[2604.00557] Multi-Camera View Scaling for Data-Efficient Robot Imitation Learning

Computer Science > Robotics arXiv:2604.00557 (cs) [Submitted on 1 Apr 2026] Title:Multi-Camera View Scaling for Data-Efficient Robot Imitation Learning Authors:Yichen Xie, Yixiao Wang, Shuqi Zhao, Cheng-En Wu, Masayoshi Tomizuka, Jianwen Xie, Hao-Shu Fang View a PDF of the paper titled Multi-Camera View Scaling for Data-Efficient Robot Imitation Learning, by Yichen Xie and 6 other authors View PDF HTML (experimental) Abstract: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...