Latent Interface Training breaks vision-action shortcuts, lifts robot success by up to 16.7 points
DJiafei · x · 2026-09-14
- Latent Interface Training (LIT) targets the vision-action shortcut in robot foundation models, where models leap from raw visual features directly to actions, bypassing structured intermediates like pose.
- LIT routes vision through a pose-supervised latent interface, forcing more structured representations.
- Results: up to 10.7 pp success-rate gains on LIBERO-Plus across four architectures, and 13–16.7 pp gains on real robots, showing the approach transfers to hardware.
Related event: NUS Proposes LIT to Break Robot Vision-Action Shortcuts(2 posts)→
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