As embodied AI research continues to advance, the need for adaptable and efficient learning algorithms for robotic tasks becomes increasingly crucial. This Master thesis investigates the performance and versatility of general-purpose models trained on large-scale datasets employed as pre-trained checkpoints and finetuned on different robotic tasks, and compares them against from-scratch robot learning approaches. This research aims to evaluate the effectiveness of pre-trained models in handling various robotic tasks, ranging from simple to complex. The study is structured as a series of experiments comparing different versions of the Octo model and of the Diffusion Policy for visuomotor trajectory learning approach. These experiments are conducted both on a simulated environment as well as on a real Panda robotic arm, and the comparative analysis is performed on identical tasks.