| Animals exhibit a remarkable variety of behaviors and morphologies. Evolving together over millions of years, brain and body are tightly coupled. By borrowing characteristics from nature, robotic systems can be produced that emulate the capabilities of natural organisms. In this dissertation, we use computational evolution and physics simulations to explore both control and morphology in robotic systems. Specifically we investigate joint-level control strategies and their interaction with morphological elements.;Our results demonstrate that evolutionary approaches are effective at producing controllers that are highly integrated with their morphology. Controllers are able to exploit passive properties of materials, such as flexibility, to effectively locomote in various environments. Moreover, the joint-level control strategy proposed in this dissertation, which abstracts the functionality of muscular systems, is used to study both biological principles and robotic controllers.;This dissertation explores a bio-inspired strategy that more closely resembles the cascading series of control observed in natural organisms. We demonstrate that evolved joint-level controllers can produce effective gaits in a variety of robotic systems, even when governed by a simple high-level control signal. Results support employing hierarchical control in robotic systems, and constructing control and morphology together during the design phase. In addition, we show that digital simulation can be an effective tool to study biomechanics, opening the door to further investigations of biological phenomena. |
