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- Title
Memristor-based adaptive neuromorphic perception in unstructured environments.
- Authors
Wang, Shengbo; Gao, Shuo; Tang, Chenyu; Occhipinti, Edoardo; Li, Cong; Wang, Shurui; Wang, Jiaqi; Zhao, Hubin; Hu, Guohua; Nathan, Arokia; Dahiya, Ravinder; Occhipinti, Luigi Giuseppe
- 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. Perception methods that enable control systems to understand and adapt to unstructured environments are desired. Wang et. al. develop a memristor-based differential neuromorphic computing, perceptual signal processing, and online adaptation method providing neuromorphic style adaptation to external sensory stimuli.
- Subjects
AUTONOMOUS robots; PREHENSION (Physiology); ROBOT hands; AUTONOMOUS vehicles; NEUROPLASTICITY; SIGNAL processing; PHYSIOLOGICAL adaptation
- Publication
Nature Communications, 2024, Vol 15, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-024-48908-8