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Research on Deep Learning-Based Human-Robot Static/Dynamic Gesture-Driven Control Framework.

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  • 1School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China.

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|December 11, 2025
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Summary
This summary is machine-generated.

This study introduces a deep learning method for robot control using static and dynamic hand gestures. The approach achieves high accuracy in gesture recognition and successful task completion for object manipulation, enhancing human-robot collaboration.

Keywords:
deep learningdynamic and static gesturegesture-driven control frameworkhuman-robot collaborationthree-dimensional Convolutional Neural Networks

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Human-robot interaction (HRI) is crucial for collaborative tasks.
  • Gesture-driven control offers a natural interface for robots.
  • Robustness in varying conditions is essential for practical HRI.

Purpose of the Study:

  • To develop a deep learning-based system for gesture-driven robot control.
  • To enable robots to perform object-grasping and delivery tasks using static and dynamic hand gestures.
  • To evaluate the system's performance and robustness in diverse lighting conditions.

Main Methods:

  • Utilized 2D Convolutional Neural Networks (2D-CNNs) for static gesture recognition.
  • Employed a hybrid 3D Convolutional Neural Networks (3D-CNNs) and Long Short-Term Memory (3D-CNN+LSTM) network for dynamic gesture recognition.
  • Integrated MediaPipe for hand feature extraction and a depth camera for 3D pose estimation.

Main Results:

  • Achieved validation accuracies of 95.38% for static and 93.18% for dynamic gestures.
  • Demonstrated average task success rates of no less than 96.88% (static) and 94.63% (dynamic) across 100 trials per participant.
  • Maintained task completion times consistently within 20 seconds under natural, low, and strong light conditions.

Conclusions:

  • The proposed deep learning approach enables robust vision-based robotic control using natural hand gestures.
  • The system effectively facilitates object-grasping and delivery tasks, showing high accuracy and reliability.
  • This research holds significant promise for advancing human-robot collaboration applications.