arXiv:2506.09994cs.ROcs.AI2025-06被引 17

用可定制磁性触觉传感器让机器人精准感知抓握力与滑动。

eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructures

  • 基于磁性微结构拼贴,可3D打印自定义形状和灵敏度。
  • 接触定位误差0.5毫米,法向力预测误差0.27牛,剪切力0.12牛。
  • 支持滑动检测与视觉触觉协同控制,适合工业与服务机器人。

若以人类经验为参考,机器人在家庭、办公室等非结构化环境中有效操作,需感知物理交互中的作用力。然而,缺乏通用、易得且可定制的触觉传感器,导致机器人操作中存在碎片化解决方案,甚至采用无传感的力觉盲方法。eFlesh通过引入低成本、易制造、高度可定制的磁性触觉传感器,填补这一空白。构建eFlesh传感器仅需四部分:爱好者级3D打印机、市售磁铁(<5美元)、目标形状的CAD模型及磁力计电路板。传感器由参数化微结构阵列构成,可调节几何形态与力学响应。我们提供开源设计工具,将凸形OBJ/STL文件转换为可3D打印的STL文件。该模块化框架支持用户创建任务专用传感器,并根据需求调整灵敏度。实验表明:接触定位均方根误差(RMSE)为0.5 mm,法向力预测RMSE为0.27 N,剪切力为0.12 N。我们还提出一种可泛化的学习型滑动检测模型,对未见物体识别准确率达95%;结合视觉的触觉控制策略使操作性能较纯视觉基线提升40%,在四项需亚毫米精度的任务中平均成功率达91%。所有设计文件、代码及从CAD到eFlesh STL的转换工具均已开源,详见https://e-flesh.com。

原文摘要 · Abstract (English)

If human experience is any guide, operating effectively in unstructured environments -- like homes and offices -- requires robots to sense the forces during physical interaction. Yet, the lack of a versatile, accessible, and easily customizable tactile sensor has led to fragmented, sensor-specific solutions in robotic manipulation -- and in many cases, to force-unaware, sensorless approaches. With eFlesh, we bridge this gap by introducing a magnetic tactile sensor that is low-cost, easy to fabricate, and highly customizable. Building an eFlesh sensor requires only four components: a hobbyist 3D printer, off-the-shelf magnets (<$5), a CAD model of the desired shape, and a magnetometer circuit board. The sensor is constructed from tiled, parameterized microstructures, which allow for tuning the sensor's geometry and its mechanical response. We provide an open-source design tool that converts convex OBJ/STL files into 3D-printable STLs for fabrication. This modular design framework enables users to create application-specific sensors, and to adjust sensitivity depending on the task. Our sensor characterization experiments demonstrate the capabilities of eFlesh: contact localization RMSE of 0.5 mm, and force prediction RMSE of 0.27 N for normal force and 0.12 N for shear force. We also present a learned slip detection model that generalizes to unseen objects with 95% accuracy, and visuotactile control policies that improve manipulation performance by 40% over vision-only baselines -- achieving 91% average success rate for four precise tasks that require sub-mm accuracy for successful completion. All design files, code and the CAD-to-eFlesh STL conversion tool are open-sourced and available on https://e-flesh.com.

触觉传感磁性传感器机器人操控3D打印

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