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:  Directing exploration with prior knowledge 
1. Abstract of Cited Paper (BIB001):  Autonomous approaching objects for an arm-hand robot is a very difficult problem because the possible arm-hand configurations are numerous. In this paper, we propose a modified reinforcement learning algorithm for a multifingerd hand approaching target objects. The proposed approach integrates the heuristic search information with the learning system, and solves the problem of how an arm-hand robot approaches objects before grasping. In addition, this method also overcomes the problem of time consuming of traditional reinforcement learning in the initial learning phase. The algorithm is applied to an arm-hand robot to approach objects before grasping, which can enable the robot to learn approaching skill by trial-and-error and plan its path by itself. The experimental results demonstrate the effectiveness of the proposed algorithm. 
2. Abstract of Cited Paper (BIB002):  Grasping an object is a task that inherently needs to be treated in a hybrid fashion. The system must decide both where and how to grasp the object. While selecting where to grasp requires learning about the object as a whole, the execution only needs to reactively adapt to the context close to the grasp's location. We propose a hierarchical controller that reflects the structure of these two sub-problems, and attempts to learn solutions that work for both. A hybrid architecture is employed by the controller to make use of various machine learning methods that can cope with the large amount of uncertainty inherent to the task. The controller's upper level selects where to grasp the object using a reinforcement learner, while the lower level comprises an imitation learner and a vision-based reactive controller to determine appropriate grasping motions. The resulting system is able to quickly learn good grasps of a novel object in an unstructured environment, by executing smooth reaching motions and preshaping the hand depending on the object's geometry. The system was evaluated both in simulation and on a real robot.

Literature Review:
As discussed in Section 2.1, balancing exploration and exploitation is an important consideration. Task knowledge can be employed to guide to robots curiosity to focus on regions that are novel and promising at the same time. For example, a mobile robot learns to direct attention by employing a modified Q-learning approach using novelty  . Using "corrected truncated returns" and taking into account the estimator variance, a six-legged robot employed with stepping reflexes can learn to walk  . Offline search can be used to guide Q-learning during a grasping task BIB002 . Using upper confidence bounds BIB001 to direct exploration into regions with potentially high rewards, grasping can be learned efficiently BIB003 ).