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Data-Driven Twisted String Actuation for Lightweight and Compliant Anthropomorphic Dexterous Hands.

Zhiyao Zheng1, Jingwei Zhan2,3, Zhaochun Li1

  • 1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

Biomimetics (Basel, Switzerland)
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel approach to control robotic hands using data-driven modeling and biomimetic design. It enhances the precision and performance of lightweight robotic hands for complex interactions.

Keywords:
anthropomorphic dexterous handback propagation neural networkbiomimetic designdata-driven modellingtwisted string actuatorvariable-load control

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Area of Science:

  • Robotics
  • Mechanical Engineering
  • Artificial Intelligence

Background:

  • Traditional robotic hand actuation systems are heavy, complex, and lack compliance.
  • Twisted String Actuators (TSAs) offer lightweight, compliant alternatives but struggle with nonlinear control under variable loads.

Purpose of the Study:

  • To develop a high-precision control method for TSAs under variable loads.
  • To design and validate a lightweight, anthropomorphic dexterous robotic hand using innovative mechanisms.

Main Methods:

  • A dual hidden-layer Back Propagation Neural Network (BPNN) was used for data-driven TSA displacement prediction.
  • A biomimetic, underactuated five-finger dexterous hand with a tendon-spring transmission was engineered.
  • An experimental platform integrated BPNN-kinematic modeling for performance validation.

Main Results:

  • Accurate prediction of TSA displacement under loads ranging from 0.1-4.2 kg.
  • Achieved an ultra-lightweight dexterous hand with precise bending angle prediction.
  • Demonstrated versatile gesture replication and robust grasping with a maximum fingertip force of 7.4 N.

Conclusions:

  • The integrated data-driven modeling and biomimetic design advance TSA control for variable-load applications.
  • This work presents a new paradigm for creating high-performance, lightweight dexterous robotic hands.