-- We introduce DexGanGrasp, a dexterous grasp synthesis method that generates and evaluates grasps with a single view in real-time. DexGanGrasp comprises a Conditional Generative Adversarial Network (cGAN)-based DexGenerator to generate dexterous grasps and a discriminator-like DexE-valautor to assess the stability of these grasps. Extensive simulation and real-world experiments showcase the effectiveness of our proposed method, outperforming the baseline FFHNet with an 18 . T o further achieve task-oriented grasping, we extend Dex-GanGrasp to DexAfford-Prompt, an open-vocabulary affordance grounding pipeline for dexterous grasping leveraging Multi-modal Large Language Models (MLLM) and Vision Language Models (VLM) with successful real-world deployments. As a crucial prerequisite for manipulation, robotic grasping is a fundamental skill for robots to start interacting with our environment. Low-dimensional end effectors like 2-jaw grippers or suction cups have been extensively studied with significant progress [1]-[4]. However, 2-jaw grippers suffer from low dexterity, which limits their applicability for task-oriented manipulations [5], [6]. Dexterous hands allow for a broader range of solutions to grasp an object.