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

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A Learning-Based Approach to Sensorize Soft Robots.

Benjamin Wee Keong Ang^1,2, Chen-Hua Yeow^1,2

¹Evolution Innovation Lab, National University of Singapore, Singapore, Singapore.

|May 4, 2022

Summary

This summary is machine-generated.

Researchers developed 3D-printed soft actuators that can sense their own state without embedded sensors. This machine learning approach enables consistent, real-time multimodal sensing for soft robots.

Keywords:

3D print long short-term memory machine learning perception soft robot control soft sensing

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Area of Science:

Robotics
Materials Science
Machine Learning

Background:

Traditional soft robots require separate actuators and sensors, complicating design and sensing capabilities.
Existing self-sensing actuators often rely on manual fabrication, leading to inconsistent performance.
The inherent nonlinearity of soft materials complicates accurate sensing.

Purpose of the Study:

To develop a novel self-sensing soft actuator using 3D printing and machine learning.
To eliminate the need for embedded sensing elements in soft actuators.
To achieve consistent, real-time multimodal sensing for soft robotic applications.

Main Methods:

Fabrication of soft actuators using 3D printing for consistent performance.
Application of machine learning algorithms to characterize nonlinear behaviors.
Development of a technique for real-time estimation of bending curvature and external forces.

Main Results:

Achieved self-sensing capabilities in soft actuators without embedded sensors.
Demonstrated consistent sensing performance due to 3D printing fabrication.
Successfully estimated bending curvature and tip forces in real time.

Conclusions:

Machine learning enables self-sensing in soft actuators, overcoming limitations of traditional designs.
3D printing ensures reproducible and reliable sensing performance.
The proposed methodology offers a generalizable approach for multimodal sensing in soft robots.