Multi-Camera View Scaling for Data-Efficient Robot Imitation Learning

ArXi:2604.00557v1 Announce Type: cross The generalization ability of imitation learning policies for robotic manipulation is fundamentally constrained by the diversity of expert nstrations, while collecting nstrations 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 nstration collection.