The field of robotics is witnessing significant advancements in dexterous grasping, with a focus on developing innovative gripper designs and control systems. Researchers are exploring new mechanisms and algorithms to enable robots to grasp and manipulate objects with unprecedented precision and versatility. One of the key trends in this area is the development of under-actuated grippers that can adapt to various object shapes and sizes. Another important direction is the use of simulation-to-reality transfer pipelines to enable zero-shot deployment of grasping policies in complex, cluttered scenes. These advances have the potential to revolutionize various applications, including manufacturing, logistics, and healthcare. Noteworthy papers in this area include: ClutterDexGrasp, which proposes a two-stage teacher-student framework for closed-loop target-oriented dexterous grasping in cluttered scenes. GRIM, which introduces a novel training-free framework for task-oriented grasping that demonstrates strong generalization capabilities with only a small number of conditioning examples.