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Related Experiment Video

Updated: Jun 29, 2025

Force and Position Control in Humans - The Role of Augmented Feedback
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Motor Interaction Control Based on Muscle Force Model and Depth Reinforcement Strategy.

Hongyan Liu1, Hanwen Zhang1, Junghee Lee1

  • 1Department of Marine Convergence Design Engineering, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea.

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|March 27, 2024
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Summary

This study introduces an advanced human motion control model using muscle force and deep reinforcement learning. The new model significantly improves motion fidelity and adaptive control in complex environments.

Keywords:
deep reinforcementenvironmental adaptionmotor interactionmuscle force modelingstage particle swarm

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Area of Science:

  • Robotics
  • Biomechanics
  • Artificial Intelligence

Background:

  • Current motion interaction models lack sufficient fidelity and adaptability to complex environments.
  • Existing models struggle with real-world dynamic and unpredictable scenarios.

Purpose of the Study:

  • To develop a human motion control model with enhanced fidelity and self-adaptation.
  • To improve autonomous decision-making and adaptive control in complex environments.

Main Methods:

  • Constructed a human motion control model integrating a muscle force model and a stage particle swarm.
  • Utilized a deep deterministic gradient strategy algorithm for motion interaction control.
  • Employed deep reinforcement learning for adaptive control strategies.

Main Results:

  • Achieved high joint trajectory correlation (up to 0.90) and muscle activity correlation (up to 0.84).
  • Demonstrated effective autonomous decision-making and adaptive control in mixed-obstacle environments.
  • Outperformed comparative models in walking distance (423 m) after 1.1 × 10^3 training iterations.

Conclusions:

  • The proposed model offers superior motion fidelity and environmental adaptability.
  • This approach provides a theoretical foundation for intelligent motion interaction and control.
  • The model enables autonomous decision-making in complex, dynamic settings.