Memristor-based adaptive neuromorphic perception in unstructured environments

Shengbo Wang, Shuo Gao (), Chenyu Tang, Edoardo Occhipinti, Cong Li, Shurui Wang, Jiaqi Wang, Hubin Zhao, Guohua Hu, Arokia Nathan, Ravinder Dahiya and Luigi Giuseppe Occhipinti ()
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Shengbo Wang: Beihang University
Shuo Gao: Beihang University
Chenyu Tang: University of Cambridge
Edoardo Occhipinti: Imperial College London
Cong Li: Beihang University
Shurui Wang: Beihang University
Jiaqi Wang: Beihang University
Hubin Zhao: Division of Surgery and Interventional Science, UCL
Guohua Hu: The Chinese University of Hong Kong, Shatin, N. T.
Arokia Nathan: University of Cambridge
Ravinder Dahiya: Northeastern University
Luigi Giuseppe Occhipinti: University of Cambridge

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Efficient operation of control systems in robotics or autonomous driving targeting real-world navigation scenarios requires perception methods that allow them to understand and adapt to unstructured environments with good accuracy, adaptation, and generality, similar to humans. To address this need, we present a memristor-based differential neuromorphic computing, perceptual signal processing, and online adaptation method providing neuromorphic style adaptation to external sensory stimuli. The adaptation ability and generality of this method are confirmed in two application scenarios: object grasping and autonomous driving. In the former, a robot hand realizes safe and stable grasping through fast ( ~ 1 ms) adaptation based on the tactile object features with a single memristor. In the latter, decision-making information of 10 unstructured environments in autonomous driving is extracted with an accuracy of 94% with a 40×25 memristor array. By mimicking human low-level perception mechanisms, the electronic neuromorphic circuit-based method achieves real-time adaptation and high-level reactions to unstructured environments.

Date: 2024
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DOI: 10.1038/s41467-024-48908-8

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