This article introduces a novel approach for learning robotic skills from human demonstrations, Elastic Fast Marching Learning (EFML) . This method seamlessly integrates concepts from Elastic Maps , a Learning from Demonstration (LfD) method based on a mesh of springs, and Fast Marching Learning (FML) , an LfD method relying on light‐based velocity fields. The combination of these methods allows a robot to generate reproductions with multiple properties, such as the ability to be trained with single or multiple demonstrations, adapt to any number of initial, final, or via‐point constraints, and generate smooth reproductions. This algorithm not only improves the efficiency of the two previous methods but also enhances capabilities beyond prior works, as the new method operates in both orientation space and task space, which neither of the original methods were able to previously. EFML exhibits advantages in terms of precision, smoothness, and speed. This approach has been validated with various comparisons in simulated environments, evaluating its performance against Elastic Maps , FML , and other contemporary LfD methods using benchmarks such as the LASA and RAIL datasets. In addition, real‐world experiments involving tasks like pouring, where both position and orientation are crucial, have been conducted to validate the approach.