Associative Learning by Classical Conditioning in Liquid Crystal Network Actuators

Zeng, Hao; Zhang, Hang; Ikkala, Olli; Priimägi, Arri

doi:10.1016/j.matt.2019.10.019

Cited by 61 publications

(67 citation statements)

References 58 publications

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“…To achieve associative learning in artificial materials, a memory and mechanisms (logic gates) to trigger and retrieve the memory are indispensable . The behavior of the Pavlovian materials can in some sense be considered as “switching” of the material property, since it requires the memory to be switched on by the simultaneous exposure to two stimuli (in the present case, light + heat).…”

Section: Discussion and Perspectivementioning

confidence: 99%

“…Therein potentiation or depression can be achieved based on the tuning of spiking. In view of the above‐mentioned literature on classical conditioning in artificial systems, we next review the newly identified concept of learning in synthetic materials by programming the material response with classical conditioning algorithm …”

Section: Pavlovian Materialsmentioning

confidence: 99%

“…As another example, we implemented the classical conditioning algorithm in mechanically deformable liquid crystal networks (LCNs), by introducing a different kind of built‐in triggerable memory . LCNs are synthetic materials combining the anisotropy of liquid crystals and the elasticity of crosslinked polymer networks .…”

Section: Pavlovian Materialsmentioning

confidence: 99%

“…c–e) Soft robotic demonstrations: a small‐sized walker insensitive to light becomes photomechanically deformable after conditioning (c); a gripper learns to grab a light‐scattering object with specific color (d); and an artificial Pavlov's dog showing jaw opening and salivation upon irradiation after conditioning (e). b–e) Adapted with permission . Copyright 2019, Cell Press.…”

Section: Pavlovian Materialsmentioning

confidence: 99%

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Viewpoint: Pavlovian Materials—Functional Biomimetics Inspired by Classical Conditioning

et al. 2020

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Herein, it is discussed whether the complex biological concepts of (associative) learning can inspire responsive artificial materials. It is argued that classical conditioning, being one of the most elementary forms of learning, inspires algorithmic realizations in synthetic materials, to allow stimuli‐responsive materials that learn to respond to a new stimulus, to which they are originally insensitive. Two synthetic model systems coined as “Pavlovian materials” are described, whose stimuli‐responsiveness algorithmically mimics programmable associative learning, inspired by classical conditioning. The concepts minimally need a stimulus‐triggerable memory, in addition to two stimuli, i.e., the unconditioned and the originally neutral stimuli. Importantly, the concept differs conceptually from the classic stimuli‐responsive and shape‐memory materials, as, upon association, Pavlovian materials obtain a given response using a new stimulus (the originally neutral one); i.e., the system evolves to a new state. This also enables the functionality to be described by a logic diagram. Ample room for generalization to different stimuli and memory combinations is foreseen, and opportunities to develop future adaptive materials with ever‐more intelligent functions are expected.

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Section: Discussion and Perspectivementioning

confidence: 99%

Section: Pavlovian Materialsmentioning

confidence: 99%

Section: Pavlovian Materialsmentioning

confidence: 99%

Section: Pavlovian Materialsmentioning

confidence: 99%

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Viewpoint: Pavlovian Materials—Functional Biomimetics Inspired by Classical Conditioning

et al. 2020

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“…Recently, the associative learning LCNs, also termed as Pavlovian materials, that "learn" to respond to an initially neutral stimulus only after being trained by an independent stimulus were designed by Priimagi and co-workers, providing unforeseen routes toward self-studying soft robots. [130,131] The principle of a Pavlovian material is shown in Figure 10A. Initially, the material is insensitive to stimulus 2 but responds to stimulus 1.…”

Section: Associative Learning Lcns For Self-studying Soft Robotsmentioning

confidence: 99%

Liquid Crystal Polymer‐Based Soft Robots

Xiao

Jiang

Zhao

2020

Advanced Intelligent Systems

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Soft robots outperform the conventional robots on enhanced safety for human-machine interaction, environmental adaptability, and continuous deformation. In this blooming area of fundamental and technological importance, liquid crystal polymer networks and liquid crystal elastomers (referred to as LCNs) have emerged as one of the most valuable candidates for soft robots due to their complex, large, and reversible shape change capabilities. To date, much research effort, mainly regarding chemical synthesis, fabrication technologies and soft robot design, has been dedicated to LCN robotic systems to endow them with versatile and complex actions controlled by various stimuli. Herein, starting with the principle that governs the stimuli-responsiveness of LCNs, recent progress made in LCN soft robots is summarized while focusing on different robotic motions, such as grapping, walking, swimming, and oscillation. Especially, novel LCNs with intelligent functions such as reprocessability, reconfigurability, self-regulating behavior and associative learning capability, are highlighted. This article aims to provide significant insights into the design and development of LCN-based soft robots.

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Humidity‐Responsive Liquid Crystalline Network Actuator Showing Synergistic Fluorescence Color Change Enabled by Aggregation Induced Emission Luminogen

Lan

Gao

Shen

et al. 2021

Adv Funct Materials

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Imitating the structures and behaviors of natural creatures is of great significance to scientists to explore novel materials for practical applications. However, the design and fabrication of biomimetic devices with complex and outstanding performances is still on the way. A bilayer film composed of liquid crystalline networks (LCN) film and hydrochromic aggregation‐induced‐emission molecule‐doped hydrophilic layer is prepared. Under different relative humidity, the composite film can deform and change fluorescence color simultaneously. The influence of the content of the hydrophilic matrix on the fluorescent property and humidity‐responsive behaviors of the bilayer film is investigated. Thanks to the mechanical anisotropy provided by uniform‐aligned LCN film, different modes of deformation of the bilayer film are achievable, like bending, curling, and twisting. More importantly, due to the independence of the LCN film and the non‐mesogenic molecules brought by this bimorph strategy, complex alignment of the LCN film and modification by immiscible molecules are realized in a single LCN actuator. Based on the functional composite film, artificial flowers showing synergistic blooming and shape‐changing is prepared. By regulating the molecular alignment of the LC mesogens of the LCN film, the artificial flowers can imitate various blooming behaviors of natural flowers like confederate jasmine and jade lotus.

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Associative Learning by Classical Conditioning in Liquid Crystal Network Actuators

Cited by 61 publications

References 58 publications

Viewpoint: Pavlovian Materials—Functional Biomimetics Inspired by Classical Conditioning

Viewpoint: Pavlovian Materials—Functional Biomimetics Inspired by Classical Conditioning

Liquid Crystal Polymer‐Based Soft Robots

Humidity‐Responsive Liquid Crystalline Network Actuator Showing Synergistic Fluorescence Color Change Enabled by Aggregation Induced Emission Luminogen

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