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: Reinforcement learning in robotics: A survey 
Chapter Title:  Reinforcement learning in the context of machine learning 
1. Abstract of Cited Paper (BIB001):  Skill acquisition is a difficult , yet important problem in robot performance. The authors focus on two skills, namely robotic assembly and balancing and on two classic tasks to develop these skills via learning: the peg-in hole insertion task, and the ball balancing task. A stochastic real-valued (SRV) reinforcement learning algorithm is described and used for learning control and the authors show how it can be used with nonlinear multilayer ANNs. In the peg-in-hole insertion task the SRV network successfully learns to insert to insert a peg into a hole with extremely low clearance, in spite of high sensor noise. In the ball balancing task the SRV network successfully learns to balance the ball with minimal feedback. > 
2. Abstract of Cited Paper (BIB002):  By now it is widely accepted that learning a task from scratch, i.e., without any prior knowledge, is a daunting undertaking. Humans, however, rarely attempt to learn from scratch. They extract initial biases as well as strategies how to approach a learning problem from instructions and/or demonstrations of other humans. For teaming control, this paper investigates how learning from demonstration can be applied in the context of reinforcement learning. We consider priming the Q-function, the value function, the policy, and the model of the task dynamics as possible areas where demonstrations can speed up learning. In general nonlinear learning problems, only model-based reinforcement learning shows significant speed-up after a demonstration, while in the special case of linear quadratic regulator (LQR) problems, all methods profit from the demonstration. In an implementation of pole balancing on a complex anthropomorphic robot arm, we demonstrate that, when facing the complexities of real signal processing, model-based reinforcement learning offers the most robustness for LQR problems. Using the suggested methods, the robot learns pole balancing in just a single trial after a 30 second long demonstration of the human instructor. 
3. Abstract of Cited Paper (BIB003):  Summary This review investigates two recent developments in artificial intelligence and neural computation: learning from imitation and the development of humanoid robots. It will be postulated that the study of imitation learning offers a promising route to gain new insights into mechanisms of perceptual motor control that could ultimately lead to the creation of autonomous humanoid robots. Imitation learning focuses on three important issues: efficient motor learning, the connection between action and perception, and modular motor control in form of movement primitives. It will be reviewed how research on representations of, and functional connections between action and perception have contributed to our understanding of motor acts of other beings. The recent discovery that some areas in the primate brain are active during both movement perception and execution has provided a hypothetical neural basis of imitation. Computational approaches to imitation learning will also be described, initially from the perspective of traditional AI and robotics, but also from the perspective of neural network models and statistical learning research. Parallels and differences between biological and computational approaches to imitation will be highlighted and an overview of current projects that actually employ imitation learning for humanoid robots will be given. 
4. Abstract of Cited Paper (BIB004):  Many control problems in the robotics field can be cast as partially observed Markovian decision problems (POMDPs), an optimal control formalism. Finding optimal solutions to such problems in general, however is known to be intractable. It has often been observed that in practice, simple structured controllers suffice for good sub-optimal control, and recent research in the artificial intelligence community has focused on policy search methods as techniques for finding sub-optimal controllers when such structured controllers do exist. Traditional model-based reinforcement learning algorithms make a certainty equivalence assumption on their learned models and calculate optimal policies for a maximum-likelihood Markovian model. We consider algorithms that evaluate and synthesize controllers under distributions of Markovian models. Previous work has demonstrated that algorithms that maximize mean reward with respect to model uncertainty leads to safer and more robust controllers. We consider briefly other performance criterion that emphasize robustness and exploration in the search for controllers, and note the relation with experiment design and active learning. To validate the power of the approach on a robotic application we demonstrate the presented learning control algorithm by flying an autonomous helicopter. We show that the controller learned is robust and delivers good performance in this real-world domain. 
5. Abstract of Cited Paper (BIB005):  We prove a quantitative connection between the expected sum of rewards of a policy and binary classification performance on created subproblems. This connection holds without any unobservable assumptions (no assumption of independence, small mixing time, fully observable states, or even hidden states) and the resulting statement is independent of the number of states or actions. The statement is critically dependent on the size of the rewards and prediction performance of the created classifiers.We also provide some general guidelines for obtaining good classification performance on the created subproblems. In particular, we discuss possible methods for generating training examples for a classifier learning algorithm. 
6. Abstract of Cited Paper (BIB006):  We present a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a structure in which to categorize LfD research. Specifically, we analyze and categorize the multiple ways in which examples are gathered, ranging from teleoperation to imitation, as well as the various techniques for policy derivation, including matching functions, dynamics models and plans. To conclude we discuss LfD limitations and related promising areas for future research. 
7. Abstract of Cited Paper (BIB007):  We present Searn, an algorithm for integrating search and learning to solve complex structured prediction problems such as those that occur in natural language, speech, computational biology, and vision. Searn is a meta-algorithm that transforms these complex problems into simple classification problems to which any binary classifier may be applied. Unlike current algorithms for structured learning that require decomposition of both the loss function and the feature functions over the predicted structure, Searn is able to learn prediction functions for any loss function and any class of features. Moreover, Searn comes with a strong, natural theoretical guarantee: good performance on the derived classification problems implies good performance on the structured prediction problem. 
8. Abstract of Cited Paper (BIB008):  Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches provide stronger guarantees in this setting, but remain somewhat unsatisfactory as they train either non-stationary or stochastic policies and require a large number of iterations. In this paper, we propose a new iterative algorithm, which trains a stationary deterministic policy, that can be seen as a no regret algorithm in an online learning setting. We show that any such no regret algorithm, combined with additional reduction assumptions, must find a policy with good performance under the distribution of observations it induces in such sequential settings. We demonstrate that this new approach outperforms previous approaches on two challenging imitation learning problems and a benchmark sequence labeling problem. 
9. Abstract of Cited Paper (BIB009):  Nearly every structured prediction problem in computer vision requires approximate inference due to large and complex dependencies among output labels. While graphical models provide a clean separation between modeling and inference, learning these models with approximate inference is not well understood. Furthermore, even if a good model is learned, predictions are often inaccurate due to approximations. In this work, instead of performing inference over a graphical model, we instead consider the inference procedure as a composition of predictors. Specifically, we focus on message-passing algorithms, such as Belief Propagation, and show how they can be viewed as procedures that sequentially predict label distributions at each node over a graph. Given labeled graphs, we can then train the sequence of predictors to output the correct labeling s. The result no longer corresponds to a graphical model but simply defines an inference procedure, with strong theoretical properties, that can be used to classify new graphs. We demonstrate the scalability and efficacy of our approach on 3D point cloud classification and 3D surface estimation from single images.

Literature Review:
(a) The OBELIX robot is a wheeled mobile robot that learned to push boxes BIB001 with a valuefunction-based approach. (Reprinted with permission from Sridhar Mahadevan.) (b) A Zebra Zero robot arm learned a peg-in-hole insertion task BIB002 with a model-free policy gradient approach. (Reprinted with permission from Rod Grupen.) (c) Carnegie Mellon's autonomous helicopter leveraged a model-based policy-search approach to learn a robust flight controller BIB005 . (d) The Sarcos humanoid DB learned a pole-balancing task BIB003 using forward models. (Reprinted with permission from Stefan Schaal.) the outcome of the choices made. From this information, a "good", or ideally optimal, policy (i.e. strategy or controller) must be deduced. Reinforcement learning may be understood by contrasting the problem with other areas of study in machine learning. In supervised learning BIB006 , an agent is directly presented a sequence of independent examples of correct predictions to make in different circumstances. In imitation learning, an agent is provided demonstrations of actions of a good strategy to follow in given situations BIB007 BIB004 . To aid in understanding the reinforcement learning problem and its relation with techniques widely used within robotics, Figure 2 provides a schematic illustration of two axes of problem variability: the complexity of sequential interaction and the complexity of reward structure. This hierarchy of problems, and the relations between them, is a complex one, varying in manifold attributes and difficult to condense to something like a simple linear ordering on problems. Much recent work in the machine learning community has focused on understanding the diversity and the inter-relations between problem classes. The figure should be understood in this light as providing a crude picture of the relationship between areas of machine learning research important for robotics. Fig. 2 . An illustration of the inter-relations between well-studied learning problems in the literature along axes that attempt to capture both the information and complexity available in reward signals and the complexity of sequential interaction between learner and environment. Each problem subsumes those to the left and below; reduction techniques provide methods whereby harder problems (above and right) may be addressed using repeated application of algorithms built for simpler problems BIB006 . Each problem subsumes those that are both below and to the left in the sense that one may always frame the simpler problem in terms of the more complex one; note that some problems are not linearly ordered. In this sense, reinforcement learning subsumes much of the scope of classical machine learning as well as contextual bandit and imitation learning problems. Reduction algorithms BIB006 are used to convert effective solutions for one class of problems into effective solutions for others, and have proven to be a key technique in machine learning. At lower left, we find the paradigmatic problem of supervised learning, which plays a crucial role in applications as diverse as face detection and spam filtering. In these problems (including binary classification and regression), a learner's goal is to map observations (typically known as features or covariates) to actions which are usually a discrete set of classes or a real value. These problems possess no interactive component: the design and analysis of algorithms to address these problems rely on training and testing instances as independent and identical distributed random variables. This rules out any notion that a decision made by the learner will impact future observations: supervised learning algorithms are built to operate in a world in which every decision has no effect on the future examples considered. Further, within supervised learning scenarios, during a training phase the "correct" or preferred answer is provided to the learner, so there is no ambiguity about action choices. More complex reward structures are also often studied: one example is known as cost-sensitive learning, where each training example and each action or prediction is annotated with a cost for making such a prediction. Learning techniques exist that reduce such problems to the simpler classification problem, and active research directly addresses such problems as they are crucial in practical learning applications. Contextual bandit or associative reinforcement learning problems begin to address the fundamental problem of exploration versus exploitation, as information is provided only about a chosen action and not what might have been. These find widespread application in problems as diverse as pharmaceutical drug discovery to ad placement on the web, and are one of the most active research areas in the field. Problems of imitation learning and structured prediction may be seen to vary from supervised learning on the alternate dimension of sequential interaction. Structured prediction, a key technique used within computer vision and robotics, where many predictions are made in concert by leveraging inter-relations between them, may be seen as a simplified variant of imitation learning BIB008 BIB009 . In imitation learning, we assume that an expert (for example, a human pilot) that we wish to mimic provides demonstrations of a task. While "correct answers" are provided to the learner, complexity arises because any mistake by the learner modifies the future observations from what would have been seen had the expert chosen the controls. Such problems provably lead to compounding errors and violate the basic assumption of independent examples required for successful supervised learning. In fact, in sharp contrast with supervised learning problems where only a single data set needs to be collected, repeated interaction between learner and teacher appears to both necessary and sufficient BIB010 to provide performance guarantees in both theory and practice in imitation learning problems. Reinforcement learning embraces the full complexity of these problems by requiring both interactive, sequential prediction as in imitation learning as well as complex reward structures with only "bandit" style feedback on the actions actually chosen. It is this combination that enables so many problems of relevance to robotics to be framed in these terms; it is this same combination that makes the problem both information-theoretically and computationally hard. We note here briefly the problem termed "baseline distribution reinforcement learning": this is the standard reinforcement learning problem with the additional benefit for the learner that it may draw initial states from a distribution provided by an expert instead of simply an initial state chosen by the problem. As we describe further in Section 5.1, this additional information of which states matter dramatically affects the complexity of learning.