# RGB-D-Based Robotic Grasping in Fusion Application Environments
## 1 Introduction  
Robotic grasping is one of the most effective solutions for handling grasping work in challenging environments. In nuclear fusion applications, many tasks need to be carried out by the robotic arm, including robotic grasping, as shown in Figure 1.  
As a fundamental robotic task, research into robotic grasping has been carried out for a long time. In the early works, researchers need to use the traditional Computer Vision (CV) algorithms to obtain the right grab point. Just as in [1], researchers use a single camera to observe the same towel from different angles of view, implement the border classification  
Figure 1: In the fusion environment, many tasks need to be carried out via robotic grasping. In this figure, the robotic arm on the right side is gripping some components from the toolbox on the left. and corner grasp point detection algorithms based on these data, and eventually achieve the towel folding task. These algorithms were quickly replaced by Convolutional Neural Network (CNN)-based algorithms after the rise of deep learning. Thus, in this paper, we focus on learning-based approaches. In [2], based on a monocular camera, [PERSON] et al. proposed a learning-based approach to hand-eye coordination for robotic grasping, and they focused on training the CNN to understand the spatial relationship between the gripper and objects in the scene so the neural network (NN) could predict the probability that the task-space motion of the gripper will result in successful grasps. Due to the nature of the robotic grasping task, the final target is to drive the gripper to the optimal gripping pose; the best input data are the 3D spatial coordinates that are hard to obtain from a monocular camera. Thus, after the RGB-D camera was introduced and mature, it quickly became a popular sensor for researchers. Now, the input data of most learning-based robotic grasping methods are based on the RGB-D messages provided by the depth cameras. Just as in [3, 4], these approaches output the appropriate grasping rectangle for each object through the CNN structure. The centre of the rectangle is the grasp point. The long side and the short side of the rectangle correspond to the grasp angle and grasp width, respectively. [PERSON] et al. [5] introduced a Generative Residual Convolutional Neural Network (GR-ConvNet) to infer multiple grasp rectangles for multiple objects in the clutter in real-time; they also output a quality for each grasp pose. On the basis of [5], [PERSON] et al. [6] adjusted the structure of the neural network and achieved a better result on accuracy and efficiency. In [7], researchers proposed a generative adversarial network based on monocular RGB images which can achieve an outstanding grasping result both in simulation and realistic environments. [PERSON] et al. [8] proposed a 6-DoF pose estimation NN which could output the semantic label of each object. The whole training set for the NN is also built on synthetic data. In [9, 10], the RGB-D information is converted into point clouds and neural networks are developed to give contact points for each object separately based on object recognition in the 3D space. In [11], [PERSON] et al. proposed a method that leverages hand-object contact heatmaps generated in a self-supervised manner in simulation to achieve a dense, point-wise grasping network, and they also introduced the \"Non-Uniform Normalized Object Coordinate Space\" (NUNOCS) representation for learning category-level object 6D poses and 3D scaling, which allows non-uniform scaling across three dimensions. With the increasing number of approaches based on generative data, researchers are placing greater demands on the quality of the generated models. In order to make the virtual data as close as possible to the real model, [PERSON] et al. [12] explored a data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to object synthesis. Meanwhile, [PERSON] et al.