DexGANGrasp: Dexterous Generative Adversarial Grasping Synthesis for Task-Oriented Manipulation

This is the authors' abstract. We don't add key points for this paper.

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 DexEvalautor 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 $18.57 %$ higher success rate in real-world evaluation. To further achieve task-oriented grasping, we extend DexGanGrasp to DexAfford-Prompt, an open-vocabulary affordance grounding pipeline for dexterous grasping leveraging Multimodal Large Language Models (MLLM) and Vision Language Models (VLM) with successful real-world deployments. For the code and data, visit our website.