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About this paper
Byungkun Lee, Minho Park, Hojoon Lee, Dongyoon Hwang, Donghu Kim, Hyunseung Kim, Kyungmin Lee, Yunho Kim and 1 more
AFO trains a compact robot policy using vision foundation models as teachers, then deploys it from RGB images and robot state alone. The student aligns features from RGB, segmentation, and depth while learning expert actions. Across seven simulated tasks, it averages 92.57±1.51% success, versus Theia’s 87.43±2.74%; a downstream-output ViT reaches 96.57±0.81%, limiting claims that AFO leads every variant.
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