Providing imaging during interventional treatments of cardiovascular diseases is challenging. Magnetic Resonance Imaging (MRI) has gained popularity as it is radiation-free and returns high resolution of soft tissue. However, the clinician has limited access to the patient, e.g., to their femoral artery, within the MRI scanner to accurately guide and manipulate an MR-compatible catheter. At the same time, communication will need to be maintained with a clinician, located in a separate control room, to provide the most appropriate image to the screen inside the MRI room. Hence, there is scope to explore the feasibility of how autonomous catheterization robots could support the steering of catheters along trajectories inside complex vessel anatomies.In this paper, we present a Learning from Demonstration based Gaussian Mixture Model for a robot trajectory optimisation during pulmonary artery catheterization. The optimisation algorithm is integrated into a 2 Degree-of-Freedom MR-compatible interventional robot allowing for continuous and simultaneous translation and rotation. Our methodology achieves autonomous navigation of the catheter tip from the inferior vena cava, through the right atrium and the right ventricle into the pulmonary artery where an interventions is performed. Our results show that our MR-compatible robot can follow an advancement trajectory generated by our Learning from Demonstration algorithm. Looking at the overall duration of the intervention, it can be concluded that procedures performed by the robot (teleoperated or autonomously) required significantly less time compared to manual hand-held procedures.