Robot Grasp

Robot grasping research aims to enable robots to reliably and efficiently grasp objects, a crucial step for various robotic manipulation tasks. Current efforts focus on improving grasp detection and planning in challenging scenarios, such as grasping transparent or partially occluded objects, using techniques like deep convolutional neural networks, differentiable rendering, and Gaussian process-based methods for shape modeling and grasp stability assessment. These advancements leverage both visual and tactile information, often incorporating 3D object detection and pose estimation to enhance accuracy and robustness, ultimately contributing to more versatile and adaptable robotic systems for diverse applications.