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arXiv · 2407.17348

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

Qian Feng·David S. Martinez Lema·Mohammadhossein Malmir·Hang Li·Jianxiang Feng·Zhaopeng Chen·Alois Knoll
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Abstract

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.

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Qian Feng, David S. Martinez Lema, Mohammadhossein Malmir, Hang Li, Jianxiang Feng, Zhaopeng Chen, Alois Knoll. 2024-07-24. DexGANGrasp: Dexterous Generative Adversarial Grasping Synthesis for Task-Oriented Manipulation. https://arxiv.org/abs/2407.17348

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