Bio-inspired neural networks with central pattern generators for learning multi-skill locomotion
Abstract Biological neural circuits, central pattern generators (CPGs), located at the spinal cord are the underlying mechanisms that play a crucial role in generating rhythmic locomotion patterns. In this paper, we propose a novel approach that leverages the inherent rhythmicity of CPGs to enhance...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-94408-0 |
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| author | Chuanyu Yang Can Pu Yuan Zou Tianqi Wei Cong Wang Zhibin Li |
| author_facet | Chuanyu Yang Can Pu Yuan Zou Tianqi Wei Cong Wang Zhibin Li |
| author_sort | Chuanyu Yang |
| collection | DOAJ |
| description | Abstract Biological neural circuits, central pattern generators (CPGs), located at the spinal cord are the underlying mechanisms that play a crucial role in generating rhythmic locomotion patterns. In this paper, we propose a novel approach that leverages the inherent rhythmicity of CPGs to enhance the locomotion capabilities of quadruped robots. Our proposed network architecture incorporates CPGs for rhythmic pattern generation and a multi-layer perceptron (MLP) network for fusing multi-dimensional sensory feedback. In particular, we also proposed a method to reformulate CPGs into a fully-differentiable, stateless network, allowing CPGs and MLP to be jointly trained using gradient-based learning. The effectiveness and performance of our approach are demonstrated through extensive experiments. Our learned locomotion policies exhibit agile and dynamic locomotion behaviors which are capable of traversing over uneven terrain blindly and resisting external perturbations. Furthermore, results demonstrated the remarkable multi-skill capability within a single unified policy network, including fall recovery and various quadrupedal gaits. Our study highlights the advantages of integrating bio-inspired neural networks which are capable of achieving intrinsic rhythmicity and fusing sensory feedback for generating smooth, versatile, and robust locomotion behaviors, including both rhythmic and non-rhythmic locomotion skills. |
| format | Article |
| id | doaj-art-222a0bc028b74b1fa5faf42ea67d8c4e |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-222a0bc028b74b1fa5faf42ea67d8c4e2025-08-20T02:10:10ZengNature PortfolioScientific Reports2045-23222025-03-0115111810.1038/s41598-025-94408-0Bio-inspired neural networks with central pattern generators for learning multi-skill locomotionChuanyu Yang0Can Pu1Yuan Zou2Tianqi Wei3Cong Wang4Zhibin Li5National Elite Institute of Engineering, Chongqing UniversityNational Elite Institute of Engineering, Chongqing UniversityShenzhen Amigaga Technology Co Ltd.School of Artificial Intelligence, Sun Yat-sen UniversityShenyang Institute of Automation, Chinese Academy of SciencesDepartment of Computer Science, University College LondonAbstract Biological neural circuits, central pattern generators (CPGs), located at the spinal cord are the underlying mechanisms that play a crucial role in generating rhythmic locomotion patterns. In this paper, we propose a novel approach that leverages the inherent rhythmicity of CPGs to enhance the locomotion capabilities of quadruped robots. Our proposed network architecture incorporates CPGs for rhythmic pattern generation and a multi-layer perceptron (MLP) network for fusing multi-dimensional sensory feedback. In particular, we also proposed a method to reformulate CPGs into a fully-differentiable, stateless network, allowing CPGs and MLP to be jointly trained using gradient-based learning. The effectiveness and performance of our approach are demonstrated through extensive experiments. Our learned locomotion policies exhibit agile and dynamic locomotion behaviors which are capable of traversing over uneven terrain blindly and resisting external perturbations. Furthermore, results demonstrated the remarkable multi-skill capability within a single unified policy network, including fall recovery and various quadrupedal gaits. Our study highlights the advantages of integrating bio-inspired neural networks which are capable of achieving intrinsic rhythmicity and fusing sensory feedback for generating smooth, versatile, and robust locomotion behaviors, including both rhythmic and non-rhythmic locomotion skills.https://doi.org/10.1038/s41598-025-94408-0 |
| spellingShingle | Chuanyu Yang Can Pu Yuan Zou Tianqi Wei Cong Wang Zhibin Li Bio-inspired neural networks with central pattern generators for learning multi-skill locomotion Scientific Reports |
| title | Bio-inspired neural networks with central pattern generators for learning multi-skill locomotion |
| title_full | Bio-inspired neural networks with central pattern generators for learning multi-skill locomotion |
| title_fullStr | Bio-inspired neural networks with central pattern generators for learning multi-skill locomotion |
| title_full_unstemmed | Bio-inspired neural networks with central pattern generators for learning multi-skill locomotion |
| title_short | Bio-inspired neural networks with central pattern generators for learning multi-skill locomotion |
| title_sort | bio inspired neural networks with central pattern generators for learning multi skill locomotion |
| url | https://doi.org/10.1038/s41598-025-94408-0 |
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