ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning

תקציר מקורי באנגליתarXiv:2512.10946v2 Announce Type: replace-cross Abstract: Human-level contact-rich manipulation relies on the distinct roles of two key modalities: vision provides spatially rich but temporally slow global context, while force sensing captures rapid local contact dynamics. Integrating these signals is challenging due to their fundamental frequency and informational disparities. In this work, we propose ImplicitRDP, a unified end-to-end visual-force diffusion policy that integrates visual planning and reactive force control within a single network. We introduce Structural Slow-Fast Learning, a mechanism utilizing causal attention to simultaneously process asynchronous visual and force tokens, allowing the policy to perform rapid force control at the action rate while maintaining the tempora