Object Grasping

Object grasping research aims to enable robots to reliably and efficiently manipulate objects, mirroring human dexterity. Current efforts focus on improving grasp planning and execution through various methods, including deep learning models for grasp detection and pose prediction from visual and tactile data, reinforcement learning for policy optimization, and the development of novel gripper designs. These advancements are crucial for applications in robotics, assistive technologies, and virtual reality, promising to enhance automation in diverse fields and improve human-robot interaction.