ImplicitRDP: End-to-End Visual-Force Diffusion Policy for Contact-Rich Manipulation
Researchers introduce ImplicitRDP, an end-to-end diffusion policy that unifies visual planning and reactive force control for contact-rich robotic manipulation. The approach leverages Structural Slow-Fast Learning to process asynchronous visual and force signals, and Virtual-target-based Representation Regularization to improve modality integration. Experiments show that ImplicitRDP outperforms vision-only and hierarchical baselines in terms of reactivity and success rates on challenging manipulation tasks.
Why it matters: This work demonstrates a significant advance in integrating visual and force modalities for robust, reactive robotic manipulation in contact-rich environments.
Full story at: arXiv Robotics ↗