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A single 500-hour visual-tactile dataset boosts zero-shot contact prediction from 13% to 38% IoU and quadruples robot manipulation success rates, proving scale and consistency are paramount for embodied learning.
Conditioning video world models on continuous tactile dynamics cuts hand-motion underestimation from 23% to 9%—and surprisingly boosts visual prediction accuracy even when touch sensors are removed at inference.
Today's video world models excel at hallucinating plausible-looking frames, but their ability to actually preserve initial object identities drops by over 40% as scene complexity scales beyond 40 instances.
Generating missing multi-view data from diverse driving videos boosts closed-loop driving robustness in edge cases by over 30%.