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Related Experiment Video

Updated: May 5, 2026

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Gait Optimization Control of Spinal Quadruped Robot Based on Deep Reinforcement Learning.

Guozheng Song1,2, Qinglin Ai1,2, Lin Li1,2

  • 1College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a spinal joint for quadruped robots, enhancing flexibility. A deep reinforcement learning framework combining central pattern generators (CPG) and TD3 algorithms optimizes gait for improved stability and terrain adaptability.

Keywords:
TD3–CPGdeep reinforcement learninggait optimizationjoint incremental strategyspinal quadruped robot

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Area of Science:

  • Robotics
  • Bio-inspired engineering
  • Artificial Intelligence

Background:

  • Quadrupedal locomotion relies on spinal flexibility for natural movement and adaptability.
  • Existing quadruped robots often lack the dynamic range of motion seen in biological systems.
  • Integrating spinal actuation offers potential for enhanced mobility and posture control in robotic platforms.

Purpose of the Study:

  • To develop and validate a novel control strategy for a quadruped robot with an actuated spinal joint.
  • To improve the gait stability and terrain adaptability of spinal quadruped robots in complex environments.
  • To explore the integration of central pattern generators (CPG) with deep reinforcement learning for optimized robotic locomotion.

Main Methods:

  • Designed a quadruped robot model incorporating an actuated spinal joint and analyzed its parameters.
  • Developed a central pattern generator (CPG) coupling model that integrates spinal motion parameters.
  • Implemented a deep reinforcement learning framework combining CPG with the twin delayed deterministic policy gradient (TD3) algorithm, utilizing a joint incremental strategy for gait optimization.
  • Conducted simulations and experiments on various obstacle terrains to evaluate the proposed TD3-CPG algorithm.

Main Results:

  • The proposed TD3-CPG algorithm effectively optimized the gait of the spinal quadruped robot.
  • Significant improvements were observed in walking stability, speed, and terrain adaptability across diverse obstacle terrains.
  • The integration of the spinal joint and the advanced control framework demonstrated enhanced dynamic motion and posture adjustment capabilities.
  • Experimental validation confirmed the algorithm's effectiveness in real-world scenarios.

Conclusions:

  • The developed deep reinforcement learning framework (TD3-CPG) successfully enhances the locomotion capabilities of spinal quadruped robots.
  • The bio-inspired spinal joint, coupled with intelligent control, significantly improves robot performance in challenging environments.
  • This research provides a promising approach for developing more agile and adaptable legged robots for various applications.