摘要Autonomous legged robots,capable of navigating uneven terrain,can perform a diverse array of tasks.However,designing locomotion controllers remains challenging.In particular,designing a controller based on durable and reliable propriocep-tive sensors,is essential for achieving adaptability.Presently,the controller must either be manually designed for specific robots and tasks,or developed using machine-learning techniques,which require extensive training time and result in complex controllers.Inspired by animal locomotion,we propose a simple yet comprehensive closed-loop modular frame-work that utilizes minimal proprioceptive feedback(i.e.,the Coxa-Femur(CF)joint angle),enabling a quadruped robot to efficiently navigate unpredictable and uneven terrains,including the step and slope.The framework comprises a basic neural control network capable of rapidly learning optimized motor patterns,and a straightforward module for sensory feedback sharing and integration.In a series of experiments,we show that integrating sensory feedback into the base neu-ral control network aids the robot in continually learning robust motor patterns on flat,step,and slope terrain,compared with the open-loop base framework.Sharing sensory feedback information across the four legs enables a quadruped robot to proactively navigate unpredictable steps with minimal interaction.Furthermore,the controller remains functional even in the absence of sensor signals.This control configuration was successfully transferred to a physical robot without any modifications.