After reviewing current approaches in Evolutionary Robotics, we point to directions of research that re likely to bring interesting results in the future. e then address two crucial aspects for future developments of Evolutionary Robotics: choice of fitness functions and scalability to real-world situations. In the first case we suggest framework to describe fitness functions, choose them according to the situation constraints, and compare available experiments in the literature on evolutionary robotics. In the second case, we suggest way to make experimental results applicable to real- world situations by evolving online continuous adaptive controllers. We also give an overview of recent experimental results showing that the suggested approaches pro- duce qualitatively superior abilities, scale up to more complex architectures, smoothly transfer from simulations to real robots and across different robotic platforms, and autonomously adapt in few seconds to several sources of strong variability that were not included during the evolutionary run.