TacCap: A Wearable FBG-Based Tactile Sensor for Seamless Human-to-Robot Skill Transfer

2025-03-03

AI Summary

Human demonstration data typically lacks tactile feedback, severely hindering robotic skill transfer. This paper introduces TacCap, a lightweight, electromagnetic interference–resistant, flexible wearable tactile sensor based on fiber Bragg gratings (FBGs), enabling— for the first time—high-fidelity, high-consistency tactile data acquisition in realistic scenarios. Methodologically, TacCap integrates FBG-based high-sensitivity sensing, soft structural design, and temporal tactile modeling; it further incorporates a grasp stability prediction model and an ablation validation framework. Experiments demonstrate that TacCap significantly outperforms existing solutions in sensitivity, repeatability, and cross-sensor consistency, substantially improving robots’ ability to infer human tactile intent. Both hardware designs and software implementations are fully open-sourced, facilitating standardized, reproducible research in touch-driven skill transfer.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Brain-Sensing and Analysis
• Computer Vision: Motion & Tracking

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Lack of tactile feedback in human demonstration datasets.
• Need for seamless human-to-robot skill transfer.
• Development of a wearable tactile sensor for real-world data collection.

Innovation

• Wearable FBG-based tactile sensor
• Lightweight, durable, EMI-immune design
• Open-source hardware and software