Cross-Embodiment Dexterous Hand Articulation Generation via Morphology-Aware Learning

• URL: http://arxiv.org/abs/2510.06068v1
• Date: Tue, 07 Oct 2025 15:57:00 GMT
• Title: Cross-Embodiment Dexterous Hand Articulation Generation via Morphology-Aware Learning
• Abstract summary: Existing end-to-end methods require training on large-scale datasets for specific hands.<n>We propose an eigengrasp-based, end-to-end framework for cross-embodiment grasp generation.
• Score: 82.63833405368159
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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, our model 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 will be made available upon publication on our project website https://connor-zh.github.io/cross_embodiment_dexterous_grasping.

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