The Moving Eye: Enhancing VLA Spatial Generalization via Hybrid Dynamic Data Collection
Vision-Language-Action (VLA) models have shown remarkable promise in generalized robotic manipulation. However, their spatial generalization remains fragile. We argue that simply increasing the number of viewpoints is insufficient. Models often fall into the trap of Shortcut Learning, latching onto spurious correlations (e.g., fixed relative poses between objects or between the camera and robot base) rather than learning true spatial relationships. In this work, we propose a data-centric solution to enhance VLA spatial generalization. We utilize a dual-arm setup where one arm performs manipulation while the other serves as a mobile environmental camera. We systematically evaluate three da...
| Authors | Jincheng Tang, Yilong Zhu, Zhengyuan Xie et al. |
|---|---|
| Primary category | cs.RO |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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