Abstract: This paper presents a novel trajectory optimization framework for grasping a thin object with the schunk (SDH2) hand-mounted on a Kuka robot. Unlike a conventional grasping task, we aim to achieve a “dynamic grasp” of the object, which requires continuous movement during the grasping process. The trajectory framework comprises two phases. Firstly, in a specified time limit of SI{10}{second}, initial offline trajectories are computed for a seamless motion from an initial configuration of the robot to grasp the object and deliver it to a pre-defined target location. Secondly, fast online trajectory optimization is implemented to update robot trajectories in real time within 100 milliseconds. This helps to mitigate pose estimation errors from the vision system. To account for model inaccuracies, disturbances, and other non-modeled effects, trajectory tracking controllers for both the robot and the gripper are implemented to execute the optimal trajectories from the proposed framework.