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Multi-agent deep reinforcement learning-based robotic arm assembly research.

Guohua Cao1, Jimeng Bai1

  • 1School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, China.

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Summary
This summary is machine-generated.

This study introduces a multi-agent reinforcement learning approach for robotic arm assembly, improving convergence and performance in complex shaft-hole tasks. The novel method enhances adaptability and stability in robotic assembly operations.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Single-agent algorithms struggle with convergence and performance in complex robotic arm assembly tasks due to variability.
  • Robotic arm assembly, particularly shaft-hole tasks, requires robust and adaptive control strategies.

Purpose of the Study:

  • To propose and evaluate a multi-agent reinforcement learning (MARL) algorithm for robotic arm shaft-hole assembly.
  • To enhance the convergence speed, stability, and adaptability of robotic assembly processes, focusing on square shaft-hole configurations.

Main Methods:

  • Analysis of shaft-hole assembly stages: hole-seeking, alignment, and insertion.
  • Integration of a novel reward function with the decoupled multi-agent deterministic deep deterministic policy gradient (DMDDPG) algorithm.
  • Development of a simulation environment in Gazebo for modeling robotic arm assembly with circular and square shaft-holes.

Main Results:

  • The proposed MARL algorithm, dividing the robotic arm into multi-agents (first three and last three joints), showed improved performance.
  • Demonstrated enhanced adaptability and faster, more stable convergence in shaft-hole assembly simulations.
  • Successfully modeled and addressed challenges in square shaft-hole assembly, a complex scenario.

Conclusions:

  • Multi-agent reinforcement learning offers a promising solution for improving robotic arm assembly tasks.
  • The DMDDPG-based approach enhances the efficiency and reliability of robotic assembly, particularly in intricate tasks like square shaft-hole insertion.
  • The developed simulation framework validates the effectiveness of the MARL strategy for real-world robotic assembly applications.