Human-robot collaboration (HRC) in the modern industry requires the employment of manipulators that can acquire and reuse skills in a easy way and without domain-specific knowledge. Learning from Demonstration (LfD) offers a practical way to do so, however, real deployments still face some caveats; such as turning raw demonstrations into reliable low-level controllers on hardware, re-parameterizing skills to new object/goal poses. This thesis presents a unified LfD pipeline implementation for the UFACTORY xArm6, a 6DOF collaborative robot (cobo) that allows learning of reusable motor skills from human kinesthetic demonstrations. Such demonstrations can be used to plan more complex manipulation task. The pipeline follows the canonical stages -- demonstration data acquisition, motion encoding, execution, and refinement -- and is developed with three learning methods for low-level skills: Behavioral Cloning (BC), Dynamic Movement Primitives (DMP), and Gaussian Mixture Models with Gaussian Mixture Regression (GMM-GMR).