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One-shot Learning from Demonstration Approach Toward a Reciprocal Sign Language-based HRI.

Seyed Ramezan Hosseini1, Alireza Taheri1, Minoo Alemi1,2

  • 1Social and Cognitive Robotics Lab, Sharif University of Technology, Tehran, Iran.

International Journal of Social Robotics
|August 16, 2021
PubMed
Summary

This study introduces a new Learning from Demonstration (LfD) architecture for robots to learn sign language. Using one-shot learning, the robot can recognize and imitate new signs after a single demonstration, improving human-robot interaction.

Keywords:
Convolutional Neural Network (CNN)Human–Robot Interaction (HRI)One-shot LearningSign LanguageSocial Robotics

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Current Learning from Demonstration (LfD) architectures lack extensibility for sign language-based human-robot interactions.
  • There is a need for efficient methods to teach robots new signs, especially for specialized sign languages like Iranian Sign Language.

Purpose of the Study:

  • To propose and implement an extensible LfD architecture for teaching Iranian Sign Language signs to a social robot.
  • To leverage one-shot learning and Convolutional Neural Networks for rapid sign recognition and imitation.

Main Methods:

  • Developed an LfD architecture incorporating one-shot learning techniques.
  • Utilized a Convolutional Neural Network for sign recognition and imitation.
  • Trained the system using a data glove for sign demonstrations.

Main Results:

  • Achieved a 70% 4-way accuracy on a small, low-diversity dataset (approx. 500 signs in 16 categories).
  • Demonstrated the potential for increased extensibility of sign vocabulary in human-robot interactions.
  • Showcased promising results for one-shot LfD in social Human-Robot Interaction.

Conclusions:

  • The proposed one-shot LfD architecture enhances the extensibility of sign language-based human-robot interactions.
  • The study highlights the effectiveness of machine learning algorithms, specifically one-shot LfD, in social Human-Robot Interaction.
  • This approach shows significant potential for teaching robots new signs efficiently.