The field of robotics is moving towards more efficient and adaptive manipulation systems, inspired by biological intelligence. Recent developments have focused on neuro-inspired approaches, such as processing raw sensor data without explicit 3D point cloud reconstruction, to enable more fluid and efficient manipulation. Another key direction is the development of gripper-aware grasp detection frameworks that can handle multiple gripper configurations and generalize to unseen grippers. Additionally, researchers are exploring the use of simple, randomly assembled objects to learn generalizable grasping policies that can be applied to complex real-world objects. Noteworthy papers include: SpikeGrasp, which introduces a neuro-inspired framework for 6-DoF grasp detection that surpasses traditional point-cloud-based baselines. XGrasp, which proposes a real-time gripper-aware grasp detection framework that efficiently handles multiple gripper configurations and achieves substantial improvements in inference speed. Learning to Grasp Anything by Playing with Random Toys, which demonstrates that robots can learn generalizable grasping policies using randomly assembled objects, achieving strong zero-shot performance on real-world objects.