly depending on the level of uncertainty in the region of interest and the type of the objects. Even though there can be a range of flexibility in the scenarios where the system can sustain the required level of performance, this can have a negative influence on the performance efficiency and maintainability aspects [13]. Firstly, the deep-learning-based approaches can be divided into two branches--working with known or unknown objects, e.g., model-based or model-free methods [14]. Even though model-based methods have become quite effective [15], they require previous knowledge about the objects of interest, which can become quite cumbersome when the training data need to be acquired. Recently, this aspect has been addressed by synthetic data generation methods [16] and sim-to-real transfer techniques [17]. Nevertheless, the goal of all the grasping techniques is to estimate the grasp pose of the object. For model-based methods, several steps before the estimated grasp pose can be present, such as object detection [18] and segmentation with respective post-processing steps depending on the  
Figure 1: Universal Robot UR5 moving around post office packages. application scenario [19] or various approaches that directly estimate the 6D pose [20; 21] of the object, from which the grasp pose can be derived. On the other hand, model-free approaches search for the grasp pose directly without relying on prior knowledge of the object for training the system. These methods are highly desirable as they can generalize to previously unseen objects. They circumvent the pose estimation step and directly estimate the grasp pose by identifying a graspable spot in the region of interest [8]. One important model-free grasping method is Dex-Net [22]. To train this method, grasps across 1500 3D object models were analyzed, resulting in a dataset of 2.8 million point clouds, suction grasps, and robustness labels (Dex-Net 3.0). The dataset was then used to train a grasp quality convolutional neural network (GQ-CNN) to classify robust suction targets in single object point clouds.