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Memristor-based adaptive neuromorphic perception in unstructured environments

Author

Listed:
  • 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
    Shandong University)

  • Ravinder Dahiya

    (Northeastern University)

  • Luigi Giuseppe Occhipinti

    (University of Cambridge)

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.

Suggested Citation

  • Shengbo Wang & Shuo Gao & Chenyu Tang & Edoardo Occhipinti & Cong Li & Shurui Wang & Jiaqi Wang & Hubin Zhao & Guohua Hu & Arokia Nathan & Ravinder Dahiya & Luigi Giuseppe Occhipinti, 2024. "Memristor-based adaptive neuromorphic perception in unstructured environments," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48908-8
    DOI: 10.1038/s41467-024-48908-8
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    References listed on IDEAS

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