EAGG: Embodiment-Aligned Grasp Generation via Geometry-Aware Graph Conditioning
Cross-end-effector grasp generation seeks a unified model that generalizes across objects and across embodiments ranging from parallel grippers to dexterous end effectors. Existing grasp generators are typically designed for a fixed embodiment or encode embodiment identity with a static descriptor, which weakens transfer when topology, actuation coupling, and contact geometry differ substantially. We present EAGG, an embodiment-aligned grasp generator that represents each embodiment with a topology-aware end-effector graph and an embodiment-specific low-dimensional end-effector control space. A frozen end-effector-cognition backbone converts the current articulated state into geometry-awa...
| Authors | Wanhao Niu, Qiyan Ke, Yuan Sun et al. |
|---|---|
| Primary category | cs.RO |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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