DexGrasp-Diffusion：基于扩散的多灵巧机械手统一功能性抓取合成方法
DexGrasp-Diffusion: Diffusion-based Unified Functional Grasp Synthesis Method for Multi-Dexterous Robotic Hands
- Advanced Robotics Centre, National University of Singapore（新加坡国立大学先进机器人中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出DexGrasp-Diffusion，一种端到端模块化扩散方法，结合统一多灵巧手抓取扩散模型与物理及功能判别器，为多灵巧手合成符合物体affordance指令的功能性抓取，并在MultiDex上验证其成功率、多样性与可靠性。
AI中文摘要：
人类抓取的多样性与适应性推动了灵巧机器人操作的发展。尽管在灵巧抓取生成方面已取得显著进展，当前研究正转向在保证功能完整性的同时优化物体操作，强调按照期望的 affordance 指令合成功能性抓取。本文针对为多种灵巧机械手合成功能性抓取的挑战，提出 DexGrasp-Diffusion，一种端到端的模块化扩散方法。DexGrasp-Diffusion 将 MultiHandDiffuser（一种新颖的统一数据驱动扩散模型，用于多灵巧手抓取估计）与 DexDiscriminator 相结合，后者采用物理判别器和具有开放词汇设置的功能判别器，基于物体 affordance 过滤物理上合理的功能性抓取。在 MultiDex 数据集上进行的实验评估提供了有力证据，表明 MultiHandDiffuser 在成功率、抓取多样性和碰撞深度方面优于基线模型。此外，我们展示了 DexGrasp-Diffusion 能够可靠地为家用物体生成符合特定 affordance 指令的功能性抓取。
英文摘要：
The versatility and adaptability of human grasping catalyze advancing dexterous robotic manipulation. While significant strides have been made in dexterous grasp generation, current research endeavors pivot towards optimizing object manipulation while ensuring functional integrity, emphasizing the synthesis of functional grasps following desired affordance instructions. This paper addresses the challenge of synthesizing functional grasps tailored to diverse dexterous robotic hands by proposing DexGrasp-Diffusion, an end-to-end modularized diffusion-based method. DexGrasp-Diffusion integrates MultiHandDiffuser, a novel unified data-driven diffusion model for multi-dexterous hands grasp estimation, with DexDiscriminator, which employs a Physics Discriminator and a Functional Discriminator with open-vocabulary setting to filter physically plausible functional grasps based on object affordances. The experimental evaluation conducted on the MultiDex dataset provides substantiating evidence supporting the superior performance of MultiHandDiffuser over the baseline model in terms of success rate, grasp diversity, and collision depth. Moreover, we demonstrate the capacity of DexGrasp-Diffusion to reliably generate functional grasps for household objects aligned with specific affordance instructions.
