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arXiv 2409.11047cs.RO
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TacDiffusion:用于精确触觉操作的力域扩散策略

TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation

Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich(慕尼黑工业大学慕尼黑机器人与机器智能研究所(MIRMI));Centre for Tactile Internet with Human-in-the-Loop (CeTI)(以人为中心的触觉互联网中心(CeTI));Mohamed Bin Zayed University of Artificial Intelligence(穆罕默德·本·扎耶德人工智能大学)

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

AI总结:

本文提出TacDiffusion框架,利用扩散模型生成6D力旋以解决高精度触觉机器人插入任务,通过基于动态系统的滤波器解决频率失配问题,在单一任务演示下实现了95.7%的零样本迁移成功率。

摘要:

装配是现代制造和服务机器人领域的关键技能。然而,掌握能处理各种高精度装配任务的可迁移插入技能仍是一项重大挑战。本文提出了一种新颖的框架,利用扩散模型为高精度触觉机器人插入任务生成6D力旋(wrench)。该框架从单一任务的演示中学习,并在各种新颖的高精度任务中实现了95.7%的零样本迁移成功率。我们的方法有效继承了先前工作所展示的自适应能力。在此框架中,我们利用基于动态系统的滤波器解决了扩散策略与实时控制回路之间的频率失配问题,将任务成功率显著提高了9.15%。此外,我们还提供了关于扩散模型推理能力与速度之间权衡的实用指南。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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