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:  Via points and splines 
1. Abstract of Cited Paper (BIB001):  This work describes the development of a model-free reinforcement learning-based control methodology for the heaving plate, a laboratory experimental fluid system that serves as a model of flapping flight. Through an optimized policy gradient algorithm, we were able to demonstrate rapid convergence (requiring less than 10 minutes of experiments) to a stroke form which maximized the propulsive efficiency of this very complicated fluid-dynamical system. This success was due in part to an improved sampling distribution and carefully selected policy parameterization, both motivated by a formal analysis of the signal-to-noise ratio of policy gradient algorithms. The resulting optimal policy provides insight into the behavior of the fluid system, and the effectiveness of the learning strategy suggests a number of exciting opportunities for machine learning control of fluid dynamics. 
2. Abstract of Cited Paper (BIB002):  The biomechanics community has recently made progress toward understanding the role of rapid arm movements in human stability recovery. However, comparatively little work has been done exploring this type of control in humanoid robots. We provide a summary of recent insights into the functional contributions of arm recovery motions in humans and experimentally demonstrate advantages of this behavior on a dynamically stable mobile manipulator. Using Bayesian optimization, the robot efficiently discovers policies that reduce total energy expenditure and recovery footprint, and increase ability to stabilize after large impacts.

Literature Review:
An open-loop policy may often be naturally represented as a trajectory, either in the space of states or targets or directly as a set of controls. Here, the actions are only a function of time, which can be considered as a component of the state. Such spline-based policies are very suitable for compressing complex trajectories into few parameters. Typically the desired joint or Cartesian position, velocities, and/or accelerations are used as actions. To minimize the required number of parameters, not every point is stored. Instead, only important via-points are considered and other points are interpolated. BIB001 optimized the position and timing of such via-points in order to learn a kendama task (a traditional Japanese toy similar to a ball in a cup). A well-known type of a via-point representations are splines, which rely on piecewise-defined smooth polynomial functions for interpolation. For example, BIB002 used a periodic cubic spline as a policy parametrization for a flapping system and BIB003 used a cubic spline to represent arm movements in an impact recovery task.