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 robotics 
1. Abstract of Cited Paper (BIB001):  Abstract A general theory of movement-pattern perception based on bi-directional theory for sensory-motor integration can be used for motion capture and learning by watching in robotics. We demonstrate our methods using the game of Kendama, executed by the SARCOS Dextrous Slave Arm, which has a very similar kinematic structure to the human arm. Three ingredients have to be integrated for the successful execution of this task. The ingredients are (1) to extract via-points from a human movement trajectory using a forward-inverse relaxation model, (2) to treat via-points as a control variable while reconstructing the desired trajectory from all the via-points, and (3) to modify the via-points for successful execution. In order to test the validity of the via-point representation, we utilized a numerical model of the SARCOS arm, and examined the behavior of the system under several conditions. Copyright © 1996 Elsevier Science Ltd. 
2. Abstract of Cited Paper (BIB002):  This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning. 
3. Abstract of Cited Paper (BIB003):  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. 
4. Abstract of Cited Paper (BIB004):  This paper presents a machine learning approach to optimizing a quadrupedal trot gait for forward speed. Given a parameterized walk designed for a specific robot, we propose using a form of policy gradient reinforcement learning to automatically search the set of possible parameters with the goal of finding the fastest possible walk. We implement and test our approach on a commercially available quadrupedal robot platform, namely the Sony Aibo robot. After about three hours of learning, all on the physical robots and with no human intervention other than to change the batteries, the robots achieved a gait faster than any previously known gait known for the Aibo, significantly outperforming a variety of existing hand-coded and learned solutions. 
5. Abstract of Cited Paper (BIB005):  Autonomous helicopter flight is widely regarded to be a highly challenging control problem. This paper presents the first successful autonomous completion on a real RC helicopter of the following four aerobatic maneuvers: forward flip and sideways roll at low speed, tail-in funnel, and nose-in funnel. Our experimental results significantly extend the state of the art in autonomous helicopter flight. We used the following approach: First we had a pilot fly the helicopter to help us find a helicopter dynamics model and a reward (cost) function. Then we used a reinforcement learning (optimal control) algorithm to find a controller that is optimized for the resulting model and reward function. More specifically, we used differential dynamic programming (DDP), an extension of the linear quadratic regulator (LQR). 
6. Abstract of Cited Paper (BIB006):  Many motor skills in humanoid robotics can be learned using parametrized motor primitives as done in imitation learning. However, most interesting motor learning problems are high-dimensional reinforcement learning problems often beyond the reach of current methods. In this paper, we extend previous work on policy learning from the immediate reward case to episodic reinforcement learning. We show that this results in a general, common framework also connected to policy gradient methods and yielding a novel algorithm for policy learning that is particularly well-suited for dynamic motor primitives. The resulting algorithm is an EM-inspired algorithm applicable to complex motor learning tasks. We compare this algorithm to several well-known parametrized policy search methods and show that it outperforms them. We apply it in the context of motor learning and show that it can learn a complex Ball-in-a-Cup task using a real Barrett WAM™ robot arm. 
7. Abstract of Cited Paper (BIB007):  Stochastic optimization arises in a wide range of problems, and as a result the ideas have been developed in different communities, creating fragmented styles in terms of notation, modeling and algorithms. Some of these variations can be explained by differences in application, as well as differences in research styles. We attempt to bridge these communities by describing how to translate notational systems, while contrasting modeling and algorithmic strategies. We also touch on differences in research styles which reflect expectations for a publishable contribution as well as the style of publication outlets used by different communities. This version of the paper has been written largely without references (more references may be added later). At this point, I think it is fair to say that all of the concepts contained in this paper have been expressed somewhere in the literature, or are at least known among groups of researchers. This article synthesizes these ideas in a coherent way, and while multiple notational systems are reviewed, there is also an attempt to develop some common notational principles that will help to foster communication across communities. This article is intended as a discussion piece and is not intended for journal publication. The goal is to help foster communication between the different communities. Constructive thoughts and comments are warmly appreciated. Since this document will evolve over time, please email your comments to powell@princeton.edu. You may put notes directly on the pdf (this is best), but if you put them in email, please include the date on the front page so I know which version you are using.

Literature Review:
Robotics as a reinforcement learning domain differs considerably from most well-studied reinforcement learning benchmark problems. In this article, we highlight the challenges faced in tackling these problems. Problems in robotics are often best represented with high-dimensional, continuous states and actions (note that the 10-30 dimensional continuous actions common in robot reinforcement learning are considered large BIB008 ). In robotics, it is often unrealistic to assume that the true state is completely observable and noise-free. The learning system will not be able to know precisely in which state it is and even vastly different states might look very similar. Thus, robotics reinforcement learning are often modeled as partially observed, a point we take up in detail in our formal model description below. The learning system must hence use filters to estimate the true state. It is often essential to maintain the information state of the environment that not only contains the raw observations but also a notion of uncertainty on its estimates (e.g. both the mean and the variance of a Kalman filter tracking the ball in the robot table tennis example). Experience on a real physical system is tedious to obtain, expensive and often hard to reproduce. Even getting to the same initial state is impossible for the robot table tennis system. Every single trial run, also called a roll-out, is costly and, as a result, such applications force us to focus on difficulties that do not arise as frequently in classical reinforcement learning benchmark examples. In order to learn within a reasonable time frame, suitable approximations of state, policy, value function, and/or system dynamics need to be introduced. However, while real-world experience is costly, it usually cannot be replaced by learning in simulations alone. In analytical or learned models of the system even small modeling errors can accumulate to a substantially different behavior, at least for highly dynamic tasks. Hence, algorithms need to be robust with respect to models that do not capture all the details of the real system, also referred to as under-modeling, and to model uncertainty. Another challenge commonly faced in robot reinforcement learning is the generation of appropriate reward functions. Rewards that guide the learning system quickly to success are needed to cope with the cost of real-world experience. This problem is called reward shaping  and represents a substantial manual contribution. Specifying good reward functions in robotics requires a fair amount of domain knowledge and may often be hard in practice. Not every reinforcement learning method is equally suitable for the robotics domain. In fact, many of the methods thus far demonstrated on difficult problems have been model-based BIB006 and robot learning systems often employ policy-search methods rather than value-function-based approaches BIB001 BIB002 BIB004 BIB005  BIB007 Peters and Schaal, 2008a,b; . Such design choices stand in contrast to possibly the bulk of the early research in the machine learning community BIB003  . We attempt to give a fairly complete overview on real robot reinforcement learning citing most original papers while grouping them based on the key insights employed to make the robot reinforcement learning problem tractable. We isolate key insights such as choosing an appropriate representation for a value function or policy, incorporating prior knowledge, and transfer knowledge from simulations. This paper surveys a wide variety of tasks where reinforcement learning has been successfully applied to robotics. If a task can be phrased as an optimization problem and exhibits temporal structure, reinforcement learning can often be profitably applied to both phrase and solve that problem. The goal of this paper is twofold. On the one hand, we hope that this paper can provide indications for the robotics community which type of problems can be tackled by reinforcement learning and provide pointers to approaches that are promising. On the other hand, for the reinforcement learning community, this paper can point out novel real-world test beds and remarkable opportunities for research on open questions. We focus mainly on results that were obtained on physical robots with tasks going beyond typical reinforcement learning benchmarks. We concisely present reinforcement learning techniques in the context of robotics in Section 2. The challenges in applying reinforcement learning in robotics are discussed in Section 3. Different approaches to making reinforcement learning tractable are treated in Sections 4-6. In Section 7, the example of a ball in a cup is employed to highlight which of the various approaches discussed in the paper have been particularly helpful to make such a complex task tractable. Finally, in Section 8, we summarize the specific problems and benefits of reinforcement learning in robotics and provide concluding thoughts on the problems and promise of reinforcement learning in robotics.