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Multi-Agent Deep Reinforcement Learning for Collision-Free Posture Control of Multi-Manipulators in Shared

Hoyeon Lee1, Chenglong Luo1, Hoeryong Jung1

  • 1Department of Mechanical Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a multi-agent deep reinforcement learning (MADRL) framework for collision-free control of multiple robotic arms. The method enhances coordination and reduces task time in shared workspaces.

Keywords:
centralized training and decentralized executioncollision avoidancemotion planningmulti-agent deep reinforcement learningmulti-manipulator systemsshared workspace environment

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Collision-free posture control for multi-manipulator systems in shared workspaces is complex due to high degrees of freedom and inter-manipulator interactions.
  • Traditional motion planning methods lack scalability and efficiency for these demanding applications.
  • Learning-based approaches are needed to address these challenges.

Purpose of the Study:

  • To present a multi-agent deep reinforcement learning (MADRL) framework for real-time collision-free posture control of multiple manipulators.
  • To develop an efficient state representation for cooperative collision avoidance.
  • To enable scalable training and decentralized execution for real-time trajectory planning.

Main Methods:

  • A multi-agent deep reinforcement learning (MADRL) framework is proposed.
  • A line-segment representation of manipulator links is used for efficient interlink distance computation.
  • A centralized training with decentralized execution (CTDE) paradigm is employed, leveraging global state during training and local observations during execution.

Main Results:

  • The proposed method demonstrates faster learning convergence and superior computational efficiency compared to conventional state representations.
  • In pick-and-place tasks, collaborative control reduced task completion time by over 50% versus single-manipulator operation.
  • High success rates (>83%) were maintained under dense workspace conditions.

Conclusions:

  • The framework provides a scalable solution for real-time, collision-free multi-manipulator control in dense industrial environments.
  • The integration of efficient state representation and scalable training paradigms addresses key coordination challenges.
  • The validated approach offers a principled foundation for advanced robotic coordination.