Experimental demonstration of third-order memristor-based artificial sensory nervous system for neuro-inspired robotics

See-On Park, Hakcheon Jeong, Seokho Seo, Youna Kwon, Jongwon Lee () and Shinhyun Choi ()
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See-On Park: Korea Advanced Institute of Science and Technology (KAIST)
Hakcheon Jeong: Korea Advanced Institute of Science and Technology (KAIST)
Seokho Seo: Korea Advanced Institute of Science and Technology (KAIST)
Youna Kwon: National Nanofab Center (NNFC)
Jongwon Lee: Chungnam National University
Shinhyun Choi: Korea Advanced Institute of Science and Technology (KAIST)

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract The sensory nervous system in animals enables the perception of external stimuli. Developing an artificial sensory nervous system has been widely conducted to realize neuro-inspired robots capable of effectively responding to external stimuli. However, it remains challenging to develop artificial sensory nervous systems that possess sophisticated biological functions, such as habituation and sensitization, enabling efficient responses without bulky peripheral circuitry. Here, we introduce a memristor device with third-order switching complexity, emulating an artificial synapse that inherently possesses habituation and sensitization properties. Incorporating an additional resistive switching TiOx layer into the HfO2 memristor exhibits third-order switching complexity and non-volatile habituation characteristics. Based on the third-order memristor, we propose a robotic system equipped with a memristor-based artificial sensory nervous system for optimizing the robot arm’s response to external stimuli without the aid of processors. It is experimentally demonstrated that the robot arm with the developed memristor-based artificial sensory nervous system ignores approximately 71% of safe and familiar stimuli while sensitively responding to threatening and significant stimuli, similar to the habituation and sensitization of biological sensory nervous systems. Our findings can be a stepping stone for energy-efficient and intelligent robotic systems with reduced hardware burden.

Date: 2025
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DOI: 10.1038/s41467-025-60818-x

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