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Deep Learning-Assisted Intelligent Liquid Crystal Elastomer Grippers Based on Autonomous Triboelectric Sensing.

Zhengyang Chen1, Yifei Nan1,2, Lanying Zhang3

  • 1State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Department of Electrical & Electronic Engineering, Guangdong Provincial Key Laboratory of Functional Oxide Materials and Devices, Southern University of Science and Technology, Shenzhen 518055, China.

ACS Applied Materials & Interfaces
|February 26, 2026
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Summary

This study introduces a self-powered soft gripper using liquid crystal elastomers and dual-mode triboelectric nanogenerators for object identification. It achieves high accuracy by combining material properties and motion data, overcoming environmental interference for intelligent robotics.

Keywords:
deep learningliquid crystal elastomermultimodal tactile sensingsoft actuatortriboelectric nanogenerator sensor

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Area of Science:

  • Robotics and Materials Science
  • Soft robotics and advanced sensor integration

Background:

  • Soft grippers offer flexibility and damage-free handling but lack robust, self-powered sensing capabilities.
  • Existing sensing methods for soft grippers are susceptible to environmental interference, limiting their practical applications.

Purpose of the Study:

  • To develop a self-powered soft gripper with integrated sensing for autonomous target identification.
  • To overcome the limitations of external sensors in soft robotic systems by creating an intrinsic sensing mechanism.

Main Methods:

  • Integration of a liquid crystal elastomer (LCE) gripper with dual-mode triboelectric nanogenerators (TENGs) using fluorinated ethylene propylene (FEP) and polydimethylsiloxane (PDMS).
  • Utilizing generated voltage signals (V1, V2) that encode material properties and kinematic parameters during object interaction.
  • Employing a hybrid convolutional neural network-long short-term memory (CNN-LSTM) deep learning architecture for feature extraction from triboelectric/electrostatic signatures.

Main Results:

  • The integrated TENG sensors successfully generated distinct voltage signals based on object properties and gripper motion.
  • The CNN-LSTM model achieved 94.4% classification accuracy across 5 material categories via cross-validation.
  • The system demonstrated robustness against environmental interference, a significant improvement over traditional sensing methods.

Conclusions:

  • The developed LCE gripper with dual-mode TENGs provides a self-powered, intrinsically sensing solution for soft robotics.
  • The fusion of triboelectric/electrostatic sensing with deep learning offers a promising approach for perceptually intelligent soft robotic systems.
  • This technology has potential applications in industrial automation and human-machine interaction, enhancing robotic perception and adaptability.