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Related Experiment Video

Updated: Dec 30, 2025

Microfluidic Preparation of Liquid Crystalline Elastomer Actuators

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Published on: May 20, 2018

Associative Learning by Classical Conditioning in Liquid Crystal Network Actuators.

Hao Zeng¹, Hang Zhang², Olli Ikkala²

¹Smart Photonic Materials, Faculty of Engineering and Natural Sciences, Tampere University, P.O. Box 541, 33101 Tampere, Finland.

|January 28, 2020

Summary

This summary is machine-generated.

Soft actuators made of liquid crystal networks can learn to respond to light after a conditioning process. This associative learning enables adaptive behaviors in soft microrobotics, mimicking biological systems.

Keywords:

MAP4: demonstrate actuation bioinspired biomimetics classical conditioning light-responsive liquid crystal network soft robotics stimuli-responsive

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Area of Science:

Materials Science
Robotics
Biomimicry

Background:

Responsive materials switch states with stimuli but lack adaptive learning.
Biological systems exhibit learning from past experiences, unlike current artificial materials.

Purpose of the Study:

To develop inanimate materials capable of associative learning.
To enable soft actuators to respond to new stimuli through conditioning.

Main Methods:

Thermoresponsive liquid crystal networks were used to create soft actuators.
A conditioning process associated light (neutral stimulus) with heating (unconditional stimulus).

Main Results:

Soft actuators learned to respond to light independently after conditioning.
Demonstrated a microrobotic system that learns to walk and gripping devices that recognize colors.

Conclusions:

Inanimate actuators can emulate elementary associative learning.
Conditioned sensitivity in actuators opens new avenues for adaptive, autonomous soft microrobotics.