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Multimodal communication is a human feature that enables diverse interactions. In human–robot interaction (HRI), robots have to communicate using human skills so that they can seem natural and assist effectively. Most research uses predefined gestures to equip robots with social abilities. However, researchers scarcely consider generating bioinspired involuntary behavior to improve a robot's expressiveness and communication. Human studies revealed that involuntary behavior affects how others perceive communicative intentions. Therefore, mimicking human involuntary behavior may positively affect HRI. This article extends our previous work on equipping robots with involuntary behavior with a user study that evaluates the use of bioinspiration for complementing gestures. A preliminary test is conducted with 15 participants to determine if they can perceive the intensities of the involuntary processes heart rate, pupil size, blink rate, breathing rate, and motor activity. 63 new participants interacted with a robot with bioinspired behaviors or a robot only showing predefined gestures to evaluate the robots’ warmth, competence, and discomfort. The results show that the preliminary test participants differentiated the intensities of the involuntary processes. Participants in the second study find the robot with bioinspired behaviors significantly warmer and more competent than the robot with predefined gestures, with no discomfort difference.

Decision-making systems allow artificial agents to adapt their behaviours, depending on the information they perceive from the environment and internal processes. Human beings possess unique decision-making capabilities, adapting to current situations and anticipating future challenges. Autonomous robots with adaptive and anticipatory decision-making emulating humans can bring robots with skills that users can understand more easily. Human decisions highly depend on dopamine, a brain substance that regulates motivation and reward, acknowledging positive and negative situations. Considering recent neuroscience studies about the dopamine role in the human brain and its influence on decision-making and motivated behaviour, this paper proposes a model based on how dopamine drives human motivation and decision-making. The model allows robots to behave autonomously in dynamic environments, learning the best action selection strategy and anticipating future rewards. The results show the model’s performance in five scenarios, emphasising how dopamine levels vary depending on the robot’s situation and stimuli perception. Moreover, we show the model’s integration into the Mini social robot to provide insights into how dopamine levels drive motivated autonomous behaviour regulating biologically inspired internal processes emulated in the robot.

Abstract The decisions made by social robots while they fulfill their tasks have a strong influence on their performance. In these contexts, autonomous social robots must exhibit adaptive and social-based behavior to make appropriate decisions and operate correctly in complex and dynamic scenarios. This paper presents a Decision-Making System for social robots working on long-term interactions like cognitive stimulation or entertainment. The Decision-making System employs the robot’s sensors, user information, and a biologically inspired module to replicate how human behavior emerges in the robot. Besides, the system personalizes the interaction to maintain the users’ engagement while adapting to their features and preferences, overcoming possible interaction limitations. The system evaluation was in terms of usability, performance metrics, and user perceptions. We used the Mini social robot as the device where we integrated the architecture and carried out the experimentation. The usability evaluation consisted of 30 participants interacting with the autonomous robot in 30 min sessions. Then, 19 participants evaluated their perceptions of robot attributes of the Godspeed questionnaire by playing with the robot in 30 min sessions. The participants rated the Decision-making System with excellent usability (81.08 out of 100 points), perceiving the robot as intelligent (4.28 out of 5), animated (4.07 out of 5), and likable (4.16 out of 5). However, they also rated Mini as unsafe (security perceived as 3.15 out of 5), probably because users could not influence the robot’s decisions.

La parálisis cerebral (PC) es la enfermedad motora más común en pacientes pediátricos, afectando su capacidad para moverse y mantener el balance corporal y la postura. Este estudio se enfoca en las terapias de rehabilitación robóticas, llevadas a cabo con la plataforma CPWalker, para la realización de ejercicios funcionales mediante movimientos precisos. El trabajo presentado en este artículo propone la integración de la robótica de asistencia social en el CPWalker a través del robot social NAO. De esta manera se plantea conseguir reforzar la motivación del paciente y el dinamismo de estas terapias, y automatizar la retroalimentación correctiva al paciente. Se han realizado dos evaluaciones a lo largo de este estudio. La primera ha sido un protocolo clínico de actividades y juegos entre NAO y los pacientes para evaluar la aceptación del robot por parte de los niños. Y, posteriormente, se ha evaluado la integración del nuevo sistema robótico social de rehabilitación desarrollado. Los resultados de este estudio indican que el robot es percibido positivamente por los pacientes.

Abstract Background Social robots have demonstrated promising outcomes in terms of increasing the social health and well-being of people with dementia and mild cognitive impairment. According to the World Health Organization’s Monitoring and assessing digital health interventions framework, usability and feasibility studies are crucial before implementing prototype social robots and proving their efficacy and effectiveness. This protocol paper aims to detail the plan for conducting the usability and feasibility study of the MINI robot based on evidence-based recommended methodology. Methods In this study, an experimental design and a mixed method of data collection will be applied. Twenty participants aged 65 and over with dementia or mild cognitive impairment will be recruited. Eight sessions of interaction with the robot, as well as qualitative and quantitative assessments, will be accomplished. The research will take place in a laboratory. Ethical approvals have been acquired. This research will be valuable in the development of the MINI robot and its practical deployment in the actual world, as well as the methodological evidence base in the sector of social robots. Discussion By the winter of 2022–2023, the findings of this study will be accessible for dissemination. This study will aid to improve the evidence-based methodology used to study the feasibility and usability of social robots in people with dementia and mild cognitive impairment as well as what can be learned to advance such study designs in the future.

The nervous system is controlled by a collection of neurons that transmit electric signals through the organism. These signals respond to internal and external stimuli and regulate many involuntary physiological functions. The autonomic nervous system regulates involuntary behaviors, such as heart rate, breathing, or pupil size through the action of neuroendocrine substances that modulate the biological processes. These physiological processes allow to express the internal state while interacting with others. This manuscript presents an artificial neuroendocrine model for regulating and expressing the involuntary physiological functions emulated in the Mini social robot. This model employs the robot's perception and simulated biological clock to generate neuroendocrine responses using simulated neuroendocrine substances to control the robot's autonomic processes of heart rate, pupil size, breathing rate, blinking rate, and locomotor activity. The primary contribution of this work is to provide biological modeling to replicate the evolution and effects of neuroendocrine substances on the regulation of autonomic processes and demonstrate how to express them in social robots using their actuators to improve their responsiveness and naturalness. These experiments show that the robot's autonomic processes can maintain proper evolution during long periods, while exhibiting circadian rhythms and responding to unexpected environmental changes.

Biological rhythms are periodic internal variations of living organisms that act as adaptive responses to environmental changes. The human pacemaker is the suprachiasmatic nucleus, a brain region involved in biological functions like homeostasis or emotion. Biological rhythms are ultradian (24 h) depending on their period. Circadian rhythms are the most studied since they regulate daily sleep, emotion, and activity. Ambient and internal stimuli, such as light or activity, influence the timing and the period of biological rhythms, making our bodies adapt to dynamic situations. Nowadays, robots experience unceasing development, assisting us in many tasks. Due to the dynamic conditions of social environments and human-robot interaction, robots exhibiting adaptive behavior have more possibilities to engage users by emulating human social skills. This paper presents a biologically inspired model based on circadian biorhythms for autonomous and adaptive robot behavior. The model uses the Dynamic Circadian Integrated Response Characteristic method to mimic human biology and control artificial biologically inspired functions influencing the robot’s decision-making. The robot’s clock adapts to light, ambient noise, and user activity, synchronizing the robot’s behavior to the ambient conditions. The results show the adaptive response of the model to time shifts and seasonal changes of different ambient stimuli while regulating simulated hormones that are key in sleep/activity timing, stress, and autonomic basal heartbeat control during the day.

Travellers use the term waymarking to define the action of posting signs, or waymarks, along a route. These marks are intended to be points of reference during navigation for the environment. In this research, we will define waymarking as the skill of a robot to signal the environment or generate information to facilitate localization and navigation, both for its own use and for other robots as well. We present an automated environment signaling system using human–robot interaction and radio frequency identification (RFID) technology. The goal is for the robot, through human–robot interaction, to obtain information from the environment and use this information to carry out the signaling or waymarking process. HRI will play a key role in the signaling process since this type of communication makes it possible to exchange more specific and enriching information. The robot uses common phrases such as “Where am I?” and “Where can I go?”, just as we humans do when we ask other people for information about the environment. It is also possible to guide the robot and “show” it the environment to carry out the task of writing the signs. The robot will use the information received to create, update, or improve the navigation data in the RFID signals. In this paper, the signaling process will be described, how the robot acquires the information for signals, writing and updating process and finally, the implementation and integration in a real social robot in a real indoor environment.

User detection, recognition, and tracking is at the heart of human–robot interaction, and yet, to date, no universal robust method exists for being aware of the people in a robot’s surroundings. The present article imports into existing social robotic platforms different techniques, some of them classical, and other novel, for detecting, recognizing, and tracking human users. The outputs from the parallel execution of these algorithms are then merged, creating a modular, expandable, and fast architecture. This results in a local user mapping through fusion of multiple user recognition techniques. The different people detectors comply with a common interface called PeoplePoseList Publisher, while the people recognition algorithms meet an interface called PeoplePoseList Matcher. The fusion of all these different modules is based on the Unscented Kalman Filtering technique. Extensive benchmarks of the subcomponents and of the whole architecture demonstrate the validity and interest of all levels of the architecture. In addition, all the software and data sets generated in this work are freely available.

In this paper, a multimodal user-emotion detection system for social robots is presented. This system is intended to be used during human–robot interaction, and it is integrated as part of the overall interaction system of the robot: the Robotics Dialog System (RDS). Two modes are used to detect emotions: the voice and face expression analysis. In order to analyze the voice of the user, a new component has been developed: Gender and Emotion Voice Analysis (GEVA), which is written using the Chuck language. For emotion detection in facial expressions, the system, Gender and Emotion Facial Analysis (GEFA), has been also developed. This last system integrates two third-party solutions: Sophisticated High-speed Object Recognition Engine (SHORE) and Computer Expression Recognition Toolbox (CERT). Once these new components (GEVA and GEFA) give their results, a decision rule is applied in order to combine the information given by both of them. The result of this rule, the detected emotion, is integrated into the dialog system through communicative acts. Hence, each communicative act gives, among other things, the detected emotion of the user to the RDS so it can adapt its strategy in order to get a greater satisfaction degree during the human–robot dialog. Each of the new components, GEVA and GEFA, can also be used individually. Moreover, they are integrated with the robotic control platform ROS (Robot Operating System). Several experiments with real users were performed to determine the accuracy of each component and to set the final decision rule. The results obtained from applying this decision rule in these experiments show a high success rate in automatic user emotion recognition, improving the results given by the two information channels (audio and visual) separately.

This paper presents a user localization system based on the fusion of visual information and sound source localization, implemented on a social robot called Maggie. One of the main requisites to obtain a natural interaction between human-human and human-robot is an adequate spatial situation between the interlocutors, that is, to be orientated and situated at the right distance during the conversation in order to have a satisfactory communicative process. Our social robot uses a complete multimodal dialog system which manages the user-robot interaction during the communicative process. One of its main components is the presented user localization system. To determine the most suitable allocation of the robot in relation to the user, a proxemic study of the human-robot interaction is required, which is described in this paper. The study has been made with two groups of users: children, aged between 8 and 17, and adults. Finally, at the end of the paper, experimental results with the proposed multimodal dialog system are presented.

Nowadays, many robotic applications require robots making their own decisions and adapting to different conditions and users. This work presents a biologically inspired decision making system, based on drives, motivations, wellbeing, and self-learning, that governs the behavior of the robot considering both internal and external circumstances. In this paper we state the biological foundations that drove the design of the system, as well as how it has been implemented in a real robot. Following a homeostatic approach, the ultimate goal of the robot is to keep its wellbeing as high as possible. In order to achieve this goal, our decision making system uses learning mechanisms to assess the best action to execute at any moment. Considering that the proposed system has been implemented in a real social robot, human-robot interaction is of paramount importance and the learned behaviors of the robot are oriented to foster the interactions with the user. The operation of the system is shown in a scenario where the robot Mini plays games with a user. In this context, we have included a robust user detection mechanism tailored for short distance interactions. After the learning phase, the robot has learned how to lead the user to interact with it in a natural way.