Summary: The development of autonomous robotic agents capable of complex navigation, control and planning is emerging as an essential area of research in many fields. The benefits associated with the successful implementation of such systems are enormous. However, the creation of robotic controllers for the efficient manipulation of autonomous agents in real-time is a very computationally complex task. Such complexity increases exponentially as the structure of the robot or its surrounding environment increase in sophistication. We present a genetic approach for the representation of articulated robotic structures as well as the evolution of the behavioral capabilities of robotic agents. Evolutionary guidance constructs are also presented as means for minimizing the search space associated with the control problem and achieving successful evolution of agents in a shorter time duration.