  Object dropping may occur when the robotic arm grasps objects with uneven
mass distribution due to additional moments generated by objects' gravity. To
solve this problem, we present a novel work that does not require extra wrist
and tactile sensors and large amounts of experiments for learning. First, we
obtain the center-of-mass position of the rod object using the widely fixed
joint torque sensors on the robot arm and RGBD camera. Further, we give the
strategy of grasping to improve grasp stability. Simulation experiments are
performed in "Mujoco". Results demonstrate that our work is effective in
enhancing grasping robustness.
