Dexterous grasping with multi-fingered hands remains challenging due to high-dimensional articulations and the cost of optimization-based pipelines. Existing end-to-end methods require training on large-scale datasets for specific hands, limiting their ability to generalize across different embodiments. We propose MorphArtGrasp, an eigengrasp-based, end-to-end framework for cross-embodiment grasp generation. From a hand's morphology description, we derive a morphology embedding and an eigengrasp set. Conditioned on these, together with the object point cloud and wrist pose, an amplitude predictor regresses articulation coefficients in a low-dimensional space, which are decoded into full joint articulations. Articulation learning is supervised with a Kinematic-Aware Articulation Loss (KAL) that emphasizes fingertip-relevant motions and injects morphology-specific structure. In simulation on unseen objects across three dexterous hands, MorphArtGrasp attains a 91.9% average grasp success rate with less than 0.4 seconds inference per grasp. With few-shot adaptation to an unseen hand, it achieves 85.6% success on unseen objects in simulation, and real-world experiments on this few-shot-generalized hand achieve an 87% success rate. The code and additional materials are available on our project website https://connor-zh.github.io/MorphArtGrasp.

翻译：多指灵巧手的抓取任务因其高维关节空间以及基于优化的流程成本高昂而仍然具有挑战性。现有的端到端方法需要在特定手型的大规模数据集上进行训练，这限制了其在不同具身形态间的泛化能力。我们提出了MorphArtGrasp，一个基于本征抓取（eigengrasp）的、用于跨具身抓取生成的端到端框架。我们从手的形态描述中推导出形态嵌入和一个本征抓取集。以这些信息、物体点云以及手腕姿态为条件，一个振幅预测器在低维空间中回归关节系数，这些系数随后被解码为完整的关节运动。关节学习过程通过一种运动学感知关节损失（Kinematic-Aware Articulation Loss, KAL）进行监督，该损失强调指尖相关运动并注入形态特定的结构。在对三种灵巧手在未见物体上的仿真实验中，MorphArtGrasp实现了91.9%的平均抓取成功率，且每次抓取推理时间少于0.4秒。通过对一个未见手型进行少量样本适应，它在仿真中对未见物体达到了85.6%的成功率，并且在该少量样本泛化手型上的真实世界实验取得了87%的成功率。代码及补充材料可在我们的项目网站 https://connor-zh.github.io/MorphArtGrasp 获取。