Rethinking High-Fidelity View Synthesis: Simple Homography Augmentation for Viewpoint-Robust VLAs

Vision-Language-Action (VLA) models perform strongly across diverse language-conditioned manipulation tasks but remain sensitive to camera viewpoint changes. Recent 3D-aware augmentation methods address this challenge by generating observations from novel viewpoints through learned view synthesis or 3D reconstruction. However, generating these views is computationally expensive and requires additional pretrained models. To address these limitations, we propose SHARP (Simple Homography Augmentation for Robust Policies), a lightweight view-augmentation method that improves VLA robustness to unseen third-person camera viewpoints. For VLAs that combine third-person and wrist-camera observations, SHARP applies randomly sampled homographies to third-person images during policy finetuning while preserving the paired wrist observations and action labels. These inexpensive 2D transformations enable online augmentation of demonstrations from a single third-person viewpoint, without additional pretrained synthesis or reconstruction models. Compared with policies finetuned on the same data without augmentation, SHARP improves average success rates across all evaluated viewpoints by 11.8 percentage points on RLBench and 16.4 percentage points on LIBERO. It achieves success rates competitive with the evaluated view-synthesis methods at substantially lower augmentation cost. Comparisons with alternative 2D augmentations highlight the benefit of geometric augmentation for viewpoint robustness. Attention analyses show a shift toward wrist observations, while camera-input analyses suggest that SHARP still benefits from third-person information under certain viewpoint shifts. These results suggest that lightweight geometric augmentation can improve viewpoint robustness in VLAs with stable wrist-camera inputs, even without faithfully reconstructing novel views.