1. DexGANGrasp: Dexterous Generative Adversarial Grasping Synthesis for Task-Oriented Manipulation
- Author
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Feng, Qian, Lema, David S. Martinez, Malmir, Mohammadhossein, Li, Hang, Feng, Jianxiang, Chen, Zhaopeng, and Knoll, Alois
- Subjects
Computer Science - Robotics - 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., Comment: 8 pages, 4 figures
- Published
- 2024