In this dissertation, we develop locomotion control strategies for a novel multi-legged spherical robot. The conceptual robot, named Robotic AllTerrain Surveyor (RATS), has a spherical body roughly the size of a soccer ball, with 12 legs equally distributed over its surface. The legs are pneumatic linear actuators, which are oriented such that their axes of motion are normal to the surface of the sphere. The objective of this robot is to be capable to circumvent complex obstacles and traps through rough terrain. The 12-legged spherical version of the robot is still on its design phase and no prototype has been built yet. Instead, a simpler planar prototype of the robot is available to study the control problem. The planar robot consists of a wheel with 5 legs pointing radially, that rotates freely over the main axis of a radius arm that constraints its motion into a plane. The air-cylinders run on compressed air which is supplied over a tether that goes through the radius arm.This work presents novel control strategies for the RATS planar robotic platform, which consist in the development of running and jumping gaits.The controls solutions presented were developed using two approaches: (1) conventional "hand-coded" controls, based on intuition and knowledge of the device; and (2) reinforcement learning techniques, where a policy is learned actively by maximizing the cumulative reward from a predefined reward function using value iteration. Simulators of the planar and spherical robots were implemented as tools to help the development process.This dissertation also includes an overview of the control problem for the spherical robot. Results for this section are presented only using simulation. AcknowledgementsI would like to especially thank my advisor David Wettergreen for his continuous support, including many insightful conversations during the development of the ideas in this thesis, and for helpful comments on the text. I thank Professor Dimi Apostolopoulos for agreeing to let me to participate in the project from which this work is derived in the first place, and also for his exceptional guidance during all the process. Haynes for his great insights about the control strategies.