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  • 1Faculty of Engineering, Tokushima University, Tokushima, Japan.

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Summary
This summary is machine-generated.

We developed an enhanced Emotional Trigger System for the REN-XIN humanoid robot, replacing the slow Word Mover's Distance (WMD) model with a faster Convolutional Neural Network-Long Short-Term Memory (CNN_LSTM) model for real-time emotion expression.

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Area of Science:

  • Robotics and Human-Robot Interaction
  • Artificial Intelligence and Machine Learning
  • Affective Computing

Background:

  • Humanoid robots require sophisticated emotion expression for natural interaction.
  • Previous emotion classification models, like Word Mover's Distance (WMD), suffer from significant time delays.
  • Real-time emotion recognition is crucial for seamless human-robot communication.

Purpose of the Study:

  • To develop an enhanced Emotional Trigger System for the REN-XIN humanoid robot.
  • To address the time delay issue associated with WMD-based emotion classification.
  • To enable automatic and smooth emotion expression in humanoid robots.

Main Methods:

  • Proposed an emotion classification model using Convolutional Neural Network and Long Short-Term Memory (CNN_LSTM) network.
  • Compared the performance of the CNN_LSTM model against WMD, CNN, and LSTM models.
  • Conducted experiments using the Chinese emotional corpus (Ren_CECps) with 1v1 and 4v1 data splits.

Main Results:

  • The CNN_LSTM model achieved classification in 10 milliseconds or less, significantly faster than the WMD model (6-8 seconds).
  • CNN_LSTM obtained the best F1 score (0.35) in the 1v1 experiment.
  • CNN_LSTM demonstrated comparable accuracy to WMD in the 4v1 experiment (F1 scores of 0.366 vs 0.367).

Conclusions:

  • The enhanced CNN_LSTM-based Emotional Trigger System significantly reduces processing time for emotion classification in robots.
  • This advancement enables smoother and more natural human-robot interaction through real-time emotion expression.
  • The proposed system offers a viable alternative to slower methods for real-time robotic emotion expression.