![]() The controller ensures that a three-finger gripper tracks a desired trajectory while applying desired forces on the object for manipulation. The learning of the action generating NN is performed on-line based on a critic NN output signal. The feedforward action generating NN in the adaptive critic NN controller compensates the nonlinear gripper and contact dynamics. Since the gripper, contact dynamics, and the object properties are not typically known beforehand, an adaptive critic neural network (NN)-based hybrid position/force control scheme is introduced. Moreover, the object has to be secured accurately and considerably fast without damaging it. ![]() A sophisticated controller is necessary since the process of grasping an object without a priori knowledge of the object's size, texture, softness, gripper, and contact dynamics is rather difficult. The object manipulation subtask is defined in terms of maintaining a predefined applied force by the fingers on the object. In this paper, object contact control subtask is defined as the ability to follow a trajectory accurately by the fingers of a gripper. The complex grasping task can be defined as object contact control and manipulation subtasks. ![]() Grasping of objects has been a challenging task for robots. Adaptive critic neural network-based object grasping control using a three-finger gripper.
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