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English(EN) Frequency-aware decomposition learning for sensorless wrench estimation in vibration-rich robotic contact

新型AI模型增强了高频任务中机器人力的估计

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种频率感知分解网络（FDN），以改进机器人接触任务中高频力和力矩的无传感器估计。该方法将扳手力范围谱分解为低频和高频分量，并分别进行估计。与现有方法相比，FDN在真实打磨数据上将高频幅度误差降低了47%，同时保持了具有竞争力的低频精度，并在单个CPU线程上以11毫秒的延迟估计了1000毫秒的范围。 AI

影响 该AI模型有望在高速、高冲击的制造和操作任务中实现更精确、响应更快的机器人控制。

排序理由 该集群包含一篇详细介绍机器人新AI模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新型AI模型增强了高频任务中机器人力的估计

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该集群包含一篇详细介绍机器人新AI模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Topics
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hyeonbeen Lee, Min-Jae Jung, Tae-Kyeong Yeu, Jong-Boo Han, Daegil Park, Simon Stepputtis, Jin-Gyun Kim ·

    面向振动噪声的无传感器机器人接触力估计的频率感知分解学习

    arXiv:2604.12905v2 Announce Type: replace-cross Abstract: Force and torque (F/T) sensors enable contact-aware control by providing reactive feedback, but they are often fragile and expensive. To overcome these limitations, sensorless methods estimate F/T or wrench solely from rob…

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