As the field of robotics continues to evolve, there is a growing emphasis on achieving seamless collaboration between humans and robots. This thesis delves into the intricate dynamics of human-robot interaction. It introduces a novel approach to establish a more natural and fluid paradigm, inspired by human-to-human interactions. This approach replaces the traditional reactive model with a predictive one, where the robot doesn't merely react to human stimuli but anticipates human intentions and responds accordingly. The purpose of this thesis is to harness the power of Neural Network (NN) transformers to forecast human forces. In order to train our Neural Network we collected data from brief interactions between a human and a robotic arm, specifically a Panda arm developed by Franka Emika.