A Robot Companion with Adaptive Object Preferences and Emotional Responses Enhances Naturalness in Human–Robot Interaction

Autonomous robot companions must engage users to create long-lasting bonds that promote their better and frequent use. Previous studies in the area revealed that a personalised human–robot interaction facilitates such connections, leading people to use robots more frequently, improving how they perceive the robot. This paper presents a biologically inspired system based on reinforcement learning to endow care-dependent robot companions with adaptive preferences for objects and dynamic emotional responses that depend on the interaction context. The system generates and adapts the robot’s preferences towards objects based on user actions, simulated internal needs, and other factors such as the kind of object. We integrate the system into Mini, which is a robot companion that simulates behaviour inspired by the famous Tamagotchi toy to promote human–robot bonding. Mini encourages users to care for it by providing objects that restore its hunger, thirst, and boredom, reacting to the actions taken by users. We conducted a within-subjects user study where participants interacted with two robots: with preferences towards objects and emotional responses, and without them. The results indicate that participants perceived the robot with preferences and emotions as more natural—Animacy, Intelligence, and Agency dimensions—but not more likeable and sociable; however, most explicitly indicated their preferences towards the robot with adaptive preferences and emotions in the posterior analysis.