EAGG uses embodiment-specific graphs and iterative geometry injection in a shared generator to achieve 56.17% average success across six end-effectors on MultiGripperGrasp, within 1.10 pp of specialized models.
UniFucGrasp: Human- hand-inspired unified functional grasp annotation strategy and dataset for diverse dexterous hands,
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EAGG: Embodiment-Aligned Grasp Generation via Geometry-Aware Graph Conditioning
EAGG uses embodiment-specific graphs and iterative geometry injection in a shared generator to achieve 56.17% average success across six end-effectors on MultiGripperGrasp, within 1.10 pp of specialized models.