This thesis proposes a Learning from Demonstration (LfD) approach designed to generalize and extract relevant features of desired motion trajectories for robotic manipulation tasks, with the specific objective of learning a sliding and picking task exploiting environmental constraints and force sensor data. Learning from Demonstration is a powerful approach in robotics, since robots can acquire new skills by observing, modeling and imitating human demonstrations of a task. This method leverages human expertise to teach robots complex movements, reducing the need for explicit programming. Research in the field of Lfd is facilitating, even for non-expert users, to teach new tasks to robots with few demonstrations, enabling robotics and skills learning to be used in a variety of fields of applications and dynamic environments.