Modulating the Non-Verbal Social Signals of a Humanoid Robot

In this demonstration we present a repertoire of social signals generated by the humanoid robot Pepper in the context of the EU-funded project MuMMER. The aim of this research is to provide the robot with the expressive capabilities required to interact with people in real-world public spaces such as shopping malls-and being able to control the non-verbal behaviour of such a robot is key to engaging with humans in an effective way. We propose an approach to modulating the non-verbal social signals of the robot based on systematically varying the amplitude and speed of the joint motions and gathering user evaluations of the resulting gestures. We anticipate that the humans' perception of the robot behaviour will be influenced by these modulations

The More I Understand it, the Less I Like it: The Relationship Between Understandability and Godspeed Scores for Robotic Gestures

This work investigates the relationship between the perception that people develop about a robot and the understandability of the gestures the latter displays. The experiments have involved 30 human observers that have rated 45 robotic gestures in terms of the Godspeed dimensions. At the same time, the observers have assigned a score to 10 possible interpretations (the same interpretations for all gestures). The results show that there is a statistically significant correlation between the understandability of the gestures - measured through an information theoretic approach - and all Godspeed scores. However, the correlation is positive in some cases (Anthropomorphism, Animacy and Perceived Intelligence), but negative in others (Perceived Safety and Likeability). In other words, higher understandability is not necessarily associated with more positive perceptions

Shaping Gestures to Shape Personalities: The Relationship Between Gesture Parameters, Attributed Personality Traits and Godspeed Scores

This work explores the role of personality as a mediation variable between the observable behaviour of a robot - gestures of different energy and spatial extension in the experiments of this work - and the subjective experience of its users as measure by the Godspeed questionnaire. The results show that, at least for some traits, the Big Five personality traits that the users attribute to a robot are predictive of the Godspeed scores, i.e., of the quality of the interaction the users have with the robot. In other words, robots that are attributed different personality traits tend to be perceived differently in relation to the quality of the interaction

Emerging Artificial Societies Through Learning

The NewTies project is implementing a simulation in which societies of agents are expected to de-velop autonomously as a result of individual, population and social learning. These societies are expected to be able to solve environmental challenges by acting collectively. The challenges are in-tended to be analogous to those faced by early, simple, small-scale human societies. This report on work in progress outlines the major features of the system as it is currently conceived within the project, including the design of the agents, the environment, the mechanism for the evolution of language and the peer-to-peer infrastructure on which the simulation runs.Artificial Societies, Evolution of Language, Decision Trees, Peer-To-Peer Networks, Social Learning

Do We Really Like Robots that Match our Personality? The Case of Big-Five Traits, Godspeed Scores and Robotic Gestures

This work investigates the role of the attraction paradigm — the tendency to associate similarity and attraction in interpersonal relations — in Human-Robot Interaction. The experiment presented here involved 30 human observers who watched and rated 45 robotic gestures in terms of BigFive personality traits and Godspeed scores. The results show that, for 24 of the 30 observers, there was a statistically significant correlation between the Godspeed scores and the perceived similarity between the robot’s personality and their own. However, the association was positive for 15 subjects — meaning that for these there is a similarity-attraction effect — and negative for the other 9 — meaning that for these there is a complementarity-attraction effect. Furthermore, the strength of the effect depends on the particular trait under examination