Human Perception of Intrinsically Motivated Autonomy in Human-Robot Interaction

A challenge in using robots in human-robot interaction (HRI) is to design behavior that is engaging enough to encourage voluntary, long-term interaction, yet robust to the perturbations induced by human interaction. Here we evaluate if a physical robot that generates its behavior based on its intrinsic motivations could address this challenge. We use an information theoretic quantity - predictive information maximization - as an intrinsic motivation, as simulated experiments showed that this leads to playful, exploratory behavior that is robust to changes to the robot's morphology and to its environment. We present a game-like study design, which allows us to focus on the interplay between the robot and the human participant. In contrast to a study design where participants order or control a robot to do a specific task, the robot and the human participants in our study design explore their behaviors without any specific goals. We conducted a within-subjects study (N=24) where participants interacted with a fully autonomous Sphero BB8 robot with different behavioral regimes: one realizing an adaptive, intrinsically motivated behavior and the other being reactive, but not adaptive. A quantitative analysis of post-interaction questionnaires showed a significantly higher perception of the dimension "Warmth" compared to the baseline behavior. Warmth is considered a primary dimension for social attitude formation in human social cognition. A human perceived as warm (friendly, trustworthy) experiences more positive social interactions. If future work demonstrates that this transfers to human-robot social cognition, then the generic methods presented here could be used to imbue robots with behavior leading to positive perception by and to positive social interaction with humans.