We apply evolutionary algorithm (EA) to the design of controller for adaptive robots. EAs can be successful for more complicated tasks, where traditional engineering methods struggle to provide a good solution. However a simple evolutionary process is computationally expensive and works only for tasks of limited complexity. Incremental evolution can provide a solution. Complex tasks are divided into easier steps. This article gives an overview of incremental evolution and explains our incremental evolution experiments. We use EA as an on-board learning method to solve the novel task encountered by the robot in its environment. A robot equipped with a simulator of itself evolves a routine to overcome an obstacle in the execution of its task. We demonstrate embedded incremental evolution of a robot program for a task with computationally limited LEGO1 robots.