From the 1 of 4 linked papers with an AI index.
4 papers
FA-RDP: A Frequency-Adaptive Reactive Diffusion Policy for Contact-Rich Manipulation
Lifeng Zhuo, Wendi Chen, Han Xue +4
The paper introduces FA-RDP, a diffusion‑based policy that adapts its inference frequency during contact‑rich manipulation, using a multi‑frequency visual‑force transformer and a m…
Asynchronous Multimodal Diffusion Policy Composition via Latency-Aware Guidance Fusion
Zihao He, Hongjie Fang, Shirun Tang +2
Diffusion policies have shown strong potential for robotic imitation learning, and recent extensions incorporate additional modalities to improve manipulation performance. However,…
Force Policy: Learning Hybrid Force-Position Control Policy under Interaction Frame for Contact-Rich Manipulation
Hongjie Fang, Shirun Tang, Mingyu Mei +9
Contact-rich manipulation demands human-like integration of perception and force feedback: vision should guide task progress, while high-frequency interaction control must stabiliz…
ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning
Wendi Chen, Han Xue, Yi Wang +6
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 cap…