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.
Authors
- Guodong Xue (ORCID: https://orcid.org/0009-0006-4366-2401)
- Zongwen Li (ORCID: https://orcid.org/0000-0002-8700-5685)
- Guanyu Liu (ORCID: https://orcid.org/0000-0002-1436-3414)
- Bin Yu (ORCID: https://orcid.org/0000-0003-0237-6311)
- Yishu Zhang (ORCID: https://orcid.org/0000-0001-8774-7374)
- Yang Xu (ORCID: https://orcid.org/0000-0003-3148-7678)
- Qian He (ORCID: https://orcid.org/0009-0005-8764-2122)
- Qing’ao Chai
- Jian Chai
- Yu Fu (ORCID: https://orcid.org/0009-0003-9251-4016)
- Yongqing Bai
- Xin He (ORCID: https://orcid.org/0000-0001-7009-2826)
- Hailiang Wang
- Jiayang Hu
- Huajin Tang
- Mengze Zhao
- Kaihui Liu (ORCID: https://orcid.org/0000-0002-8781-2495)
Institutions
- 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)
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
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Zhejiang Province