Cable Manipulation

Cable manipulation research focuses on enabling robots to effectively grasp, move, and interact with cables, addressing challenges posed by their flexibility and high degrees of freedom. Current efforts concentrate on developing robust control algorithms, often employing deep learning models like Temporal Convolutional Networks and Graph Neural Networks, to compensate for hysteresis and accurately predict cable deformation. These advancements are crucial for improving automation in various fields, including minimally invasive surgery, warehouse automation, and aerial manipulation tasks, by enhancing precision and reliability in cable-based operations. The integration of tactile sensing with vision-based systems further enhances dexterity and control in complex cable routing and assembly scenarios.