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arXiv · 2409.11047

TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation

Yansong Wu·Zongxie Chen·Fan Wu·Lingyun Chen·Liding Zhang·Zhenshan Bing·Abdalla Swikir·Sami Haddadin·Alois Knoll
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Abstract

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.

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Yansong Wu, Zongxie Chen, Fan Wu, Lingyun Chen, Liding Zhang, Zhenshan Bing, Abdalla Swikir, Sami Haddadin, Alois Knoll. 2024-09-17. TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation. https://arxiv.org/abs/2409.11047

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