Multi-Camera View Scaling for Data-Efficient Robot Imitation Learning
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...
| Authors | Yichen Xie, Yixiao Wang, Shuqi Zhao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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