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This dissertation explores how chaos can provide a platform for the nervous system to control locomotion and what variables influence the structure of the chaotic pattern. Here we explore these principles with computer simulations from a passive dynamic walking model and novel complementary experiments with humans. Our computer simulations provide the first insight that hip and ankle joint actuations can be used to transition to gaits embedded within the chaotic locomotive attractor. We further demonstrated that these concepts are feasible in the nervous system with an artificial neural network that championed the use of hip joint actuations to transition to stable gaits during uncertain environments and unforeseen perturbations. To explore if these principles were possible in humans, we built a mechanical hip joint actuator that tuned the motion of the swing leg. Our results were similar to our simulations and indicated that neuromuscular control of the swing leg plays a large role the chaotic structure in locomotion. We further explored if the generation of horizontal forces during the stance phase are involved in chaotic structure of locomotion. A mechanical horizontal actuator assisted or resisted the motion of the center of mass during the stance phase of the passive dynamic walking model and humans. Our simulations indicated that changes in the horizontal forces can promote the locomotive system to transition to gaits that are embedded within the chaotic attractor. We built a mechanical horizontal actuator that assisted or resisted the forward motion of the center of mass to explore these principles in humans. Our experimental results were similar to our simulations and demonstrated that horizontal forces during the stance phase were responsible for large changes in the chaotic structure. We further probed the nature of chaotic gait by exploring the mechanical constructs of the locomotive system. We altered the influence of gravity in the governing equations o