# Robotic Grasping of Unknown Objects Based on Deep-Learning Based Feature Detection
## 1 Introduction  
Grasping has remained a significant focus of research in robotics for decades, enabling robots to interact proficiently with objects in the real world. Humans, upon looking at an object, even for the first time, instinctively know where to pick it up from. This, coupled with the incredible dexterity of the human hand, makes any grasping attempt almost always a success. However, this is a highly challenging task for current robot systems, as robots do not have such instinct nor dexterity [1]. Modern-day robots are highly advanced; they can be programmed to perform highly complex movements with high accuracy and precision. However, these programs tend to be specific to a certain object or environment. A change in any of these circumstances renders these programs unreliable and a new program has to be designed. This lack of flexibility in robot grasping is a challenging problem [2; 3]. The rise of deep learning algorithms has provided a possible solution to this problem [4] by introducing data-driven techniques. Through the interactions between sensors and the environment, grasp pose generations can be achieved by imitating human grasping strategies [5]. Integrating Deep Convolutional Neural Networks (CNNs) into grasping algorithms allows robots to deal with changes in the environment more readily. Modern data-driven techniques for robotic grasping involve teaching a robot with large amounts of data instead of training with physical object models [6]. These training data usually contain many images of each object in various positions and orientations, and the grasping pose is manually labelled [7; 8]. Once the training is completed, the robot can grasp these objects regardless of how they are placed. Significant past works in grasping such as those by [PERSON] et al. [9] and [PERSON] et al. [10] approached the problem by amassing large quantities of data over an extended period oftime, 50,000 and 800,000 datapoints, respectively. Many grasps are executed, and the results of those grasps are recorded in terms of success or failure. This allows the algorithm to learn what grasps are good. The resultant algorithm was effective in grasping specific objects. Another technique that has been explored is to use object detectors as part of the grasping algorithm. Modern object detectors are highly accurate and run considerably fast; a few iterations can run in a second, making it feasible for real-time usage. This is comparative to the early stages of CNN development, whereby the high computational cost and long run times per iteration make them unsuitable for real-time detection and grasping [11]. [PERSON] et al. [12] have trained an object detector using the images and grasping points of two object classes from the Open Image Dataset (OID). The resultant model can pick up these two classes of objects successfully with around 70% precision. [PERSON] et al. [13] approach grasping by integrating multi-agent deep reinforcement learning with an object detector. The resultant MA-TD3H algorithm has a very high success rate in grasping objects, but can only grasp the three objects that it is trained on, and it is untested on unknown objects outside of this training dataset. Some other studies find that using an object detector alone is too limited and approach the problem differently. [PERSON] et al. [14], [PERSON] et al. [15], and [PERSON] et al. [16] propose a two-stage cascaded algorithm by first using the object detector to identify and localize an object before using a second Grasp Generation CNN (GGCNN) to determine the grasp position. A more recent study by [PERSON] et al. [17] first utilizes an object recognition algorithm to recognize six different categories of objects, and then develops a GGCNN that is trained with grasp positions for each category. Upon encountering an object, the object recognition module determines the category that best matches it. The GGCNN then generates a grasp position for it. The approach sports an 86% grasp success rate.