Assembly is a crucial skill for robots in both modern manufacturing and service robotics. However, mastering transferable insertion skills that can handle a variety of high-precision assembly tasks remains a significant challenge. This paper presents a novel framework that utilizes diffusion models to generate 6D wrench for high-precision tactile robotic insertion tasks. It learns from demonstrations performed on a single task and achieves a zero-shot transfer success rate of 95.7% across various novel high-precision tasks. Our method effectively inherits the self-adaptability demonstrated by our previous work. In this framework, we address the frequency misalignment between the diffusion policy and the real-time control loop with a dynamic system-based filter, significantly improving the task success rate by 9.15%. Furthermore, we provide a practical guideline regarding the trade-off between diffusion models' inference ability and speed.
@article{arxiv.2409.11047,
title = {TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation},
author = {Yansong Wu and Zongxie Chen and Fan Wu and Lingyun Chen and Liding Zhang and Zhenshan Bing and Abdalla Swikir and Sami Haddadin and Alois Knoll},
journal= {arXiv preprint arXiv:2409.11047},
year = {2025}
}