# RGB-D-Based Robotic Grasping in Fusion Application Environments
## 1 Introduction
[13] proposed a generation network based on CNN, which could complement the 3D shape of an object with an incomplete points cloud from several points of view. With adequate offline training, the generation network could complement the 3D shape accurately and rapidly. Obtaining training data is always the most important and most difficult part of the supervised learning-based method. Some researchers are beginning to explore the use of unsupervised learning to solve the robotic grasping problem. In [14], researchers trained a vision-based closed-loop grasping reinforcement learning (RL) agent in a simulation environment, then transferred it to the real world and completed the grasping task successfully. [PERSON] et al. [15] presented a deep reinforcement learning-based method to solve the problem of robotic grasping using Visio-motor feedback. [PERSON] et al. [16] compared various RL algorithms in a virtual and realistic environment and selected an off-policy algorithm that could handle the task well. From these related works, the learning-based ones exhibit several trends:  
* Using the generated data as training data and translating the results from virtual to real;* Transfer the task from individual objects to cluster objects grasping. Most robotic grasping methods focus on building an end-to-end grasping network that could output a result that contains the grasp point, pose, and width. This is the right development direction and works well under ideal circumstances. However, in the fusion application environment, all components, including the interior of the vacuum chamber and the components to be assembled, are made of the same metal materials and have smooth surfaces. Almost all the RGB-D cameras' performance on the non-optimal smooth surfaces with specular reflection is poor. The CNN-based grasping network algorithm can hardly yield stable results in such an environment. Meanwhile, due to the black-box nature of the processes within neural networks, researchers could hardly eliminate such uncertainty by changing or improving the structural design of an NN, so the end-to-end NN approaches would transmit this uncertainty directly to the final decision which is intolerable in many industrial scenarios. To eliminate such uncertainty, some researchers work hard to ensure that the training set is extensive and comprehensive enough to cover all kinds of situations, but it is too expensive. Moreover, despite the availability of many open-source benchmarks, training datasets with labels are still very scarce in some specific scenarios, such as a fusion vacuum vessel (VV), so achieving consistent and high-quality robotic grasping in such a challenging environment is still quite tricky. A more efficient approach is to abandon the obsessive pursuit of an end-to-end network and add traditional but controllable approaches between the network output and the final decision, thus eliminating these uncertainties. In our case, we decompose the grasping task into object recognition based on an instance segmentation network and grasping pose computation based on plane extraction and clustering algorithm. In this paper, we chose a well-developed instance segmentation network that has been proposed by [PERSON] et al. [17] as the fundamental of our robotic grasping methods. Then, we use the clustering algorithm to obtain more stable segmentation results and calculate the correct grasping points and poses via plane extraction. The final experimental results show that our method can achieve a high success grasping rate in the challenging environment.