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:  Traditional methods 
1. Abstract of Cited Paper (BIB001):  This paper develops an enhanced teaching interface tested on both a Baxter robot and a KUKA iiwa robot. Movements are collected from a human demonstrator by using a Kinect v2 sensor, and then the data is sent to a remote PC for the teleoperation with Baxter. Meanwhile, data is saved locally for the playback process of the Baxter. The dynamic movement primitive (DMP) is used to model and generalize the movements. In order to learn from multiple demonstrations accurately, dynamic time warping (DTW), is used to pretreat the data recorded by the robot platform and Gaussian mixture model (GMM), aiming to generate multiple patterns after the teaching process, are employed for the calculation of the DMP. Then the Gaussian mixture regression (GMR) algorithm is applied to generate a synthesized trajectory with smaller position errors in 3D space. This proposed approach is tested by performing two tasks on a KUKA iiwa and a Baxter robot.

Literature Review:
Traditional methods consider the dynamics of the motion and generate motion primitives. The Dynamic Movement Primitives (DMPs) [Schaal, 2006] are one of the most popular motion representations that can serve as an reactive feedback controller. DMPs are units of action that are formalized as stable nonlinear attractor systems. They encode kinematic control policies as differential equations with the goal as the attractor BIB001 . A nonlinear forcing term allows shaping the transient behavior to the attractor without endangering the well-defined attractor properties. Once this nonlinear term has been initialized, e.g., via imitation learning, this movement representation can be generalized with respect to task parameters such as start, goal, and duration of the movement. DMPs have been successfully used to in imitation learning, reinforcement learning, movement recognition and so on. Colomé et al. [Colomé and Torras, 2018] addressed the process of simultaneously learning a DMP-characterized robot motion and its underlying joint couplings through linear dimensionality reduction, which provides valuable qualitative information leading to a reduced and intuitive algebraic description of such motion. Rai et al. BIB001 proposed learning feedback terms for reactive planning and control. They investigate how to use machine learning to add reactivity to a previously learned nominal skilled behaviors represented by DMPs. Pervez and Lee  proposed a generative model for modeling the forcing terms in a task parameterized DMP. Li et al. BIB002 proposed an enhanced teaching interface for a robot using DMP and Gaussian Mixture Model (GMM). The movements are collected from a human demonstrator by using a Kinect v2 sensor. GMM is employed for the calculation of the DMPs, which model and generalize the movements.