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<p>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 proprioceptive 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 framework 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 neural 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.</p>

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Continuous Learning and Adaptation of Neural Control for Proprioceptive Feedback Integration in a Quadruped Robot

  • 期刊论文

  • 发表日期：

    2025年6月30日

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  • Yanbin Zhang,
  • Yang Li,
  • Zhendong Dai

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 proprioceptive 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 framework 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 neural 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.