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Object dropping may occur when the robotic arm grasps objects with unevenmass distribution due to additional moments generated by objects' gravity. Tosolve this problem, we present a novel work that does not require extra wristand tactile sensors and large amounts of experiments for learning. First, weobtain the center-of-mass position of the rod object using the widely fixedjoint torque sensors on the robot arm and RGBD camera. Further, we give thestrategy of grasping to improve grasp stability. Simulation experiments areperformed in "Mujoco". Results demonstrate that our work is effective inenhancing grasping robustness.

Center-of-Mass-based Robust Grasp Pose Adaptation Using RGBD Camera and Force/Torque Sensing