Trajectory learning from demonstrations has the ability to interactively teach robots newskills, eliminating the need for manual programming of a desired behavior. In this thesis, I have implemented Trajectory Learning from Generalized Cylinders (TLGC), a trajectory-based learning approach through demonstrations. Generalized Cylinder (GC) is a geometric representation composed of an arbitrary space curve defined as the spine, along with a varying cross section. GCs are able to extract implicit characteristics of a skill by encoding the demonstration space. They are able to reproduce multiple trajectories maintaining the characteristics of the demonstrated trajectory. This constructed model can be used to generalize a skill for unforeseen circumstances through Laplacian trajectory editing. This is achieved by adding fixed points that must be passed through, while keeping the local shape similar. This work has been validated through a real-world demonstration with a Hello Robot – Stretch RE1. The strengths and weaknesses of this approach are discussed in this thesis.