Deformable objects manipulation (DOM) is largely considered an open problem in robotics. The complexity stems from the high degrees of freedom and nonlinear nature of the object configurations. In this thesis, we consider placing and flattening tasks for cloth-like objects. We propose a practical framework to place a cloth on a surface based on visual perception and human demonstrations. We present a novel feature, Wrinkle cOntRaction Direction (WORD), which extracts a stretching direction to flatten clothes from image and depth data. Furthermore, we integrate WORD and demonstrations into Gaussian Processes to learn a cloth placing policy. Simulation and robot experiment results are used to validate the performance of WORD and the proposed learning framework in this study. The results show that WORD efficiently captures wrinkles on the contact part of the cloth in the simulation as well as the real robot experiment. Besides, the proposed learning framework performs successful results in cloth placing and flattening.A video of the experiments and execution of the tasks is available at https://youtu.be/iV2mAPqL7mA.