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Related Experiment Video

Updated: Nov 13, 2025

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot
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A Vision-Based Sensing Approach for a Spherical Soft Robotic Arm.

Matthias Hofer1, Carmelo Sferrazza1, Raffaello D'Andrea1

  • 1Institute for Dynamic Systems and Control, ETH Zurich, Zurich, Switzerland.

Frontiers in Robotics and AI
|March 15, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a vision-based sensing method for fabric soft robotic arms, enabling proprioception without mechanical contact. This approach uses cameras and a convolutional neural network for accurate, real-time orientation estimation.

Keywords:
computer visionfabric bellowspneumatic actuationproprioceptionsoft roboticssupervised machine learningvision-based sensing

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Area of Science:

  • Robotics
  • Computer Vision
  • Materials Science

Background:

  • Sensory feedback is critical for controlling soft robotic systems.
  • Proprioception (self-sensing) is vital for deploying soft robots outside controlled lab environments.
  • Existing methods often require mechanical interaction, compromising soft system compliance.

Purpose of the Study:

  • To present a vision-based sensing approach for fabric soft robotic arms.
  • To enable proprioception for soft robots without mechanical sensor integration.
  • To maintain the inherent compliance of soft robotic systems.

Main Methods:

  • Integrating cameras into inflatable, fabric-based bellow actuators.
  • Utilizing three camera-equipped actuators to control a spherical robotic arm.
  • Employing a convolutional neural network (CNN) for orientation prediction from camera images.
  • Using motion capture systems for ground truth data during supervised learning.

Main Results:

  • The vision-based system provides high-resolution sensory feedback.
  • No mechanical interaction is needed, preserving the soft robot's compliance.
  • The CNN accurately predicts orientation angles from camera images.
  • Real-time orientation estimation achieves approximately one-degree accuracy.

Conclusions:

  • Vision-based sensing offers a viable solution for proprioception in soft robotics.
  • The presented method allows for accurate, non-invasive state estimation.
  • The system's reliability is demonstrated through closed-loop control of the robotic arm.