Endovascular techniques have been embraced as a minimally-invasive treatment approach within different disciplines of interventional radiology and cardiology. The current practice of endovascular procedures, however, is limited by a number of factors including exposure to high doses of X-ray radiation, limited 3D imaging, and lack of contact force sensing and haptic feedback from the endovascular tools and the vascular anatomy. More recently, development of robotic platforms have aimed to improve these practices by removing the operator from the radiation source and increasing the precision and stability of catheter motion with added degrees-of-freedom. Despite their increased application and a growing research interest in this area, many such systems have been designed without considering the natural manipulation skills and ergonomic preferences of the operators. Existing studies on tool interactions and behaviour patterns of operators have been very limited, and presently there is a lack of objective and quantitative metrics for performance and skill evaluation. This research proposes a framework for automated and objective assessment of endovascular skill, by measuring catheter-tissue contact forces and operator force/motion patterns across different skill levels, relating operator tool forces to catheter dynamics and forces exerted on the vasculature, and learning the underlying force and motion patterns that are characteristic of skill. Furthermore, a novel cooperative robotic catheterization system based on 'Learning-from-Demonstration' is developed, by utilizing a learning-based approach for generating optimum motion trajectories from multiple demonstrations of a catheterization task, as well as encoding the higher-level structure of a task as a sequence of primitive motions, to enable semi-autonomous catheter navigation within a collaborative setting. The results provide important insights into improving catheter navigation in the form of assistive or semi-autonomous robotics, and motivate the design of collaborative robots that are intuitive to use, while reducing the cognitive workload of the operator.