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However, model\u2010based approaches benefit from precise model analysis, which can be cumbersome and demand substantial domain expertise, while learning\u2010based methods, though promising, often necessitate prolonged training periods and may result in complex and opaque controllers. This architecture aims to mimic neural\u2010muscle control and sensory feedback mechanisms, enabling legged robots to adjust neural signal intensity based on proprioceptive feedback and achieve behavior responses similar to those observed in animals. Specifically, and the central pattern generator creates insect\u2010like gaits, the virtual motoneurons network generates continuously adjustable trajectories for omnidirectional motion and limb control. The sensorimotor integration module, event\u2010based finite state machine, and local reactive strategies allow robots to traverse unstructured terrains. The method is experimentally applied to a newly developed hexapod robot named RENS H2. 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