Cross-embodiment dexterous grasping aims to generate stable and diverse grasps for robotic hands with heterogeneous kinematic structures. Existing methods are often tailored to specific hand designs and fail to generalize to unseen hand morphologies outside the training distribution. To address these limitations, we propose \textbf{UniMorphGrasp}, a diffusion-based framework that incorporates hand morphological information into the grasp generation process for unified cross-embodiment grasp synthesis. The proposed approach maps grasps from diverse robotic hands into a unified human-like canonical hand pose representation, providing a common space for learning. Grasp generation is then conditioned on structured representations of hand kinematics, encoded as graphs derived from hand configurations, together with object geometry. In addition, a loss function is introduced that exploits the hierarchical organization of hand kinematics to guide joint-level supervision. Extensive experiments demonstrate that UniMorphGrasp achieves state-of-the-art performance on existing dexterous grasp benchmarks and exhibits strong zero-shot generalization to previously unseen hand structures, enabling scalable and practical cross-embodiment grasp deployment.

翻译：跨具身灵巧抓取旨在为具有异构运动学结构的机械手生成稳定且多样的抓取姿态。现有方法通常针对特定的手部设计定制，难以泛化到训练分布之外未见过的机械手形态。为应对这些局限性，我们提出了 **UniMorphGrasp**，一种基于扩散的框架，它将手部形态信息整合到抓取生成过程中，以实现统一的跨具身抓取合成。所提出的方法将来自不同机械手的抓取姿态映射到一个统一的类人规范手部姿态表示中，为学习提供了一个共同空间。抓取生成过程则以结构化的手部运动学表示（编码为源自手部配置的图）以及物体几何形状为条件。此外，我们引入了一个损失函数，该函数利用手部运动学的层次化组织来指导关节级别的监督。大量实验表明，UniMorphGrasp 在现有灵巧抓取基准测试中取得了最先进的性能，并对先前未见的手部结构展现出强大的零样本泛化能力，从而实现了可扩展且实用的跨具身抓取部署。