Robotic Grasping

Robotic grasping research aims to enable robots to reliably and efficiently grasp objects, a crucial step towards broader automation. Current efforts focus on improving grasp detection accuracy and robustness in cluttered environments, often employing deep learning models like convolutional neural networks and transformers, along with vision-language models for more nuanced object understanding and language-guided manipulation. These advancements are driving progress in various applications, from industrial automation and warehouse logistics to assistive robotics and surgery, by enhancing the dexterity and adaptability of robotic systems.