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arXiv · 2607.11031

GraspGraphNet: Graph-Structured Multi-Embodiment Dexterous Grasp Generation

Yeonseo Lee·Taeyeop Lee·Hyosup Shin·Guebin Hwang·Sungho Jo
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Abstract

Dexterous grasp generation across robot hands is challenging because hands differ in kinematic topology, actuation dimensions, and native command spaces. We introduce GraspGraphNet, a topology-aware grasp generation framework that represents each hand as a URDF-derived kinematic graph and directly generates executable palm poses and joint configurations. GraspGraphNet combines hierarchical object surface encoding, differentiable forward kinematics, and dynamic world-edge message passing to model evolving robot-object interactions. It applies conditional flow matching directly in executable palm-pose and joint-state space, avoiding post-processing optimization, inverse kinematics, and retargeting. Using a shared model trained on Barrett Hand, Allegro Hand, and Shadow Hand, GraspGraphNet achieves an average success rate of 83.48% with 40ms inference time per grasp on a 40-object benchmark. Without retraining, the same model achieves 72.70% success on controlled finger-removal variants, demonstrating robustness to hand-topology variations. These results suggest that graph-structured hand representations can effectively support dexterous grasp generation across robot hands with different kinematic structures. Project: https://lysees.github.io/graspgraphnet-page

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Yeonseo Lee, Taeyeop Lee, Hyosup Shin, Guebin Hwang, Sungho Jo. 2026-07-13. GraspGraphNet: Graph-Structured Multi-Embodiment Dexterous Grasp Generation. https://arxiv.org/abs/2607.11031

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