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Generating a fluent motion of approaching, grasping and lifting an object comprises a number of problems which are typically tackled separately. Some existing research specializes on the optimization of the final grasp posture based on force closure criteria neglecting the motion necessary to approach this grasp. Other research specializes on motion optimization including collision avoidance criteria, but typically not considering the subsequent grasp as part of the optimization problem. In this paper we aim to combine existing techniques for grasp optimization, trajectory optimization, and attractor-based movement representation, into a comprehensive framework that allows us to efficiently compute a fluent approach and grasping motion. The feasibility of the proposed approach is shown in simulation studies and experiments with a humanoid robot.