Grasping and manipulating allows a humanoid robot to interact with it's environment and thus a central component for planning and execution of grasping motions is of great importance for the robot's application in every-day environments. Therefore, we are investigating methods to endow our humanoid robots with such indispensable capabilities. We are developing integrated apporaches for the three main tasks needed for grasp and manipulation tasks: Grasp Planning, Solving the inverse kinematics for redundant manipulators and planning of collision-free motions. In particular, we address the research topics of human-inspired grasp planning, representations of grasping actions and imitation of human grasping on humanoid robots. In addition, we are working on the integration of different grasping related methods, developed in the research community by our collaborators, to endow our humanoid robots with the capabilities of grasping different classes of known and unknown objects. Since the determination of collision-free trajectories of the robot has to be done in a fast and reliable way, taking into account a changing environment ou apporaches are based on randomized algorithms, such as Rapidly Exploring Random Trees (RRT). The methods to plan collision-free motions enable our humanoid robots to grasp obejcts with one or with both hands, to re-grasp objects and to realize imanual manipulation tasks. In addition, we are investigating integrated motion planning approaches, combining the three main task of planing a grasping motion to an online planning concept: finding a feasible grasp, solving the inverse kinematics and searching the configuration space for collision-free trajectories. Futhermore multi-robot planners are developed, allowing the simultaneous execution of cooperative grasping motions. The apporaches are evaluated in simulation and on the humanoid robot ARMAR-III. To allow an robust execution of graping and manipulation motions, Visual Servoing techniques are applied for accurate positioning of the hand.