Associative Learning by Classical Conditioning in Liquid Crystal Network Actuators

Responsive and shape-memory materials allow stimuli-driven switching between fixed states. However, their behavior remains unchanged under repeated stimuli exposure, i.e., their properties do not evolve. By contrast, biological materials allow learning in response to past experiences. Classical conditioning is an elementary form of associative learning, which inspires us to explore simplified routes even for inanimate materials to respond to new, initially neutral stimuli. Here, we demonstrate that soft actuators composed of thermoresponsive liquid crystal networks “learn” to respond to light upon a conditioning process where light is associated with heating. We apply the concept to soft microrobotics, demonstrating a locomotive system that “learns to walk” under periodic light stimulus, and gripping devices able to “recognize” irradiation colors. We anticipate that actuators that algorithmically emulate elementary aspects of associative learning and whose sensitivity to new stimuli can be conditioned depending on past experiences may provide new routes toward adaptive, autonomous soft microrobotics.