GEAR-VLA: Learning Geometry-Aware Action Representations for Generalizable Robotic Manipulation
Vision-Language-Action (VLA) models achieve strong benchmark performance but still struggle in real-world deployment with unseen objects, background shifts, and different robot embodiments. We argue that this stems from the lack of a unified geometry-aware manipulation representation, leaving existing VLAs vulnerable to low-level trajectory supervision, misaligned 3D features, and embodiment differences. To address this, we propose GEAR-VLA, a VLA framework for learning unified geometry-aware action representations for generalizable robotic manipulation. GEAR-VLA adopts coarse-to-fine action learning, where multi-source embodied pretraining equips the VLM with embodied reasoning and discr...
| Authors | Yuan Zhang, Shiqi Zhang, Yedong Shen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 7 June 2026 |
| Last updated at source | 10 June 2026 |
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