Abstract: Compliant grippers are used in the macro and micro world for capturing and manipulating objects, when it is necessary to manipulate fragile objects, such as surgical instruments, in biomedicine for manipulating individual cells, etc. In addition, grippers with a rigid contact between the grippers and the rigid objects can lead to damage to the objects, so in these cases, a control system is required to control the contact forces. Development of compliant finger-like gripper for soft touch application is described in this paper. This paper provides an answer to the question how to design, model and implement such compliant gripper. The developed compliant fingers are inspired by the real human fingers. Machine learning methods where proposed for realization of non-distractive grasping of fragile objects where feedback information is acquired by computer vision system while intelligent algorithms where considered for motion control and grasping. The complexity of lays not only in non-destructive grasping but also in adequate force implementation in order to avoid dropping the grasped object. Hence, nonlinear simulation of compliant gripper, by means of finite elements method (FEM), is provided in this paper. Several experiment where done to provide real life conformation of such analysis.