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.

Affective feedback from social robots is a useful technique for communicating to people whether they are interacting “well” with the robot or not. However, some users, such as people with physical or cognitive difficulties, may not be able to interact in all the desired ways. In these cases, affective feedback from the robot could be excessively negative—an “unhappy” robot, leading to an unrewarding experience for the user. This article presents a motivation-based architecture for an autonomous multimodal social robot, that incorporates an affective feedback mechanism which generates an affective state by combining the internal needs of the robot and the social interaction quality. The balance between these two factors can dynamically change, allowing the robot to adapt its affective feedback to the user's interaction style and capabilities. We have implemented this architecture in a simulation and in a MiRo social robot, and report experiments examining the behavior of the system in interactions with different experimental user profiles. The results show that the adaptive mechanism allows the robot to change its affective feedback to give more positive encouragement to users than in non-adaptive cases.

Socially assistive robots in healthcare have reported positive results in recent years, for example, in reducing the impact of mild cognitive impairment in older adults. The lack of a qualified workforce and the increase in the older adult population in developed countries have encouraged designers to develop socially assistive robots that operate autonomously by bringing in cognitive and decision-making methods to facilitate the caregivers’ tasks, select the most appropriate activities, and personalize the interaction. This paper presents the development of a cognitive human-inspired decision-making system for autonomous social assistive robots managing the personalized selection of exercises in cognitive stimulation and providing affective support to their users. The decision-making system receives inputs from the robot’s perceptions, user information stored in the robot’s memory, events in an agenda, and information from a bio-inspired module. These inputs generate autonomous decisions that drive the robot’s behavior depending on each situation. We show the system’s capacity, integrated into our Mini social robot, to adapt the interaction, select tailored exercises based on the user’s features, and execute exercises previously programmed by a caregiver to alleviate cognitive deterioration and accompany older people. Besides, the system generates a natural robot behavior based on biologically inspired methods to personalize activities, engage the user, and increase the number of robot services.

Social robots coexist with humans in situations where they have to exhibit proper communication skills. Since users may have different features and communicative procedures, personalizing human–robot interactions is essential for the success of these interactions. This manuscript presents Active Learning based on computer vision and human–robot interaction for user recognition and profiling to personalize robot behavior. The system identifies people using Intel-face-detection-retail-004 and FaceNet for face recognition and obtains users’ information through interaction. The system aims to improve human–robot interaction by (i) using online learning to allow the robot to identify the users and (ii) retrieving users’ information to fill out their profiles and adapt the robot’s behavior. Since user information is necessary for adapting the robot for each interaction, we hypothesized that users would consider creating their profile by interacting with the robot more entertaining and easier than taking a survey. We validated our hypothesis with three scenarios: the participants completed their profiles using an online survey, by interacting with a dull robot, or with a cheerful robot. The results show that participants gave the cheerful robot a higher usability score (82.14/100 points), and they were more entertained while creating their profiles with the cheerful robot than in the other scenarios. Statistically significant differences in the usability were found between the scenarios using the robot and the scenario that involved the online survey. Finally, we show two scenarios in which the robot interacts with a known user and an unknown user to demonstrate how it adapts to the situation.

Adapting to dynamic environments is essential for artificial agents, especially those aiming to communicate with people interactively. In this context, a social robot that adapts its behaviour to different users and proactively suggests their favourite activities may produce a more successful interaction. In this work, we describe how the autonomous decision-making system embedded in our social robot Mini can produce a personalised interactive communication experience by considering the preferences of the user the robot interacts with. We compared the performance of Top Label as Class and Ranking by Pairwise Comparison, two promising algorithms in the area, to find the one that best predicts the user preferences. Although both algorithms provide robust results in preference prediction, we decided to integrate Ranking by Pairwise Comparison since it provides better estimations. The method proposed in this contribution allows the autonomous decision-making system of the robot to work on different modes, balancing activity exploration with the selection of the favourite entertaining activities. The operation of the preference learning system is shown in three real case studies where the decision-making system works differently depending on the user the robot is facing. Then, we conducted a human-robot interaction experiment to investigate whether the robot users perceive the personalised selection of activities more appropriate than selecting the activities at random. The results show how the study participants found the personalised activity selection more appropriate, improving their likeability towards the robot and how intelligent they perceive the system. query Please check the edit made in the article title.

The external aspects of a robot affect how people behave and perceive it while interacting. In this paper, we study the importance of the mouth displayed by a social robot and explore how different designs of an artificial LED-based mouths alter the participants' judgments of a robot's attributes and their attention to the robot's message. We evaluated participants' judgments of a speaking robot under four conditions: 1) without a mouth; 2) with a static smile; 3) with a vibrating, wave-shaped mouth; and 4) with a moving, human-like mouth. A total of 79 participants evaluated their perceptions of an on-video robot showing one of the four conditions. The results show that the presence of a mouth, as well as its design, alters the perception of the robot. In particular, the presence of a mouth makes the robot to be perceived more lifelike and less sad. The human-like mouth was the one participants liked the most and, along with the smile, they were the friendliest ones. On the contrary, participants rated the mouthless robot and the one with the wave-like mouth as the most dangerous ones.

In this study, an autonomous social robot is living in a laboratory where it can interact with several items (people included). Its goal is to learn by itself the proper behaviors in order to maintain its well-being at as high a quality as possible. Several experiments have been conducted to test the performance of the system. The Object Q-Learning algorithm has been implemented in the robot as the learning algorithm. This algorithm is a variation of the traditional Q-Learning because it considers a reduced state space and collateral effects. The comparison of the performance of both algorithms is shown in the first part of the experiments. Moreover, two mechanisms intended to reduce the learning session durations have been included: Well-Balanced Exploration and Amplified Reward. Their advantages are justified in the results obtained in the second part of the experiments. Finally, the behaviors learned by our robot are analyzed. The resulting behaviors have not been preprogrammed. In fact, they have been learned by real interaction in the real world and are related to the motivations of the robot. These are natural behaviors in the sense that they can be easily understood by humans observing the robot.

Due to the anticipated future, extensive use of robots, human beings will probably share common spaces with them. The relationships between robots and humans will be conducted at close distances. Predicting people's future positions helps robots understand human behavior and react safely and naturally. In this paper, we propose a method for predicting people's positions in crossing behaviors, i.e. different trajectories people follow when they are crossing each other. We conducted a field experiment to gather various crossing behaviors of pedestrians in a shopping mall environment and analyzed them by focusing on “hot areas” spaces where people modify their trajectories for crossing. We clustered typical crossing behaviors in hot areas and modeled them using Hidden Markov Models for predictions. Our algorithm more accurately predicts the future positions of pedestrians by considering moving direction and speed.