Show Lab sweeps 3/3 at CoRL 2026: MetaWAM hits 68.9% on RoboCasa with 39% lower inference latency
MikeShou1 · x · 2026-09-06
NUS Show Lab, after pivoting to embodied AI last year, got all 3 of its first CoRL 2026 submissions accepted, with first-year PhD/master students as authors.
- Supervise What Survives: uses video generation models to edit human demo videos into robot videos, then guides robot learning with the geometry in those videos instead of hoping to learn control directly.
- Where Success Breaks: introduces Failure-Boundary Learning, reframing robust VLA adaptation as discovering, localizing, and shaping the boundary between recoverable deviations and task failure, exploiting information in failed executions.
- MetaWAM: World Action Models like Cosmos Policy predict actions and video jointly, but images, proprioception, actions, future states, and values are represented as separate images, inflating inference cost. MetaWAM packs these modalities into a unified latent meta-frame, achieving 68.9% success on RoboCasa while cutting inference latency by 39%.
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