This paper proposes the method that the humanoid with the movable binocular eye-camera unit can acquire the ability to recognize distance to an object by itself. Movable eye-camera has advantages such as quick object tracking or wide field of view. However, it’s difficult to get the distance to an object because of the errors of intrinsic and extrinsic camera parameters. We solve this problem by using the neural network that receives the convergence angle and the object position in binocular images as input and outputs the object position in the real world. In the training phase, the humanoid moves its arm randomly and recognizes hand position in binocular images. At the same time, it collects hand position in the real world calculated by forward kinematics as teaching data. We confirmed the validity of the method by using musculoskeletal humanoid "Musashi" and its movable binocular eye-camera unit.