Artificial plateau neurons with in-situ spike-malleability for rhythmic quadrupedal locomotion

Whole-body intelligent locomotion systems face persistent challenges of redundant actuation and poor energy efficiency, limiting real-world deployment. Bio-inspired central pattern generators offer a promising framework for rhythmic control, yet hardware implementations struggle to match the efficiency and adaptability of biological systems. Here, we introduce an in-situ spike-malleable artificial plateau neuron integrating a bistable plateau gate with a transient threshold-switch. The neuron generates amplitude-programmable rhythmic spike bursts, achieving energy-efficient, antagonistic activation of extensors and flexors via a scalable circuit comprising two paired units (plateau gate and threshold-switch). The design leverages distributed encoding for coordinated muscle control, operating at ultra-low energy dissipation (141.37 pJ/spike). An expanded four-unit circuit enhances dynamic spike malleability, enabling parallel processing for multi-joint coordination. On a quadruped robot (Unitree Go2), these distributed circuits directly drive joint-level proportional derivative controllers using the Gaussian-filtered rhythmic spikes, enabling energy-efficient trotting without centralized computation. Critically, the system achieves stable on-ground locomotion and demonstrates adaptive gait transitions in real-world environments. Our approach merges ultra-compact hardware with bio-inspired architecture, advancing neuromorphic systems for energy-efficient autonomous robotics.

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Publication Details

Journal
Nature Communications
Published
2026-04-28
DOI
https://doi.org/10.1038/s41467-026-72428-2
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Artificial plateau neurons with in-situ spike-malleability for rhythmic quadrupedal locomotion

Guodong Xue, Zongwen Li, Guanyu Liu, Bin Yu et al.
Nature Communications
Advanced Memory and Neural Computing
article

Artificial plateau neurons with in-situ spike-malleability for rhythmic quadrupedal locomotion

Guodong Xue, Zongwen Li, Guanyu Liu, Bin Yu, Yishu Zhang, Yang Xu, Qian He, Qing’ao Chai, Jian Chai, Yu Fu, Yongqing Bai, Xin He, Hailiang Wang, Jiayang Hu, Huajin Tang, Mengze Zhao, Kaihui Liu
article en

Abstract

Whole-body intelligent locomotion systems face persistent challenges of redundant actuation and poor energy efficiency, limiting real-world deployment. Bio-inspired central pattern generators offer a promising framework for rhythmic control, yet hardware implementations struggle to match the efficiency and adaptability of biological systems. Here, we introduce an in-situ spike-malleable artificial plateau neuron integrating a bistable plateau gate with a transient threshold-switch. The neuron generates amplitude-programmable rhythmic spike bursts, achieving energy-efficient, antagonistic activation of extensors and flexors via a scalable circuit comprising two paired units (plateau gate and threshold-switch). The design leverages distributed encoding for coordinated muscle control, operating at ultra-low energy dissipation (141.37 pJ/spike). An expanded four-unit circuit enhances dynamic spike malleability, enabling parallel processing for multi-joint coordination. On a quadruped robot (Unitree Go2), these distributed circuits directly drive joint-level proportional derivative controllers using the Gaussian-filtered rhythmic spikes, enabling energy-efficient trotting without centralized computation. Critically, the system achieves stable on-ground locomotion and demonstrates adaptive gait transitions in real-world environments. Our approach merges ultra-compact hardware with bio-inspired architecture, advancing neuromorphic systems for energy-efficient autonomous robotics.

Nature Communications
Zhejiang University of Science and Technology (CN), Peking University (CN), Zhejiang Lab (CN), Zhejiang University of Technology (CN), Zhejiang University (CN), Renmin University of China (CN)
National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province
Affordable and clean energy
Openalex Percentile: Top 8%
Advanced Memory and Neural Computing
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