Adaptive Circadian Rhythms for Autonomous and Biologically Inspired Robot Behavior.

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), circadian (∼24 h), or infradian (>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.

Potential Facilitators of and Barriers to Implementing the MINI Robot in Community-Based Meeting Centers for People With Dementia and Their Carers in the Netherlands and Spain: Explorative Qualitative Study.

BACKGROUND: Social robots, as a form of digital health technologies, are used to support emotional, cognitive, and physical care and have shown promising outcomes in enhancing social well-being in people with dementia (PwD) by boosting emotions, social interactions, and activity participation. OBJECTIVE: The goal is to investigate the attitude of stakeholders and potential facilitators and the barriers to implementing the social robot MINI in community-based meeting centers (MCs) for PwD and carers in the Netherlands and Spain. METHODS: Based on the British Medical Research Council guidance for process evaluation of the implementation of complex interventions and the model for tracing the facilitators of and barriers to the adaptive implementation of innovations in dementia care, an explorative qualitative study was conducted. Following the introduction of the MINI robot, 11 stakeholders were interviewed in 3 MCs in the Netherlands and 1 in Spain, as well as stakeholders in health and welfare organizations in both countries. In addition, 12 adults with dementia participated in focus groups. The data were thematically analyzed and narratively described. RESULTS: Overall, the stakeholder opinion and interest in the MINI robot were positive. The most important (expected) facilitating factors mentioned by stakeholders appeared to be human resources, funding, the impact of the MINI robot on the users and programs of the MCs, characteristics of the innovation, and collaboration with other care and welfare organizations. However, the (expected) barriers mentioned concerned the physical context and functionalities of the MINI robot, the user context, and MC activity policies. CONCLUSIONS: The findings will inform professional stakeholders, such as MC directors and managers, as well as care and welfare organizations, on the practicality of using the MINI robot in MCs. Furthermore, our research will aid MINI robot developers in tailoring its features to PwD's preferences and demands and MC policies, which will contribute to the MINI robot's effective adoption and deployment.

A biologically inspired decision-making system for the autonomous adaptive behavior of social robots.

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.

An adaptive decision-making system supported on user preference predictions for human-robot interactive communication.

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 usability and feasibility validation of the social robot MINI in people with dementia and mild cognitive impairment; a study protocol.

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.

Modelling Multimodal Dialogues for Social Robots Using Communicative Acts.

Social Robots need to communicate in a way that feels natural to humans if they are to effectively bond with the users and provide an engaging interaction. Inline with this natural, effective communication, robots need to perceive and manage multimodal information, both as input and output, and respond accordingly. Consequently, dialogue design is a key factor in creating an engaging multimodal interaction. These dialogues need to be flexible enough to adapt to unforeseen circumstances that arise during the conversation but should also be easy to create, so the development of new applications gets simpler. In this work, we present our approach to dialogue modelling based on basic atomic interaction units called Communicative Acts. They manage basic interactions considering who has the initiative (the robot or the user), and what is his/her intention. The two possible intentions are either ask for information or give information. In addition, because we focus on one-to-one interactions, the initiative can only be taken by the robot or the user. Communicative Acts can be parametrised and combined in a hierarchical manner to fulfil the needs of the robot's applications, and they have been equipped with built-in functionalities that are in charge of low-level communication tasks. These tasks include communication error handling, turn-taking or user disengagement. This system has been integrated in Mini, a social robot that has been created to assist older adults with cognitive impairment. In a case of use, we demonstrate the operation of our system as well as its performance in real human-robot interactions.

A Bio-inspired Motivational Decision Making System for Social Robots Based on the Perception of the User.

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.

Detecting and Classifying Human Touches in a Social Robot Through Acoustic Sensing and Machine Learning.

An important aspect in Human-Robot Interaction is responding to different kinds of touch stimuli. To date, several technologies have been explored to determine how a touch is perceived by a social robot, usually placing a large number of sensors throughout the robot's shell. In this work, we introduce a novel approach, where the audio acquired from contact microphones located in the robot's shell is processed using machine learning techniques to distinguish between different types of touches. The system is able to determine when the robot is touched (touch detection), and to ascertain the kind of touch performed among a set of possibilities: stroke, tap, slap, and tickle (touch classification). This proposal is cost-effective since just a few microphones are able to cover the whole robot's shell since a single microphone is enough to cover each solid part of the robot. Besides, it is easy to install and configure as it just requires a contact surface to attach the microphone to the robot's shell and plug it into the robot's computer. Results show the high accuracy scores in touch gesture recognition. The testing phase revealed that Logistic Model Trees achieved the best performance, with an F-score of 0.81. The dataset was built with information from 25 participants performing a total of 1981 touch gestures.

Augmented Robotics Dialog System for Enhancing Human-Robot Interaction.

Augmented reality, augmented television and second screen are cutting edge technologies that provide end users extra and enhanced information related to certain events in real time. This enriched information helps users better understand such events, at the same time providing a more satisfactory experience. In the present paper, we apply this main idea to human-robot interaction (HRI), to how users and robots interchange information. The ultimate goal of this paper is to improve the quality of HRI, developing a new dialog manager system that incorporates enriched information from the semantic web. This work presents the augmented robotic dialog system (ARDS), which uses natural language understanding mechanisms to provide two features: (i) a non-grammar multimodal input (verbal and/or written) text; and (ii) a contextualization of the information conveyed in the interaction. This contextualization is achieved by information enrichment techniques that link the extracted information from the dialog with extra information about the world available in semantic knowledge bases. This enriched or contextualized information (information enrichment, semantic enhancement or contextualized information are used interchangeably in the rest of this paper) offers many possibilities in terms of HRI. For instance, it can enhance the robot's pro-activeness during a human-robot dialog (the enriched information can be used to propose new topics during the dialog, while ensuring a coherent interaction). Another possibility is to display additional multimedia content related to the enriched information on a visual device. This paper describes the ARDS and shows a proof of concept of its applications.