Compared to humans or primates, the ability of contemporary robotic hands is suprisingly limited, they can grasp only few objects in constricted poses with limited grasping postures and position. The aim of GRASP is the design of a cognitive system capable of performing tasks in open-ended environments, dealing with uncertainty and novel situations. We have decided to study the problem of object manipulation and grasping, by providing theoretical and measurable basis for system design that are valid in both human and artificial systems. We believe that this is of utmost importance for the design of artificial cognitive systems that are to be deployed in real environments and interact with humans and other artificial agents. Such systems need the ability to exploit the innate knowledge and self-understanding to gradually develop cognitive capabilities. To demonstrate the feasibility of our approach, we will instantiate, implement and evaluate our theories on robot systems with different emobodiments and levels of complexity. These systems will operate in real-world scenarios, with and without human intervention and tutoring.GRASP will develop means for robotic systems to reason about graspable targets, to explore and investigate their physical properties and finally to make artificial hands grasp any object. We will use theoretical, computational and experimental studies to model skilled sensorimotor behavior based on known principles governing grasping and manipulation tasks performed by humans. Therefore, GRASP sets out to integrate a large body of findings from disciplines such as neuroscience, cognitive science, robotics, multi-modal perception and machine learning to achieve a core capability: Grasping any object by building up relations between task setting, embodied hand actions, object attributes, and contextual knowledge.