To be useful in the real world, robots need to be able to move safely in unstructured environments and achieve their given tasks despite unexpected environmental changes or failures of some of their sensors. The variability of the world makes it impractical to develop very detailed plans of actions before execution since the world might change before execution begins and thus invalidate the plan. We propose to generate the very detailed plan of actions needed to control a robot at execution time. This allows to obtain up-to-date information about the environment through sensors and to use it in deciding how to achieve the task. Our theory is based on the premise that proper application of knowledge in the integration and utilization of sensors and actuators increases the robustness of execution. In our approach we produce the detailed plan of primitive actions and execute it by using an object-oriented approach, in which primitive components contain domain specific knowledge and knowledge about the available sensors and actuators. These primitives perform signal and control processing as well as serve as an interface to high-level planning processes. This paper addresses the issue of what knowledge needs to be available about sensors, actuators and processes in order to be able to integrate their usage, and control them during execution. The proposed methods of execution works for any sensor/actuator existing on the robot when given such knowledge.