The need of robotic clothing assistance in the field of assistive robotics is growing, as it is one of the most basic activities in daily life of elderly and disabled people. In this study, we are investigating the applicability of using Dynamic Movement Primitives (DMP) as a task parameterization model for performing clothing assistance tasks. The robotic cloth manipulation task deals with putting the cloth on both the arms. The robot should do cooperative manipulation by holding the cloth. Also, there can be many failure scenarios as clothes are highly non-rigid. DMP can represent nonlinear motion with a set of differential equations. These equations can be adapted to generate any movement trajectory just by changing the goal parameter. The system consists of Baxter humanoid robot and Microsoft Kinect RGBD sensor for tracking the posture of hands. To perform the task, a demonstration is recorded by moving the Baxter arms in the appropriate trajectory. The recorded trajectory is parameterized by using DMP. Once the system is trained, new postures are accommodated by DMP. The cloth manipulation is done by Baxter humanoid robot which follows the trajectory generated by DMP. We have performed the experiments on soft mannequin instead of human. The result shows that DMPs are able to generalize the movement trajectory for the modified posture also.