You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Vision-based Robotic Grasping from Object Localization, Pose Estimation, Grasp Detection to Motion Planning: A Review 
Chapter Title:  Reinforcement learning to grasp the object 
1. Abstract of Cited Paper (BIB001):  This paper presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve this, it first uses a category-agnostic affordance prediction algorithm to select and execute among four different grasping primitive behaviors. It then recognizes picked objects with a cross-domain image classification framework that matches observed images to product images. Since product images are readily available for a wide range of objects (e.g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data. Exhaustive experimental results demonstrate that our multi-affordance grasping achieves high success rates for a wide variety of objects in clutter, and our recognition algorithm achieves high accuracy for both known and novel grasped objects. The approach was part of the MIT-Princeton Team system that took 1st place in the stowing task at the 2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are available online at http://arc.cs.princeton.edu

Literature Review:
The reward function is defined relating to the state of grasping. Viereck et al.  proposed an approach to learn a closed-loop controller for robotic grasping that dynamically guides the gripper to the object. Levine et al. proposed a learning-based method for hand-eye coordination in robotic grasping from monocular images. They utilized the grasp attempts as the reward function, and trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps. The architecture of the CNN grasp predictor is shown in Fig. 36 . Kalashnikov et al.  utilized QT-Opt, a scalable self-supervised vision-based reinforcement learning framework, to perform closed-loop real-world grasping. Their method shows great scalability to unseen objects. Besides, it is able to automatically learn regrasping strategies, probe objects to find the most effective grasps, learn to reposition objects and perform other non-prehensile pre-grasp manipulations, and respond dynamically to disturbances and perturbations. Frederik et al. BIB001 proposed a method on learning robotic skills from raw image observations by using autonomously collected experience. They devise a self-supervised algorithm for learning image registration to keep track of the objects of interest. Experimental results demonstrate that unlabeled data is successfully used to perform complex manipulation tasks. Fang et al. proposed a task-oriented grasping network, which is a learning-based model for jointly predicting task-oriented grasps and subsequent manipulation actions given the visual inputs. Besides, they employed the self-supervised learning paradigm to allow the robot perform grasping and manipulation attempts with the training labels automatically generated. ., 2018] . The input image It, as well as the pregrasp image I0, are fed into a 6 × 6 convolution with stride 2, followed by 3×3 maxpooling and 6 5×5 convolutions. This is followed by a 3×3 maxpooling layer. The motor command vt is processed by one fully connected layer, which is then pointwise added to each point in the response map of pool2 by tiling the output over the special dimensions. The result is then processed by 6 3 × 3 convolutions, 2 × 2 max-pooling, 3 more 3 × 3 convolutions, and two fully connected layers with 64 units, after which the network outputs the probability of a successful grasp through a sigmoid. Each convolution is followed by batch normalization. (Courtesy of Methods considering obstacles The above mentioned methods assume that no obstacles exist on the path between the robotic hand and the grasp positions on target object. There also exist some methods which learn to push the obstacles away and grasp the target object in a closed-loop manner. Zeng et al. BIB002 proposed a model-free deep reinforcement learning method to discover and learn the synergies between pushing and grasping. Their method involves two convolutional networks that map from visual observations to actions. The two networks are trained jointly in a Q-learning framework and are entirely self-supervised by trial and error, where rewards are provided from successful grasps. With picking experiments in both simulation and real-word scenarios, their system quickly learns complex behaviors aiming challenging clutter cases and achieves better grasping success rates and picking efficiencies.