arXiv:2510.05382cs.RO2025-10被引 4

低成本触觉指尖可精准感知力与材质,提升机械手操作能力。

A multi-modal tactile fingertip design for robotic hands to enhance dexterous manipulation

  • 集成应变片与接触麦克风,同时捕捉压力和高频振动。
  • 在0-5N范围内稳定测力,能区分不同材料的接触特性。
  • 适用于无视觉依赖的精细操作,适合机器人灵巧抓取场景。

触觉感知有望显著提升机器人操作的精度与多样性,但因传感器成本高、制造集成难及信号信息表达性不足,实际应用受限。本文提出一种低成本、易制作、可适配且紧凑的多模态触觉指尖设计,集成应变片传感器用于测量静态力,接触麦克风用于捕获接触时的高频振动。所有传感器均内置指尖内部,避免直接磨损。实验表明,应变片可在0-5 N范围内重复测量二维平面力,接触麦克风具备区分接触材料属性的能力。该设计应用于三个从完全无视觉到全视觉遮挡的操作任务中,证明不同触觉模态可灵活配合或独立使用,结合视觉信息实现更优性能。例如,在仅靠触觉下100%成功率完成纸杯逐个取出与堆叠任务,这是纯视觉难以实现的。

原文摘要 · Abstract (English)

Tactile sensing holds great promise for enhancing manipulation precision and versatility, but its adoption in robotic hands remains limited due to high sensor costs, manufacturing and integration challenges, and difficulties in extracting expressive and reliable information from signals. In this work, we present a low-cost, easy-to-make, adaptable, and compact fingertip design for robotic hands that integrates multi-modal tactile sensors. We use strain gauge sensors to capture static forces and a contact microphone sensor to measure high-frequency vibrations during contact. These tactile sensors are integrated into a compact design with a minimal sensor footprint, and all sensors are internal to the fingertip and therefore not susceptible to direct wear and tear from interactions. From sensor characterization, we show that strain gauge sensors provide repeatable 2D planar force measurements in the 0-5 N range and the contact microphone sensor has the capability to distinguish contact material properties. We apply our design to three dexterous manipulation tasks that range from zero to full visual occlusion. Given the expressiveness and reliability of tactile sensor readings, we show that different tactile sensing modalities can be used flexibly in different stages of manipulation, solely or together with visual observations to achieve improved task performance. For instance, we can precisely count and unstack a desired number of paper cups from a stack with 100\% success rate which is hard to achieve with vision only.

触觉感知机器人抓取多模态传感

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