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Related Experiment Video

Updated: Aug 11, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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An enhanced deep deterministic policy gradient algorithm for intelligent control of robotic arms.

Ruyi Dong1, Junjie Du1, Yanan Liu1

  • 1College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, China.

Frontiers in Neuroinformatics
|February 9, 2023
PubMed
Summary

This study enhances the deep deterministic policy gradient (DDPG) algorithm for robot arm control. The improved DDPG shows better adaptability and faster convergence, achieving a 91.27% success rate in end-reaching tasks.

Keywords:
artificial intelligencedeep deterministic policy gradient algorithmexperience replay mechanismintelligent controlmachine learningreward functionrobotic arm

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional control methods lack robustness and adaptability in dynamic environments.
  • Deep deterministic policy gradient (DDPG) offers a potential solution but requires optimization.

Purpose of the Study:

  • To improve the robustness and adaptability of the DDPG algorithm for robot arm motion control.
  • To accelerate the convergence of the DDPG algorithm through enhanced sampling strategies.

Main Methods:

  • Developed a hybrid reward function by superimposing different reward components.
  • Modified the DDPG experience replay mechanism by combining priority and uniform sampling techniques.
  • Validated the improved algorithm in a simulated robot arm control environment.

Main Results:

  • The improved DDPG algorithm demonstrated accurate control over robot arm motion.
  • Achieved convergence in a shorter time compared to the original DDPG algorithm.
  • Reached an average success rate of 91.27% in robotic arm end-reaching tasks.

Conclusions:

  • The enhanced DDPG algorithm offers superior environmental adaptability and robustness.
  • The proposed modifications significantly improve learning efficiency and task success rates in robotics.
  • This approach provides a more effective control strategy for complex robotic applications.