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An intelligent controller using adaptive neuro-fuzzy inference system was developed to control the force of gripping fruits and vegetables of an agricultural robot. The inputs of the controller are the current griping force and the detail coefficients of discrete wavelet transform of the signal from slipping sensor fixed on the robotic end effector. The output of the controller is the displacement of fingers of the end effector. Firstly, a subtractive clustering was applied to generate a fuzzy model, and the radius of the clustering was adjusted to optimize the fuzzy rules. Then methods of sampling training data were introduced, and a hybrid training algorithm consisting of the gradient descent and least square algorithms was implemented to tune antecedent parameters and consequent part of the model. Finally, the experiments of controlling the griping force were carried out. It shows that the controller is able to adapt itself to differences of the fruits and vegetables in mass and surface friction characteristics. Moreover the controlling overshoot of griping force is restrained successfully and less than 0.8N, which prevented the grasping of fruits and vegetables from mechanical destruction.