Robots are starting to be used in tasks involving human–robot interactions. For them to be efficient in these tasks, they must be seen as suitable interaction partners. One method to achieve this is to enable them to use proper verbal and non-verbal communication. Selecting non-verbal behaviours that appropriately complement the robot’s verbal messages is complex and requires roboticists to understand how each dimension of communication, as well as their combination, affects how the user perceives the message. In this work, we evaluated the effect of selecting appropriate non-verbal expressions given the robot’s speech on how users perceive the robot and its expressiveness. To do this, we conducted a within-subjects experiment where participants played cards with two robots — one that used a co-speech gesture prediction module for selecting its non-verbal expressions and another that used random expressions. The results showed that using the gestures predicted by our system improves the experience of participants during interactions. Specifically, participants perceived the robot using the co-speech gesture prediction module as having a higher level of agency, and as having a more coherent expressiveness. • We conducted a within-subjects that sought to evaluate how an appropriate selection of co-speech gestures has over how users perceive a social robot and its expressiveness. • Participants observed a researcher having a staged interaction with two robots, and then played a card game with them. • The addition of the co-speech gesture prediction module led to a statistically significant improvement in how users perceived the agency of a social robot. • The use of the gesture prediction module led to participants perceiving a higher coherence between the robot’s verbal and non-verbal communication when compared with a robot that uses random, neutral non-verbal gestures. • The gestures of the robot that used the co-speech gesture prediction module were perceived as being more expressive, although the differences were only marginally significant. These differences become significant among users 40 years old or younger.

Social robots are increasingly used in healthcare and education, but technological gaps, fears of human replacement, and moral or social beliefs can limit their acceptance. In collaborative settings, the activities to complete may influence users' willingness to participate, raising the question of how moral and social attitudes shape human-robot interaction. This paper studies the effect of the Social Judgement Theory on social robotics to analyse which factors affect the users' willingness to complete the robot's requests. The methodology classifies the activities requested by the robot into assimilation (activities people typically accept), non-commitment (activities people usually reject) and the contrast (activities some people accept) groups. We conducted a user study with 63 participants interacting with the Mini social robot in a collaborative session where it requested some actions from the user. We analyse whether the kind of activity requested by the robot, its expressiveness, and demographic, moral, social, and robot factors influence the user behaviour. Results show that the Social Judgement Theory can be extended to social robotics since the kind of activity affects the user's willingness to complete it. Besides, the results indicate that an expressive robot convinced users more than a non-expressive robot and that participants who lied about their completed activities were more easily persuaded. We also found that participants with moderate knowledge of robotics completed more activities than those with low knowledge, and individuals with previous experience interacting with Mini were more likely to comply with its requests. However, demographic factors such as age or gender do not seem to influence robot persuasion despite previous studies suggesting they are important in human-robot collaboration.

Social robots are increasingly becoming part of our society. In this sense, such robots’ interaction capabilities depend largely on their ability to detect and respond to stimuli in their environment as humans would. This paper describes a bio-inspired exogenous attention-based perception architecture for social robots. Exogenous attention regulates those involuntary attentional processes driven by the salience of perceived stimuli. The architecture incorporates bio-inspired concepts, such as inhibition to return, focus of attention selection and detecting salient stimuli, that have been validated on a real social robot. We have integrated several multimodal mechanisms for recognising salient visual, auditory, and tactile stimuli. Then, the attention architecture’s performance was evaluated by comparing it to a ground truth gathered from interactions of 50 users, obtaining comparable responses in real-time.