Stable Grasp

Stable grasping in robotics aims to enable robots to securely and reliably hold objects, a crucial skill for various manipulation tasks. Current research focuses on developing robust grasp planning algorithms, often employing deep learning models like convolutional neural networks and transformers, and integrating tactile sensing for improved grasp stability and adaptability to diverse object shapes and robot hand designs. These advancements are significant for improving robotic manipulation in areas such as manufacturing, logistics, and human-robot collaboration, where reliable grasping is essential for efficient and safe operation.