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Related Experiment Video

Updated: Jun 23, 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

Visuo-dynamic self-modelling of soft robotic systems.

Richard Marques Monteiro¹, Jialei Shi², Helge Wurdemann²

¹Bio-Inspired Robotics Lab, University of Cambridge, Cambridge, United Kingdom.

Frontiers in Robotics and AI

|June 20, 2024

Summary

This study introduces a novel learning-based method for dynamic modeling of soft robots directly in visual space. This approach enables comprehensive control for various soft robotic systems without predefined structures.

Keywords:

machine learning modelling and control optimal control recurrent neural net (RNN)soft robotics

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Area of Science:

Robotics
Machine Learning
Control Theory

Background:

Soft robots present complex nonlinear dynamics and high degrees of freedom, posing significant challenges for traditional modeling and control.
Existing reduced-order models often compromise control accuracy and limit the operational range of soft robots.

Purpose of the Study:

To develop an end-to-end learning-based approach for fully dynamic modeling of general robotic systems.
To enable direct learning of dynamic models in the visual space, bypassing predefined structural assumptions.

Main Methods:

An end-to-end learning framework was employed to directly learn dynamic models from visual observations.
The models generated have dimensionality matching the observation space, adapting complexity to the sensory system.
The method was applied to a soft robotic manipulator for controller development.

Main Results:

The proposed method successfully modeled a soft robotic manipulator using only 90 minutes of real-world data.
Demonstrated applicability in controller development, achieving dynamic control tasks like shape control, trajectory tracking, and obstacle avoidance.
The learned models achieved a wide range of dynamic control capabilities.

Conclusions:

This work presents a comprehensive strategy for controlling general soft robotic systems, irrespective of their shape, properties, or dimensionality.
The visual-space dynamic modeling approach offers a powerful, unconstrained method for soft robot control.
The findings pave the way for more advanced and versatile soft robot applications.