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

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.

EmoACT: a Framework to Embed Emotions into Artificial Agents Based on Affect Control Theory

As robots and artificial agents become increasingly integrated into daily life, enhancing their ability to interact with humans is essential. Emotions, which play a crucial role in human interactions, can improve the naturalness and transparency of human-robot interactions (HRI) when embodied in artificial agents. This study aims to employ Affect Control Theory (ACT), a psychological model of emotions deeply rooted in interaction, for the generation of synthetic emotions. A platform-agnostic framework inspired by ACT was developed and implemented in a humanoid robot to assess its impact on human perception. Results show that the frequency of emotional displays impacts how users perceive the robot. Moreover, appropriate emotional expressions seem to enhance the robot's perceived emotional and cognitive agency. The findings suggest that ACT can be successfully employed to embed synthetic emotions into robots, resulting in effective human-robot interactions, where the robot is perceived more as a social agent than merely a machine.

Toward a Universal Concept of Artificial Personality: Implementing Robotic Personality in a Kinova Arm

The fundamental role of personality in shaping interactions is increasingly being exploited in robotics. A carefully designed robotic personality has been shown to improve several key aspects of Human-Robot Interaction (HRI). However, the fragmentation and rigidity of existing approaches reveal even greater challenges when applied to non-humanoid robots. On one hand, the state of the art is very dispersed; on the other hand, Industry 4.0 is moving towards a future where humans and industrial robots are going to coexist. In this context, the proper design of a robotic personality can lead to more successful interactions. This research takes a first step in that direction by integrating a comprehensive cognitive architecture built upon the definition of robotic personality - validated on humanoid robots - into a robotic Kinova Jaco2 arm. The robot personality is defined through the cognitive architecture as a vector in the three-dimensional space encompassing Conscientiousness, Extroversion, and Agreeableness, affecting how actions are executed, the action selection process, and the internal reaction to environmental stimuli. Our main objective is to determine whether users perceive distinct personalities in the robot, regardless of its shape, and to understand the role language plays in shaping these perceptions. To achieve this, we conducted a user study comprising 144 sessions of a collaborative game between a Kinova Jaco2 arm and participants, where the robot's behavior was influenced by its assigned personality. Furthermore, we compared two conditions: in the first, the robot communicated solely through gestures and action choices, while in the second, it also utilized verbal interaction.