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

This article introduces ImplicitRDP, a novel approach using diffusion models for visual-force control. The 'slow-fast learning' aspect suggests an attempt to improve efficiency and performance by separating different learning rates or processing speeds for different aspects of the task. The end-to-end nature implies a focus on a complete system, likely aiming for direct input-to-output control without intermediate steps. The use of 'structural' suggests an emphasis on the underlying architecture and how it's designed to handle the visual and force data.