In this paper I claim that one of the main characteristics that makes the evolutionary robotics approach suitable for the study of adaptive behavior in natural and artificial agents is the possibility of relying largely on a self-organization process. Indeed, by using artificial evolution the role of the designer may be limited to the specification of a fitness function which measures the ability of a given robot to perform a desired task. From an engineering point of view, the main advantage of relying on self-organization is the fact that the designer does not need to divide the desired behavior into simple basic behaviors to be implemented in separate layers (or modules) of the robot control system. By selecting individuals for their ability to perform the desired behavior as a whole, simple basic behaviors can emerge from the interaction between several processes in the control system and from the interaction between the robot and the environment. From the point of view of the study of natural systems, the possibility of evolving robots that are free to select their way to solve a task by interacting with their environment may help us to understand how natural organisms produce adaptive behavior. Finally, the attempt to scale up to more complex tasks may help us to identify what the critical features of natural evolution are which allowed the emergence of the extraordinary variety of highly adapted life forms present on the planet.