Robotic Emotional Expression Generation Based on Mood Transition and Personality Model

Area of Science:

• Robotics
• Human-Computer Interaction
• Affective Computing

Background:

• Designing robots capable of autonomous emotional interaction is crucial for natural human-robot collaboration.
• Existing approaches often lack nuanced emotional expression and continuous mood adaptation.
• Integrating personality traits into robot emotional models is key for believable interactions.

Purpose of the Study:

• To propose a novel method for designing robot mood transitions for enhanced human-robot interaction.
• To develop a 2-D emotional model integrating robot emotion, mood, and personality.
• To enable robots to generate continuous emotional expressions and respond dynamically to human emotions.

Main Methods:

• A 2-D emotional model was developed, incorporating robot emotion, mood, and personality.
• Robot personality was programmed using factors from the Big Five personality traits.
• A method for fusing basic robotic emotional behaviors was designed to create continuous facial expressions.
• An artificial face simulator and a robotic head were used for implementation and testing.

Main Results:

• The proposed method allows for the programming of robot personality by adjusting Big Five factors.
• Robotic emotional states are manifested through continuous facial expressions.
• Preliminary experiments demonstrated that the mood transition scheme appropriately responds to user emotional changes.
• Questionnaire surveys confirmed the effectiveness of the proposed method in evaluating robotic responses.

Conclusions:

• The developed mood transition design enables robots to exhibit more humanlike emotional interactions.
• The 2-D emotional model effectively integrates personality traits for nuanced emotional expression.
• The system shows promise for creating more engaging and responsive robots in human-robot interaction scenarios.