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arXiv · 2205.01048

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

Shang Liu·Xiaobao Wei·Lulu Wang·Jing Zhang·Boyu Li·Haosong Yue
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Abstract

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.

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Shang Liu, Xiaobao Wei, Lulu Wang, Jing Zhang, Boyu Li, Haosong Yue. 2022-05-02. Center-of-Mass-based Robust Grasp Pose Adaptation Using RGBD Camera and Force/Torque Sensing. https://arxiv.org/abs/2205.01048

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