Vision-Language-Action (VLA) models represent a recent and promising direction in robotics, enabling agents to understand natural language instructions, perceive complex visual scenes, and perform manipulation tasks. However, these models often struggle to generalize across different robotic embodiments and environments, as changes in camera viewpoints, kinematics, or action spaces introduce significant distribution shifts. This thesis investigates the problem of robotic embodiment adaptation by evaluating the performance and adaptability of existing pre-trained VLA models on diverse robotic setups. The study focuses on fine-tuning and assessing multiple state-of-the-art VLA architectures: Diffusion Policy, OpenVLA, OpenVLA-OFT, SmolVLA, GR00T, and π0 using imitation learning. Data were collected primarily in simulation with the RLBench environment, which provides standardized tasks for the 7-DoF Franka Panda arm, and further validated on a 6-DoF real-world manipulator developed by the DIANA student team.