The Moving Eye: Enhancing VLA Spatial Generalization via Hybrid Dynamic Data Collection移动视角：通过混合动态数据采集增强VLA空间泛化能力
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Researchers propose a dual-arm robotic setup with a moving camera arm to improve Vision-Language-Action (VLA) model spatial generalization through hybrid data collection combining continuous and static viewpoints.
The approach addresses Shortcut Learning, where models learn spurious correlations in fixed object poses or camera positions rather than true spatial relationships; three data patterns (Fixed, Multi-Fixed, Moving Views) were systematically evaluated.
The hybrid strategy combining continuous motion with diverse static views achieves best performance by reducing spurious correlations while maintaining training stability, enabling VLAs to better generalize to unseen environments.