# Object-Independent Grasping in Heavy Clutter
## 1 Introduction  
For a robot to grasp a target object in a cluttered environment successfully, where many objects are stacked in a small space such as a box, the gripper should not collide with surrounding objects or the walls of the box. Because of the recent development of artificial neural networks (ANN), grasping algorithms can provide excellent performance if sufficient data are provided for learning [1; 2; 3]. However, learning often requires several robots and devices to compute the vast amount of data needed. Additionally, in cases where the target objects change frequently (such as in the logistics industry), ANN-based grasping algorithms have to be retrained, which is inefficient [4]. Therefore, an algorithm that allows the robot to grasp unknown objects without excessive learning is necessary. Many grasping algorithms use either a geometry-based or data-driven method. The former is a traditional method in which the grasp pose is estimated by predicting the exact three-dimensional (3D) position of an object [5; 6] or by matching the 3D point cloud using known 3D computer-aided design (CAD) models [7; 8; 9]. Therefore, applying this method to a new object is cumbersome, because an accurate CAD model is needed and cannot always be obtained. Thus, the estimated pose of the target object may be inaccurate. Because of this, methods that estimate the pose of the objects in 3D environments without CAD models have been recently proposed [10]. Though geometry-based grasping methods are often used because the CAD models of manufactured objects are available, the logistics industry is unlikely to have CAD models of the products. Thus, these methods are hardly applied in logistics. In contrast, in the data-driven method, the grasp poses of the objects are estimated using an ANN-based learning scheme. This method generally has a higher success rate than the traditional geometry-based methods. In this method, RGB images [11; 12], depth images [3; 13], or both [14] can be used. However, a data-driven method requires a large amount of data that are manuallylabeled; thus, collecting the training dataset is time-consuming and costly, and the training time is long. To solve these issues, methods such as obtaining the data from simulations [13] and using generative adversarial networks (GANs) [15] have been proposed. However, simulations may have low success rates because of the difference to reality, and the GAN-based method requires a long training time [16] and is hard to be trained. There are also reinforcement learning-based schemes. These, similar to deep learning-based methods, have good performance when properly taught. However, their learning requires an enormous amount of data. For instance, Google collected more than 0.9 million grasp data over several months using 14 robots [1; 2]. Using a single robot, this would have taken years. Furthermore, even when these data are collected, the algorithm can hardly operate if they are not obtained in different environments. Herein, we propose a bin-picking scheme based on the depth difference image (DDI), which estimates the graspability by analyzing the space around the object to be grasped. By DDI-based bin picking (DBP), a robot with a two-finger gripper can grasp unknown objects in a cluttered environment. This does not require a learning process (which requires a substantial amount of data) or CAD models of the target objects. Therefore, the most significant contribution of this study is to provide a generalized grasp solution that does not need prior information, including CAD models and training data. DBP consists of a grasp candidate generator, grasp pose evaluator, and grasp pose modifier. The grasp candidate generator considers the shape of an object and the surrounding space, generating a group of candidates for the robot to attempt grasping. The grasp pose evaluator determines the most appropriate grasp candidate using a Gaussian mixture model (GMM) and DDI.