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.

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.

This paper presents a system in which a robot uses Active Learning (AL) to improve its learning capabilities for pose recognition. We propose a sub-type of Feature Queries, Rank Queries (RQ), in which the user states the relevance of a characteristic of the learning space. In the case of pose learning, these queries refer to the relevance of a single limb for a certain pose. We test the use of RQ with 24 users to learn 3 pointing poses and compare the learning accuracy against a passive learning approach. Our results show that RQ can increase the robot's learning accuracy.

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.

In many occasions people need to go to certain places without having any prior knowledge about the environment. This situation may occur when the place is visited for the first time, or even when there is not any available map to situate us. In those cases, the signs of the environment are essential for achieving the goal. The same situation may happen for an autonomous robot. This kind of robots must be capable of solving this problem in a natural way. In order to do that, they must use the resources present in their environment. This paper presents a RFID-based signage system, which has been developed to guide and give important information to an autonomous robot. The system has been implemented in a real indoor environment and it has been successfully proved in the autonomous and social robot Maggie. At the end of the paper some experimental results, carried out inside our university building, are presented.

Research on dialog systems is a very active area in social robotics. During the last two decades, these systems have evolved from those based only on speech recognition and synthesis to the current and modern systems, which include new components and multimodality. By multimodal dialogue we mean the interchange of information among several interlocutors, not just using their voice as the mean of transmission but also all the available channels such as gestures, facial expressions, touch, sounds, etc. These channels add information to the message to be transmitted in every dialogue turn. The dialogue manager (IDiM) is one of the components of the robotic dialog system (RDS) and is in charge of managing the dialogue flow during the conversational turns. In order to do that, it is necessary to coherently treat the inputs and outputs of information that flow by different communication channels: audio, vision, radio frequency, touch, etc. In our approach, this multichannel input of information is temporarily fused into communicative acts (CAs). Each CA groups the information that flows through the different input channels into the same pack, transmitting a unique message or global idea. Therefore, this temporary fusion of information allows the IDiM to abstract from the channels used during the interaction, focusing only on the message, not on the way it is transmitted. This article presents the whole RDS and the description of how the multimodal fusion of information is made as CAs. Finally, several scenarios where the multimodal dialogue is used are presented.

User simultaneous detection and tracking is an issue at the core of human–robot interaction (HRI). Several methods exist and give good results; many use image processing techniques on images provided by the camera. The increasing presence in mobile robots of range-imaging cameras (such as structured light devices as Microsoft Kinects) allows us to develop image processing on depth maps. In this article, a fast and lightweight algorithm is presented for the detection and tracking of 3D clusters thanks to classic 2D techniques such as edge detection and connected components applied to the depth maps. The recognition of clusters is made using their 2D shape. An algorithm for the compression of depth maps has been specifically developed, allowing the distribution of the whole processing among several computers. The algorithm is then applied to a mobile robot for chasing an object selected by the user. The algorithm is coupled with laser-based tracking to make up for the narrow field of view of the range-imaging camera. The workload created by the method is light enough to enable its use even with processors with limited capabilities. Extensive experimental results are given for verifying the usefulness of the proposed method.

In this paper, a new approach to the generation and the role of artificial emotions in the decision-making process of autonomous agents (physical and virtual) is presented. The proposed decision-making system is biologically inspired and it is based on drives, motivations, and emotions. The agent has certain needs or drives that must be within a certain range, and motivations are understood as what moves the agent to satisfy a drive. Considering that the well-being of the agent is a function of its drives, the goal of the agent is to optimize it. Currently, the implemented artificial emotions are happiness, sadness, and fear. The novelties of our approach are, on one hand, that the generation method and the role of each of the artificial emotions are not defined as a whole, as most authors do. Each artificial emotion is treated separately. On the other hand, in the proposed system it is not mandatory to predefine either the situations that must release any artificial emotion or the actions that must be executed in each case. Both the emotional releaser and the actions can be learned by the agent, as happens on some occasions in nature, based on its own experience. In order to test the decision-making process, it has been implemented on virtual agents (software entities) living in a simple virtual environment. The results presented in this paper correspond to the implementation of the decision-making system on an agent whose main goal is to learn from scratch how to behave in order to maximize its well-being by satisfying its drives or needs. The learning process, as shown by the experiments, produces very natural results. The usefulness of the artificial emotions in the decision-making system is proven by making the same experiments with and without artificial emotions, and then comparing the performance of the agent.

When a reinforcement learning agent executes actions that can cause frequent damage to itself, it can learn, by using Q-learning, that these actions must not be executed again. However, there are other actions that do not cause damage frequently but only once in a while, for example, risky actions such as parachuting. These actions may imply punishment to the agent and, depending on its personality, it would be better to avoid them. Nevertheless, using the standard Q-learning algorithm, the agent is not able to learn to avoid them, because the result of these actions can be positive on average. In this article, an additional mechanism of Q-learning, inspired by the emotion of fear, is introduced in order to deal with those risky actions by considering the worst results. Moreover, there is a daring factor for adjusting the consideration of the risk. This mechanism is implemented on an autonomous agent living in a virtual environment. The results present the performance of the agent with different daring degrees.

This paper presents a framework for multimodal human-robot interaction. The proposed framework is being implemented in a personal robot called Maggie, developed at RoboticsLab of the University Carlos III of Madrid for social interaction research. The control architecture of this personal robot is a hybrid control architecture called AD (Automatic-Deliberative) that incorporates an Emotion Control System (ECS) Maggie’s main goal is to interact establish a peer-to-peer relationship with humans. To achieve this goal, a set of human-robot interaction skills are developed based on the proposed framework. The human-robot interaction skills imply tactile, visual, remote voice and sound modes. The multi-modal fusion and synchronization are also presented in this paper.