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Related Experiment Video

Updated: Jun 14, 2026

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

Deep learning-enabled versatile shape perception for soft robots via single-ended multimode fiber.

Zhaofan He1,2, Lele Wang1,2, Haidi Geng1,2

  • 1Department of Precision Instrument, Tsinghua University, Beijing 100084, China.

Science Advances
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

Researchers developed a versatile shape perception method using a single multimode fiber (MMF) and deep learning. This optical approach enables precise proprioception for soft robots, enhancing their intelligent capabilities in complex scenarios.

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Area of Science:

  • Robotics and Intelligent Systems
  • Optical Sensing and Metrology
  • Artificial Intelligence and Machine Learning

Background:

  • Advancements in soft robotics towards embodied intelligent systems necessitate precise proprioception.
  • Existing methods for capturing continuous deformations in soft robots are limited, especially in confined interventional settings.
  • A universal solution for versatile shape perception in soft robots is currently lacking.

Purpose of the Study:

  • To introduce a novel deep learning-enabled versatile shape perception method for soft robots.
  • To address the limitations of current proprioception techniques in diverse and confined robotic interactions.
  • To establish a foundational framework for enhanced closed-loop control in soft robotic systems.

Main Methods:

  • Utilized a single-ended multimode fiber (MMF) with a minimalist reflective optical architecture.
  • Leveraged intrinsic optical integration, eliminating the need for complex demodulation units and distal devices.
  • Treated chaotic optical speckle fields as data streams encoding high-dimensional shape information, processed by reconfigurable neural decoders.

Main Results:

  • Achieved high accuracy (>99%) in discrete state confirmation for soft grippers.
  • Demonstrated continuous shape tracking on bionic dexterous hands with a ~5-fold spatial resolution enhancement.
  • Enabled intuitive 3D morphological reconstruction of soft surgical robots with an IoU > 0.93.

Conclusions:

  • The developed method provides a versatile framework for overcoming hardware adaptability limitations through computation.
  • This optical sensing approach establishes a robust foundation for precise proprioception in soft robots.
  • The findings pave the way for advanced closed-loop control strategies in digital twins of soft robots.