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Updated: Sep 11, 2025

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms
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Robotic Arm Trajectory Planning in Dynamic Environments Based on Self-Optimizing Replay Mechanism.

Pengyao Xu1, Chong Di1, Jiandong Lv2

  • 1Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China.

Sensors (Basel, Switzerland)
|August 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel neural network-based expert-guided triple experience replay mechanism (NETM) to improve deep reinforcement learning for robotic arm trajectory planning in dynamic environments, enhancing accuracy and safety.

Keywords:
dynamic environmentexperience replay mechanismreward function designrobotic manipulation

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Robotic arms in dynamic environments face challenges like real-time changes and uncertainties.
  • Deep reinforcement learning (DRL) for trajectory planning struggles with expert strategy acquisition, low experience utilization, and reward function design.

Purpose of the Study:

  • To address the limitations of DRL in robotic arm trajectory planning.
  • To improve convergence speed and performance in complex dynamic environments.

Main Methods:

  • Designed a neural network-based expert-guided triple experience replay mechanism (NETM).
  • Developed an improved reward function tailored for dynamic environments.
  • Integrated imitation learning with DRL for optimized experience replay.

Main Results:

  • NETM expands limited expert demonstrations and algorithm successes into optimized expert experiences.
  • Experimental results demonstrate accelerated convergence in dynamic scenarios.
  • NETM improved accuracy by over 30% and the safe rate by 2.28% compared to baseline algorithms.

Conclusions:

  • The proposed NETM effectively enhances DRL for robotic arm trajectory planning.
  • The approach significantly improves performance and convergence in dynamic environments.
  • NETM offers a viable solution for real-world robotic applications facing uncertainty.