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LLM-based robot personality simulation and cognitive system.

Jia-Hsun Lo1, Han-Pang Huang2, Jie-Shih Lo3

  • 1Department of Mechanical Engineering, National Taiwan University, Taipei, Taiwan.

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Summary
This summary is machine-generated.

This study introduces a cognitive robot framework using Chat GPT-4 to emulate human personality, enhancing human-robot interaction. The robot, Mobi, demonstrates human-like conversation, social conflict handling, and intent understanding.

Keywords:
Human robot interactionLarge language modelRobot personality and cognitionTheory of mind

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Human-Robot Interaction

Background:

  • Human-robot interaction (HRI) benefits from personality emulation for improved user experience.
  • Existing cognitive frameworks lack robust personality simulation capabilities.
  • Chat GPT-4 offers potential for advanced conversational AI in robotics.

Purpose of the Study:

  • To develop and validate a cognitive robot framework emulating specific personality traits.
  • To integrate emotion, motivation, memory, and attention into a robot's cognitive architecture.
  • To assess the human-likeness and effectiveness of the simulated personality.

Main Methods:

  • Utilized state-space realization for personality trait emulation.
  • Implemented document embedding for long-term memory encoding and retrieval.
  • Generated emotions based on future event predictions.
  • Employed International Personality Item Pool - Neuroticism, Extraversion and Openness (IPIP-NEO) and Big Five tests for validation.
  • Applied Kelly's role construct repertory and Cattell's 16PF for personality modeling.
  • Evaluated Theory of Mind (ToM) using the ToMi dataset.

Main Results:

  • The cognitive robot framework successfully emulated personality traits, including emotion and memory.
  • Personality simulations demonstrated construct validity when compared to human subjects.
  • The robot, Mobi, exhibited enhanced Theory of Mind capabilities, outperforming other agents in second-order belief tasks.
  • Mobi showed proficiency in personalized conversation, social conflict resolution, and user intent understanding.

Conclusions:

  • The proposed cognitive robot personality model achieves a high degree of human likeness.
  • The framework enables robots to engage in flexible, intentional, and context-aware conversations.
  • This research advances the development of more sophisticated and relatable conversational agents in HRI.