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Real-time emotion generation in human-robot dialogue using large language models.

Chinmaya Mishra1,2, Rinus Verdonschot2, Peter Hagoort2,3

  • 1Furhat Robotics AB, Stockholm, Sweden.

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Summary

Social robots can now express emotions using Large Language Models (LLMs) for better human connections. This study shows LLM-controlled facial expressions improve robot likability and task performance in human-robot interaction.

Keywords:
GPT3HRILLMaffective HRIaffective behavioremotion appraisalemotionssocial robots

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Robot Interaction

Background:

  • Affective behaviors enhance social robot connections and internal state expression.
  • Emotions are crucial for signaling understanding in Human-Robot Interaction (HRI).

Purpose of the Study:

  • To leverage Large Language Models (LLMs) for controlling robot affective behavior.
  • To predict robot emotions in real-time using dialogue history and emotion appraisal.
  • To evaluate the impact of LLM-driven congruent facial expressions on user perception and task performance.

Main Methods:

  • Interpreted emotion appraisal as an Emotion Recognition in Conversation (ERC) task.
  • Utilized GPT-3.5 to predict robot turn emotions from dialogue history.
  • Assessed model performance in a user study comparing congruent, no emotion, and incongruent expressions.

Main Results:

  • LLM reliably generated emotions, which participants could perceive.
  • Congruent, model-driven facial expressions led to robots being perceived as more human-like and emotionally appropriate.
  • Participants showed improved performance in a card sorting game with congruent robot expressions.

Conclusions:

  • LLMs can reliably control robot affective behavior in real-time.
  • Congruent emotional expressions enhance user perception and interaction outcomes.
  • Findings support LLM integration for robots in therapy, companionship, and customer service roles.