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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.
A novel air-ground collaboration method achieves a 77% joint success rate, showcasing the power of shared spatial context in navigation tasks.
RoboDojo reveals that integrating simulation and real-world tasks can significantly enhance the evaluation of robot manipulation policies, bridging the gap between theoretical performance and practical deployment.
