Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Determinant Factors of Depressive Disorder in Spain: A Comprehensive Analysis of the European Health Interview Survey.

The International journal of social psychiatry·2026

Same author

Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis.

ACS central science·2025

Same author

Attention-based map encoding for learning generalized legged locomotion.

Science robotics·2025

Same author

Characterization of spine and torso stiffness via differentiable biomechanics.

Medical image analysis·2025

Same author

Enriching productive mutational paths accelerates enzyme evolution.

Nature chemical biology·2024

Same author

Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning.

ACS central science·2024

Search research articles

Related Experiment Video

Updated: Dec 31, 2025

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

Published on: June 10, 2020

MakeSense: Automated Sensor Design for Proprioceptive Soft Robots.

Javier Tapia^1,2, Espen Knoop¹, Mojmir Mutný¹

¹Disney Research, Zurich, Switzerland.

|January 1, 2020

Summary

This study introduces a computational method to add proprioceptive sensing to soft robots. The technique automatically designs minimal sensor networks for accurate deformation state reconstruction, enhancing robot interaction capabilities.

Keywords:

computational design sensor design soft sensing

More Related Videos

Bioinspired Soft Robot with Incorporated Microelectrodes

Bioinspired Soft Robot with Incorporated Microelectrodes

Published on: February 28, 2020

Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery

Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery

Published on: November 14, 2015

Related Experiment Videos

Last Updated: Dec 31, 2025

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

Published on: June 10, 2020

Bioinspired Soft Robot with Incorporated Microelectrodes

Bioinspired Soft Robot with Incorporated Microelectrodes

Published on: February 28, 2020

Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery

Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery

Published on: November 14, 2015

Area of Science:

Robotics
Soft Robotics
Sensor Networks

Background:

Soft robots offer advantages in human interaction and delicate object manipulation.
Proprioceptive sensing is crucial for soft robots to understand their physical state.
Current methods for sensor integration in soft robots can be complex and suboptimal.

Purpose of the Study:

To present a computational method for augmenting soft robots with proprioceptive sensing.
To automatically compute a minimal stretch-receptive sensor network for soft robotic designs.
To optimize sensor networks for accurate deformation state reconstruction under interaction forces.

Main Methods:

The sensor design problem is framed as a subselection problem, choosing a minimal sensor set from fabricable options.
An analytical gradient of the reconstruction performance measure is used with respect to selection variables.
The method is demonstrated on bending bar, gripper, and simulated tentacle soft robot designs.

Main Results:

The proposed method successfully computes minimal sensor networks for soft robots.
Sensorized robots can reconstruct their full deformation state under interaction forces.
The technique shows effectiveness on various soft robot designs, including complex ones.

Conclusions:

This computational approach enables efficient proprioceptive sensing augmentation for soft robots.
The method provides a systematic way to design optimal sensor networks for specific tasks.
The developed technique enhances the capabilities of soft robots in unstructured environments.