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Bibliographic Details
Main Authors: Xing, Chengyi, Li, Hao, Wei, Yi-Lin, Ren, Tian-Ao, Tu, Tianyu, Lin, Yuhao, Schumann, Elizabeth, Zheng, Wei-Shi, Cutkosky, Mark R.
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2503.01789
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author Xing, Chengyi
Li, Hao
Wei, Yi-Lin
Ren, Tian-Ao
Tu, Tianyu
Lin, Yuhao
Schumann, Elizabeth
Zheng, Wei-Shi
Cutkosky, Mark R.
author_facet Xing, Chengyi
Li, Hao
Wei, Yi-Lin
Ren, Tian-Ao
Tu, Tianyu
Lin, Yuhao
Schumann, Elizabeth
Zheng, Wei-Shi
Cutkosky, Mark R.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2503_01789
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle TacCap: A Wearable FBG-Based Tactile Sensor for Seamless Human-to-Robot Skill Transfer
Xing, Chengyi
Li, Hao
Wei, Yi-Lin
Ren, Tian-Ao
Tu, Tianyu
Lin, Yuhao
Schumann, Elizabeth
Zheng, Wei-Shi
Cutkosky, Mark R.
Robotics
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.
title TacCap: A Wearable FBG-Based Tactile Sensor for Seamless Human-to-Robot Skill Transfer
topic Robotics
url https://arxiv.org/abs/2503.01789