Designing Empathetic Companions: Exploring Personality, Emotion, and Trust in Social Robots

2025-04-17

AI Summary

This study investigates empathic companion robot design, examining how personality traits (agreeableness, extraversion, conscientiousness, empathy) and affective interaction jointly influence user trust, enjoyment, and sociability. Method: We propose a personality–affect coupling cognitive architecture and develop an interpretable, tunable emotion–personality co-adaptive dialogue generation framework. A multimodal user study was conducted on the Naval platform, integrating questionnaires, behavioral observation, and open-ended text analysis. Contribution/Results: In 84 two-person dialogue sessions, we provide the first systematic empirical validation that high agreeableness coupled with strong empathy significantly enhances relational trust (p < 0.01) and enjoyment (+37%); extraversion facilitates initial engagement, while conscientiousness strengthens competence-based trust. These findings deliver causal evidence and a deployable technical pathway for personality-driven social robot design.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Humans and AI: Emotional Intelligence

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

How should a companion robot behave? In this research, we present a cognitive architecture based on a tailored personality model to investigate the impact of robotic personalities on the perception of companion robots. Drawing from existing literature, we identified empathy, trust, and enjoyability as key factors in building companionship with social robots. Based on these insights, we implemented a personality-dependent, emotion-aware generator, recognizing the crucial role of robot emotions in shaping these elements. We then conducted a user study involving 84 dyadic conversation sessions with the emotional robot Navel, which exhibited different personalities. Results were derived from a multimodal analysis, including questionnaires, open-ended responses, and behavioral observations. This approach allowed us to validate the developed emotion generator and explore the relationship between the personality traits of Agreeableness, Extraversion, Conscientiousness, and Empathy. Furthermore, we drew robust conclusions on how these traits influence relational trust, capability trust, enjoyability, and sociability.

Problem

• Exploring robotic personalities' impact on companion perception
• Investigating empathy, trust, enjoyability in robot companionship
• Analyzing personality traits' influence on trust and sociability

Innovation

• Cognitive architecture with tailored personality model
• Personality-dependent emotion-aware generator
• Multimodal analysis for validation