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1,000 hours of human touch data unlocks a 37% jump in vision-only dexterous manipulation success, revealing touch as a powerful pretraining signal beyond its direct use in policies.
This work introduces DexTouch-WM, an action-conditioned world model that learns from scalable human touch to jointly predict future RGB observations and bilateral tactile dynamics, and shows that scalable human interaction provides a complementary data axis for learning dexterous robot world models.
Integration of diverse robot policies can be streamlined from hours to minutes, revolutionizing how we deploy and evaluate robotic systems.