DexGANGrasp: Dexterous Generative Adversarial Grasping Synthesis for Task-Oriented Manipulation
<|reference_start|>DexGANGrasp: Dexterous Generative Adversarial Grasping Synthesis for Task-Oriented Manipulation: 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.<|reference_end|>
@article{feng2024dexgangrasp:,
    title={DexGANGrasp: Dexterous Generative Adversarial Grasping Synthesis for
  Task-Oriented Manipulation},
    author={Qian Feng, David S. Martinez Lema, Mohammadhossein Malmir, Hang Li,
  Jianxiang Feng, Zhaopeng Chen, Alois Knoll},
    journal={arXiv preprint arXiv:2407.17348},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17348},
    primaryClass={cs.RO}
}