TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation
<|reference_start|>TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation: 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.<|reference_end|>
@article{wu2024tacdiffusion:,
    title={TacDiffusion: Force-domain Diffusion Policy for Precise Tactile
  Manipulation},
    author={Yansong Wu, Zongxie Chen, Fan Wu, Lingyun Chen, Liding Zhang, Zhenshan
  Bing, Abdalla Swikir, Alois Knoll, Sami Haddadin},
    journal={arXiv preprint arXiv:2409.11047},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11047},
    primaryClass={cs.RO}
}