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Underwater robot grasping object, the object and fingertip force control problems, but due to the dynamic model, grab objects location and stiffness of uncertainty, the traditional impedance control method is not robust, so the impedance control based on the location of the neural network method are studied, impedance controller is constructed based on the location of the neural network with three layers forward feedback neural network to build the compensator structure, based on the BP algorithm and the Delta learning rule, get the update rules of back propagation. The neural network control system has strong adaptability, can very good finish machine hand grasping object task. Underwater robot single finger respectively for soft material (foam) and hard materials (wood) on 5N repeated experiments, the constant power of results show that the method has a good compensation and control effect, for underwater robot accurately grasp and reasonable operation lay the foundation.