We introduce DexGanGrasp, a dexterous grasping synthesis method that generates and evaluates grasps with single view in real time. DexGanGrasp comprises a Conditional Generative Adversarial Networks (cGANs)-based DexGenerator to generate dexterous grasps and a discriminator-like DexEvalautor to assess the stability of these grasps. Extensive simulation and real-world expriments showcases the effectiveness of our proposed method, outperforming the baseline FFHNet with an 18.57% higher success rate in real-world evaluation. We further extend DexGanGrasp to DexAfford-Prompt, an open-vocabulary affordance grounding pipeline for dexterous grasping leveraging Multimodal Large Language Models (MLLMs) and Vision Language Models (VLMs), to achieve task-oriented grasping with successful real-world deployments.
@article{arxiv.2407.17348,
title = {DexGANGrasp: Dexterous Generative Adversarial Grasping Synthesis for Task-Oriented Manipulation},
author = {Qian Feng and David S. Martinez Lema and Mohammadhossein Malmir and Hang Li and Jianxiang Feng and Zhaopeng Chen and Alois Knoll},
journal= {arXiv preprint arXiv:2407.17348},
year = {2024}
}