The aim of GRASP is the design of a cognitive system capable of performing grasping and manipulation tasks in open-ended environments, dealing with novelty, uncertainty and unforeseen situations. 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. To meet these objectives, we will use theoretical, computational and experimental studies to model skilled sensorimotor behaviour based on known principles governing grasping and manipulation tasks performed by humans. As widely recognised, to design and evaluate such a complex system, we need to integrate computational techniques from machine learning, computer vision, control theory and signal processing together with experimental frameworks that include real robotic and simulation tools that allow for a long-term, experimental control over sensory inputs and tasks. Hence, the objective of GRASP is to integrate findings from disciplines such as neuroscience, cognitive science, robotics, multimodal perception and machine learning to achieve a core cognitive capability: Grasping any object by building up relations between task setting, embodied hand actions, object attributes, and contextual knowledge such that learnt grasps are extendable toward new, never seen objects in new situations.