Grasping objects in a general household setting is a dexterous task, high compliance is needed to generate a grasp that leads to grasp closure. Standard 6 Degree of Freedom (DoF) manipulators with parallel grippers are naturally incapable of showing such dexterity. This renders many objects in household settings difficult to grasp, as the manipulator cannot access readily available antipodal (planar) grasps. In such scenarios, one must either use a high DoF end effector to learn this compliance or change the initial configuration of the object to find an antipodal grasp. A pipeline that uses the extrinsic forces present in the environment to make up for this lack of compliance is proposed. The proposed method: i) Takes the point cloud input from the environment, and creates a search space with all its available poses. This search space is used to identify the best graspable position for an object with a grasp score network ii) Learn how to approach an object, and generate an appropriate set of motor primitives that converts the current ungraspable pose to a graspable pose. iii) Run a naive grasp detection network to verify the proposed methods and subsequently grasp the initially ungraspable object. By integrating these components, objects that were initially ungraspable, with a standard grasp detection model DexNet, remain no longer ungraspable.