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Universal grasping for a diverse range of objects is a challenging problem in robotics, especially in the presence of mixed properties with fragile/rigid and heavy/light. Toward universal grasping, this article presents a practical and systematic grasping control framework that enables a variable stiffness gripper to handle the objects with diverse properties using a category-aware force regulation approach, termed classification-based force grasping. Under this framework, a convolutional neural network (CNN) is employed to classify the category of the grasping object, and a grasping force is determined based on the classified category through a database that records a predefined force magnitude per category. Sequentially, the gripper can be adjusted to a force-optimized stiffness, which facilitates the achievement of an accurate grasping force regulation in a large range. Technically, two novel enabling modules are …