Using Deep Learning Technology to Realize the Automatic Control Program of Robot Arm Based on Hand Gesture Recognition


  • Shang-Liang Chen Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, Taiwan
  • Li-Wu Huang Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, Taiwan



deep learning, hand gesture recognition, human robot interaction, YOLO


In this study, the robot arm control, computer vision, and deep learning technologies are combined to realize an automatic control program. There are three functional modules in this program, i.e., the hand gesture recognition module, the robot arm control module, and the communication module. The hand gesture recognition module records the user’s hand gesture images to recognize the gestures’ features using the YOLOv4 algorithm. The recognition results are transmitted to the robot arm control module by the communication module. Finally, the received hand gesture commands are analyzed and executed by the robot arm control module. With the proposed program, engineers can interact with the robot arm through hand gestures, teach the robot arm to record the trajectory by simple hand movements, and call different scripts to satisfy robot motion requirements in the actual production environment.


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How to Cite

S.-L. . Chen and L.-W. Huang, “Using Deep Learning Technology to Realize the Automatic Control Program of Robot Arm Based on Hand Gesture Recognition”, Int. j. eng. technol. innov., vol. 11, no. 4, pp. 241–250, Aug. 2021.