TacCap用光纤光栅传感器实现人手触觉数据无缝传给机器人
TacCap: A Wearable FBG-Based Tactile Sensor for Seamless Human-to-Robot Skill Transfer
- 采用光纤光栅技术，轻便抗干扰，适合真实场景采集触觉数据
- 实测显示传感器灵敏度高、重复性好，跨传感器一致性稳定
- 可提升机器人抓取稳定性，适合人机协作与仿生操控研究
触觉感知对灵巧操作至关重要，但大规模人类示范数据集普遍缺乏触觉反馈，限制了技能向机器人的迁移效果。为此，我们提出TacCap，一种基于光纤布喇格光栅（FBG）的可穿戴触觉传感器，旨在实现人机技能的无缝传递。TacCap重量轻、耐久性强，且不受电磁干扰，适用于真实环境下的数据采集。本文详细介绍了其设计与制作工艺，评估了其灵敏度、重复性及跨传感器一致性，并通过抓取稳定性预测与消融实验验证了其有效性。结果表明，TacCap能实现可迁移的触觉数据采集，弥合人类示范与机器人执行之间的鸿沟。为促进后续研究，我们开源了硬件设计与软件代码。
原文摘要 · Abstract (English)
Tactile sensing is essential for dexterous manipulation, yet large-scale human demonstration datasets lack tactile feedback, limiting their effectiveness in skill transfer to robots. To address this, we introduce TacCap, a wearable Fiber Bragg Grating (FBG)-based tactile sensor designed for seamless human-to-robot transfer. TacCap is lightweight, durable, and immune to electromagnetic interference, making it ideal for real-world data collection. We detail its design and fabrication, evaluate its sensitivity, repeatability, and cross-sensor consistency, and assess its effectiveness through grasp stability prediction and ablation studies. Our results demonstrate that TacCap enables transferable tactile data collection, bridging the gap between human demonstrations and robotic execution. To support further research and development, we open-source our hardware design and software.
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