In this letter, we experimentally validate the recent concepts of closed-loop state and input sensitivity in the context of robust manipulation control for a robot manipulator. Our objective is to assess how optimizing trajectories with respect to sensitivity metrics can enhance the closed-loop system's performance w.r.t. model uncertainties, such as those arising from payload variations during precise manipulation tasks. We conduct a series of experiments to validate our optimization approach across different trajectories, focusing primarily on evaluating the precision of the manipulator's end-effector at critical moments where high accuracy is essential. Our findings offer valuable insights into improving the closed-loop robustness of the robot's state and inputs against physical parametric uncertainties that could otherwise degrade the system's performance.