Recent increases in demand for post-stroke motor rehabilitation services together with limited time of therapist and accessibility issues, in particular for patients living in remote areas, have created a significant burden on healthcare systems worldwide. Semi-autonomous techniques that allow for sharing the time of a therapist between multiple patients have attracted great interest. Among them Learning from Demonstration (LfD) based robots have been studied as solutions to address this growing demand. In this work, a Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR) based LfD approach are proposed to generate a versatile framework to deliver rehabilitation in the absence of the therapist. To collect data for training the models, a bilateral telerehabilitation system is used to enable patient-therapist collaborative task performance is one Degree of Freedom (DOF). The performance and generalizability of the trained model are demonstrated for a variety of patient actions.