The ability for robotic systems to detect objects in a scene and grasp them appropriately is fundamental for performing a wide range of actions. While this has the potential to significantly impact various sectors of our society, it remains challenging due to the computational requirements and the complexity of the problem at hand. While many works have provided methods to learn how to approach and grasp objects optimizing metrics like stability, robustness and velocity, little has been done to provide models with the capability to reason about how to specialize grasping strategies depending on follow-up actions, similarly to how humans change their grasping strategy of a tool depending on the purpose of the grasp (using or handing-over).