Accurate and reliable identification of the relative transfer functions (RTFs) between microphones with respect to a desired source is an essential component in the design of microphone array beamformers, specifically when applying the minimum variance distortionless response (MVDR) criterion. Since an accurate estimation of the RTF in a noisy and reverberant environment is a cumbersome task, we aim at leveraging prior knowledge of the acoustic enclosure to robustify the RTFs estimation by learning the RTF manifold. In this paper, we present a novel robust RTF identification method, tested and trained using both real recordings and simulated scenarios, which relies on learning the RTF manifold using a graph convolutional network (GCN) to infer a robust representation of the RTFs in a confined area, and consequently enhance the beamformers performance.