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Mobile manipulation policies do not need joint action spaces to coordinate effectively: decoupling arm and base streams via masked joint attention and training-time world imagination drives state-of-the-art coordination across 17 distinct embodiments.
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.
