Key Takeaways & Executive Findings
- •• • The sensor array comprises 18 sensing units distributed across the robotic hand, enabling whole-hand tactile coverage beyond fingertip-only perception, which is critical for complex manipulation tasks such as pulling and grasping. • • The crosslinked interpenetrating network ensures high interlayer tensile strength, preventing delamination under mechanical deformation, a key failure mode in multilayer flexible sensors; this directly addresses the interlayer mechanical mismatch problem. • • The sensor array demonstrates high sensitivity and low hysteresis, with excellent long-term reliability over 10,000 loading cycles, indicating robust performance for repeated robotic operations. • • Object recognition accuracy reaches 90.1% using convolutional neural network algorithms, validated in real-time on a digital twin interface, showcasing the sensor's integration with AI for adaptive grasping and teleoperation.
Abstract
Tactile sensing for dexterous robotic hands is essential for achieving human-like precision in manipulation. However, current tactile sensors face challenges such as insufficient durability, limited coverage, and poor conformability to curved, jointed surfaces. This study presents a stretchable distributed tactile sensor array designed for dexterous robotic hands. The array comprises 18 sensing units distributed across the hand, incorporating quasi-homogeneous functional layers interconnected by crosslinked interpenetrating networks, and composite electrodes combining high conductivity with stretchability. This design yields a thin, soft, transparent, and stretchable sensor array that integrates seamlessly with a commercial dexterous hand. The sensor array exhibits high interlayer tensile strength, high sensitivity, low hysteresis, and excellent long-term reliability over 10,000 loading cycles. Experimental results demonstrate accurate detection of tactile force across the entire robotic hand during object grasping. Using convolutional neural network algorithms, the sensor array identifies different object types with 90.1% accuracy, with results displayed in real time on a digital twin interface. The proposed sensor array holds significant potential for embodied intelligence and robotics in adaptive grasping, safe manipulation, and remote teleoperation.
1. Introduction
Dexterous robotic hands require comprehensive tactile sensing to achieve human-like manipulation. However, current tactile sensors predominantly focus on fingertips, limiting operations to simple actions like touching and pinching. Complex tasks such as pulling and grasping demand distributed sensing across the entire hand. Moreover, multilayer sensor designs suffer from interlayer mechanical mismatch due to disparate Young's moduli, leading to delamination and failure under significant deformations. These bottlenecks restrict the durability and conformability of tactile sensors for real-world robotic applications.
This study introduces a stretchable distributed tactile sensor array that overcomes these limitations by employing a crosslinked interpenetrating network to bond quasi-homogeneous functional layers, ensuring high interlayer tensile strength and mechanical robustness. The array integrates 18 sensing units across a commercial dexterous hand, providing comprehensive tactile coverage. With high sensitivity, low hysteresis, and reliability over 10,000 cycles, the sensor array enables accurate force detection and object recognition via convolutional neural networks, achieving 90.1% accuracy. This work addresses the critical need for durable, conformable, and distributed tactile sensing in advanced robotic systems.
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LI Dongsheng, HUO Wenjun, SUN Yuyang, YU Lei, JI Tianci, LI Aomen, JIANG Feng, LIU Yan, ZHANG Wufeng, LIU Huicong (2026). Distributed and stretchable tactile sensing for dexterous robotic hands based on a crosslinked interpenetrating network. SCIENCE CHINA Materials. https://doi.org/10.1007/s40843-025-3840-3
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Frequently Asked Questions
What is the interlayer tensile strength of the sensor array, and how does it prevent delamination under repeated mechanical stress?
The sensor array exhibits high interlayer tensile strength due to the crosslinked interpenetrating network, which bonds the quasi-homogeneous functional layers. This design mitigates interlayer mechanical mismatch and prevents separation during robotic hand operations, ensuring long-term reliability over 10,000 loading cycles.
How does the sensor array achieve conformability to the curved and jointed surfaces of a dexterous hand without compromising sensitivity?
The sensor array is thin, soft, transparent, and stretchable, allowing seamless integration with commercial dexterous hands. The quasi-homogeneous functional layers and composite electrodes maintain high conductivity and stretchability, enabling conformal contact while preserving high sensitivity and low hysteresis.
What is the spatial resolution and coverage of the sensor array, and how does it compare to fingertip-only sensors?
The sensor array comprises 18 sensing units distributed across the entire robotic hand, providing comprehensive tactile coverage. This distributed architecture enables detection of forces across the whole hand during grasping, unlike fingertip-only sensors that limit operations to simple actions. This expanded coverage is essential for complex tasks such as pulling and grasping.
How was the 90.1% object recognition accuracy achieved, and what is the inference latency for real-time applications?
Object recognition was achieved using convolutional neural network algorithms trained on tactile data from the sensor array. The 90.1% accuracy was validated experimentally, with results displayed in real time on a digital twin interface. While specific latency is not detailed, the real-time demonstration indicates suitability for adaptive grasping and teleoperation.
What are the scalability and manufacturing challenges for integrating this sensor array into commercial robotic hands?
The sensor array's design uses crosslinked interpenetrating networks and composite electrodes, which are compatible with scalable fabrication processes. However, integrating 18 sensing units across a commercial hand requires precise alignment and packaging. The demonstrated reliability over 10,000 cycles suggests robustness, but cost and production yield need further assessment for large-scale deployment.
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