InfiNoVA 数据增强框架让机器人策略在陌生视角下成功率提升 5.4 倍
InfiNoVA: Infinite Novel View Augmentation for Viewpoint Invariant Robot Policies
机器人换个机位就翻车的问题有解了：InfiNoVA 用 3D Gaussian 造新视角训练数据，陌生视角成功率提升 5.4 倍，不用改模型架构。
InfiNoVA 是一个面向 VLA 策略的数据增强框架，把多相机演示轨迹重建为随时间变化的 3D Gaussian 表示，再从采样相机位姿渲染新视角训练数据，同时保持状态-动作对应关系。在四个真实操作任务上，用 InfiNoVA 训练的策略在未见随机视角下平均成功率比 VISTA 增强和未增强策略高 5.4 倍。它还比直接在全部五个物理相机视角上训练高出 1.7 倍，且无需改动策略架构。相比生成式新视角合成，显式场景表示降低了帧级伪影和任务相关的幻觉。
InfiNoVA: Infinite Novel View Augmentation for Viewpoint Invariant Robot Policies
Vision-Language-Action (VLA) policies often rely strongly on the camera viewpoints seen during training, causing substantial performance degradation when deployed from unseen perspectives. Collecting demonstrations from sufficiently diverse physical viewpoints is expensive and still provides only sparse coverage of the viewpoint space. We introduce InfiNoVA, a data-augmentation framework that converts synchronized multi-camera demonstrations into a dense distribution of geometrically consistent training views. InfiNoVA reconstructs each manipulation trajectory as a time-varying 3D Gaussian representation and renders novel observations from sampled camera poses while preserving the original state-action correspondence. This explicit scene representation improves frame-level fidelity and temporal consistency while reducing task-critical hallucinations observed in generative novel-view synthesis. Across four real-world manipulation tasks, policies trained with InfiNoVA achieve 5.4x higher average success under unseen randomized viewpoints than both VISTA-based augmentation and the unaugmented policy. InfiNoVA further achieves 1.7x higher success than training directly on all five physical camera views. These results show that dense, geometrically grounded viewpoint augmentation provides a practical route toward camera-robust robot policies without modifying the underlying policy architecture.
