Robotic grasping represents a major competency for the safe and effective handling of objects in unstructured settings. Stability in grasps, especially when considering objects whose shape or properties are irregular or non-uniform, still presents a challenge deriving principally from uncertainties inherent to contact geometry and friction properties and mass distribution. This thesis presents learning methods for the enhancement of the robustness of grasps through explicit consideration of the influence of the center of mass (CoM) on stable manipulation. We present a new system that integrates RGB-D perception, pose generation for grasps, and reinforcement learning to forecast the CoM and control stable grasp execution.