Scalable Cross-embodiment Dexterous Grasping via Morphology-Prior Diffusion

This paper presents SOMO, a scalable framework for crossembodiment grasp synthesis that transfers to novel robot hands using only their hand description (i.e., a Unified Robot Description Format (URDF) file), without requiring any hand–object interaction annotations. Unlike prior approaches that rely on hand-specific models or annotated grasp data for each embodiment, SOMO introduces a shared Morphology-Prior Diffusion model applicable across heterogeneous hands through three key designs. First, grasping is formulated as a 3D assembly problem by predicting per-link SE(3) poses, enabling geometry-driven generation independent of hand-specific kinematics, with feasibility enforced through a post joint optimization stage. Second, a classifier-free training strategy learns a morphology prior from physically valid hand configurations generated through forward-kinematic exploration without object–grasp annotations, enabling grasp synthesis for unseen robot hands given only their URDF files. Third, a 3D shape-aware VAE encodes link and object geometry into a shared embedding, enabling consistent reasoning about hand–object complementarity across embodiments. Experiments show that SOMO achieves state-of-the-art grasp synthesis across six robot hands and demonstrates strong annotation-free generalization to previously unseen hands. Code and models will be released soon.