多摄像头视角缩放用于数据高效的机器人模仿学习
Multi-Camera View Scaling for Data-Efficient Robot Imitation Learning
AI总结：
本文提出一种利用场景多样性提升机器人模仿学习数据效率的方法，通过摄像头视角缩放生成伪演示，增强视觉表示的视角不变性，并在仿真和现实任务中验证了其有效性。
摘要：
模仿学习策略在机器人操作中的泛化能力本质上受限于专家演示的多样性，而在实际中跨环境收集演示成本高且困难。本文提出一个实用框架，通过在演示收集过程中缩放摄像头视角来利用固有的场景多样性，而无需额外人力。通过多个同步摄像头视角生成每个专家轨迹的伪演示，丰富了训练分布并提高了视觉表示的视角不变性。我们分析了不同动作空间如何与视角缩放相互作用，并展示相机空间表示进一步增强了多样性。此外，我们引入了多视角动作聚合方法，使单视角策略在部署时能受益于多个摄像头。在仿真和现实任务中的广泛实验表明，与单视角基线相比，该方法在数据效率和泛化能力上取得了显著提升。我们的结果表明，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，需要最少的额外硬件设置，并能无缝集成到现有模仿学习算法中。项目网站为https://yichen928.github.io/robot_multiview。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
