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Aiming toward bioinspired materials whose responsivity evolves depending on their history, we disclose programmable liquid crystal polymer networks that ‘‘learn’’ to respond to an initially neutral stimulus (light) after association with an intrinsically effective stimulus (heating). The concept is inspired by the Pavlovian conditioning and enables soft robots that learn to walk, grippers that recognize different irradiation colors, and an artificial Pavlov’s dog. This is a step toward actuators that algorithmically mimic elementary aspects of learning.

Associative Learning by Classical Conditioning in Liquid Crystal Network Actuators