Early on, with Professor Bagnell and Dr. Martin Zinkevich, I formalized a reduction from an inverse optimal control (IOC) form of imitation learning to maximum margin structured classification that prompted a widespread sharing of ideas between the two fields. Since then, we have developed online, batch, and functional subgradient methods to efficiently solve the optimization problems associated with both structured prediction and imitation learning problems in robotics. Collectively, our framework is known as maximum margin planning (MMP), and our specific class of linear and nonlinear gradient-based approaches to solving these problems is known as LEArning to seaRCH (LEARCH). Applications of this framework are diverse and include footstep prediction, grasp prediction, heuristic learning, overhead navigation, LADAR classification, optical character recognition, as well as parsing and other problems areas that I have not directly worked on myself. A portion of my thesis work is dedicated to generalizing these ideas to high-dimensional configuration spaces where optimal planning itself is intractable and leveraging ideas from Riemannian geometry to improve the computational efficiency of the corresponding control problems. See the pages on IOHC and CHOMP for details.