TacPrint：用于人机接触再现的可穿戴指尖触觉传感器
TacPrint: A Wearable Fingertip Tactile Sensor for Human-to-Robot Contact Reproduction
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中文总结 AI 辅助
本研究提出可穿戴指尖触觉传感器TacPrint，通过“现实-仿真-现实”流程实现接触深度图估计，人机接触再现实验显示其可显著提升抓取、擦拭等任务的成功率。
中文摘要 AI 辅助
以人类为中心的数据收集正成为机器人技能获取的重要范式，但无缝集成低成本、可扩展的触觉传感系统以捕获细粒度指尖交互且不影响自然操作仍是关键挑战，这降低了接触丰富任务中人机迁移的可靠性。本研究提出TacPrint，一种可穿戴指尖触觉传感器，其硅胶皮肤内表面的突起与24个电容式触觉像素一一对应，可实现局部电容响应。采用“现实-仿真-现实”流程，从24通道电容信号中估计35×26的接触深度图。针对仿真生成的标签，模型的接触区域均方根误差为0.223±0.161mm，加权质心误差为1.213±2.379像素，交并比（IoU）为0.829±0.169。在实测电容输入下，经引导校准的接触中心处，网络预测深度在全部40次受控试验中平均绝对误差为0.085±0.057mm；在37次参考接触区域未被传感边界截断的试验中，平均接触位置误差为0.250±0.208mm。在人机回放任务中，触觉引导补偿使抓取和擦拭成功率分别从0%提升至91.67%和90%；在闭环抓取中，密集深度反馈在所有测试位置的成功率为87.5%，边缘接触条件下为85%，而原始触觉像素反馈的对应成功率分别为67.5%和45%。
英文摘要
Human-centric data collection is emerging as a significant paradigm for robot skill acquisition, but seamlessly integrating low-cost, scalable tactile sensing systems that capture fine-grained fingertip interactions without compromising natural operation remains a key challenge. This reduces the reliability of human-to-robot transfer in contact-rich tasks. In this work, we present TacPrint, a wearable fingertip tactile sensor, where protrusions on the inner surface of the silicone skin are aligned one-to-one with 24 capacitive taxels to enable localized capacitive responses. A real-to-sim-to-real pipeline estimates a 35 $\times$ 26 contact-depth map from 24-channel capacitive signals. Against simulation-generated labels, the model achieved a contact-region RMSE of 0.223 $\pm$ 0.161 mm, a weighted-centroid error of 1.213 $\pm$ 2.379 pixels, and an IoU of 0.829 $\pm$ 0.169. With measured capacitive inputs, the network-predicted depth evaluated at the guide-calibrated contact center showed a mean absolute error of 0.085 $\pm$ 0.057 mm across all 40 controlled trials, while the mean contact-position error was 0.250 $\pm$ 0.208 mm across the 37 trials whose reference contact regions were not truncated by the sensing boundary. In human-to-robot replay, tactile-guided compensation increased grasping and wiping success rates from 0% to 91.67% and 90%, respectively. In closed-loop grasping, dense-depth feedback achieved success rates of 87.5% over all tested positions and 85% under edge-contact conditions, compared with 67.5% and 45% for raw-taxel feedback.
发表机构
- Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
- Imprintx Robotics（Imprintx机器人公司）
- Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）
机构由 AI 辅助整理，请以论文原文为准。
