The generalization ability of imitation learning policies for robotic manipulation is fundamentally constrained by the diversity of expert demonstrations, while collecting demonstrations across varied environments is costly and difficult in practice. In this paper, we propose a practical framework that exploits inherent scene diversity without additional human effort by scaling camera views during demonstration collection. Instead of acquiring more trajectories, multiple synchronized camera perspectives are used to generate pseudo-demonstrations from each expert trajectory, which enriches the training distribution and improves viewpoint invariance in visual representations. We analyze how different action spaces interact with view scaling and show that camera-space representations further enhance diversity. In addition, we introduce a multiview action aggregation method that allows single-view policies to benefit from multiple cameras during deployment. Extensive experiments in simulation and real-world manipulation tasks demonstrate significant gains in data efficiency and generalization compared to single-view baselines. Our results suggest that scaling camera views provides a practical and scalable solution for imitation learning, which requires minimal additional hardware setup and integrates seamlessly with existing imitation learning algorithms. The website of our project is https://yichen928.github.io/robot_multiview.

翻译：机器人操作中模仿学习策略的泛化能力本质上受限于专家演示的多样性，然而在实际中跨不同环境收集演示成本高昂且实施困难。本文提出一种无需额外人力即可利用场景内在多样性的实用框架——通过在演示采集过程中缩放相机视角。该方法无需获取更多轨迹，而是利用多个同步摄像机视角从每条专家轨迹生成伪演示，从而丰富训练分布并提升视觉表征中的视角不变性。我们分析了不同动作空间与视角缩放的交互机制，证明相机空间表征能进一步增强多样性。此外，我们引入了一种多视角动作聚合方法，使单视角策略在部署阶段也能从多个相机中获益。在仿真和真实世界操作任务中的大量实验表明，与单视角基线相比，该方法在数据效率和泛化能力方面取得了显著提升。研究结果表明，相机视角缩放为模仿学习提供了一种实用且可扩展的解决方案，该方案所需额外硬件配置极少，并能与现有模仿学习算法无缝集成。项目网站地址为：https://yichen928.github.io/robot_multiview