Companion Robots Research Articles

Game of Life With Your Companion Robot: Exploring the Sustainable Future for Long-Term Human-Robot Interaction

Reducing electronic waste is one of the key topics in Sustainable Interaction Design. However, research regarding the sustainable future of the long-term use of robots is limited. Our study employs a game-based workshop to investigate the factors influencing potential users’ sustainability choices in long-term human-robot interactions. We developed a board game called “Game of Life with Your Companion Robot” to help participants situate themselves in the context of cohabiting with companion robots of their choice. Through five workshops with seventeen participants, we explore (a) the factors mentioned by participants that influence their sustainability choices in long-term human-robot interactions, and (b) the connections between how participants frame their companion robots and their sustainability choices. We use four sustainable criteria to evaluate participants’ choices. Our findings show that different framings of robots can result in different sustainable outcomes.

The Social Awareness Index as a Tool to Determine the Role of a Companion Robot in the Lives of People with Reduced Mobility

Robots are becoming an increasingly popular element of the developing technological reality. A companion (social) robot can be defined as an autonomous machine that recognises other robots and people and engages in social interactions. Robots of this type are designed to serve people, so they often act as guides, assistants, companions, guardians, teachers and domestic pets. This study aims to define and operationalise the components and synthetically measure the social awareness (index) concerning the usability of a companion robot in everyday life. A comprehensive new research tool was developed as part of the study in the form of an original standardised interview questionnaire by the CATI/CAWI technique. Empirical studies show that an increase in social acceptance requires that potential consumers be provided with the necessary information to know the technologies better and improve their digital skills related to social robots.

The Role of Animal Assisted Therapy in the Rehabilitation of Mental Health Disorders: A Systematic Literature Review

Mental health disorders like depression, anxiety, post-traumatic stress disorder (PTSD), and schizophrenia significantly reduce the daily functioning and quality of life of individuals. Traditional treatments often fall short, thus opening interest in complementary therapies like animal assisted therapy (AAT) which encourages the human-animal bond and gives emotional and psychological support. This review evaluated the efficacy of AAT in treating mental health disorders, and understanding its mechanisms and benefits. A comprehensive literature review was conducted (using databases such as PubMed, Google Scholar, and ResearchGate) focused on peer-reviewed articles, systematic reviews and meta-analyses published in the last 2 decades. Studies indicated that AAT improved mental health outcomes for people with depression, anxiety, PTSD, and schizophrenia. Interaction with therapy animals reduced cortisol levels, increased oxytocin, lowered blood pressure, and increased social engagement and emotional regulation. Benefits included reduced symptoms of depression, anxiety, PTSD, and improved social functioning and quality of life. AAT offered a complementary treatment for mental health disorders, providing emotional comfort, improving mood, and serving as a nonpharmacological option for individuals. However, challenges such as individual preferences, allergies, ethical concerns for therapy animals, and logistical issues must be addressed. Future research should focus on the long-term effects and mechanisms involved to optimize the application of AAT in the clinical setting, and alternatives like robotic companion pets could also be explored.

Canadian perspectives on loneliness; digital communication as meaningful connection.

This perspective piece considers loneliness and its relationship to communication, connection, and technology by reviewing the origins and lessons from the field. It begins with a search for an operational definition, then examines the differences between experiential (situational/isolation-based) and existential (continuous, non-situational) loneliness. Technology is addressed as both a hindrance and a tool for alleviating loneliness with the example of companion robots as an emerging technology for loneliness mitigation. Cultural differences in experiences of loneliness, specifically as a public health issue, are in the context of the COVID-19 pandemic in Canada. Concepts of social and emotional loneliness, individualism and collectivism, socioeconomic status, vulnerability, and lived experience are explored and provide an emphasis on 'meaningful connection' in the study of loneliness.

Development of Dual-Arm Human Companion Robots That Can Dance.

As gestures play an important role in human communication, there have been a number of service robots equipped with a pair of human-like arms for gesture-based human-robot interactions. However, the arms of most human companion robots are limited to slow and simple gestures due to the low maximum velocity of the arm actuators. In this work, we present the JF-2 robot, a mobile home service robot equipped with a pair of torque-controlled anthropomorphic arms. Thanks to the low inertia design of the arm, responsive Quasi-Direct Drive (QDD) actuators, and active compliant control of the joints, the robot can replicate fast human dance motions while being safe in the environment. In addition to the JF-2 robot, we also present the JF-mini robot, a scaled-down, low-cost version of the JF-2 robot mainly targeted for commercial use at kindergarten and childcare facilities. The suggested system is validated by performing three experiments, a safety test, teaching children how to dance along to the music, and bringing a requested item to a human subject.

LQR-based control strategy for improving human–robot companionship and natural obstacle avoidance

In the dynamic and unstructured environment of human–robot symbiosis, companion robots require natural human–robot interaction and autonomous intelligence through multimodal information fusion to achieve effective collaboration. Nevertheless, the control precision and coordination of the accompanying actions are not satisfactory in practical applications. This is primarily attributed to the difficulties in the motion coordination between the accompanying target and the mobile robot. This paper proposes a companion control strategy based on the Linear Quadratic Regulator (LQR) to enhance the coordination and precision of robot companion tasks. This method enables the robot to adapt to sudden changes in the companion target’s motion. Besides, the robot could smoothly avoid obstacles during the companion process. Firstly, a human–robot companion interaction model based on nonholonomic constraints is developed to determine the relative position and orientation between the robot and the companion target. Then, an LQR-based companion controller incorporating behavioral dynamics is introduced to simultaneously avoid obstacles and track the companion target’s direction and velocity. Finally, various simulations and real-world human–robot companion experiments are conducted to regulate the relative position, orientation, and velocity between the target object and the robot platform. Experimental results demonstrate the superiority of this approach over conventional control algorithms in terms of control distance and directional errors throughout system operation. The proposed LQR-based control strategy ensures coordinated and consistent motion with target persons in social companion scenarios.

Establishing the Foundations of Emotional Intelligence in Care Companion Robots to Mitigate Agitation Among High-Risk Patients With Dementia: Protocol for an Empathetic Patient-Robot Interaction Study.

An estimated 6.7 million persons are living with dementia in the United States, a number expected to double by 2060. Persons experiencing moderate to severe dementia are 4 to 5 times more likely to fall than those without dementia, due to agitation and unsteady gait. Socially assistive robots fail to address the changing emotional states associated with agitation, and it is unclear how emotional states change, how they impact agitation and gait over time, and how social robots can best respond by showing empathy. This study aims to design and validate a foundational model of emotional intelligence for empathetic patient-robot interaction that mitigates agitation among those at the highest risk: persons experiencing moderate to severe dementia. A design science approach will be adopted to (1) collect and store granular, personal, and chronological data using Personicle (an open-source software platform developed to automatically collect data from phones and other devices), incorporating real-time visual, audio, and physiological sensing technologies in a simulation laboratory and at board and care facilities; (2) develop statistical models to understand and forecast the emotional state, agitation level, and gait pattern of persons experiencing moderate to severe dementia in real time using machine learning and artificial intelligence and Personicle; (3) design and test an empathy-focused conversation model, focused on storytelling; and (4) test and evaluate this model for a care companion robot (CCR) in the community. The study was funded in October 2023. For aim 1, architecture development for Personicle data collection began with a search for existing open-source data in January 2024. A community advisory board was formed and met in December 2023 to provide feedback on the use of CCRs and provide personal stories. Full institutional review board approval was received in March 2024 to place cameras and CCRs at the sites. In March 2024, atomic marker development was begun. For aim 2, after a review of open-source data on patients with dementia, the development of an emotional classifier was begun. Data labeling was started in April 2024 and completed in June 2024 with ongoing validation. Moreover, the team established a baseline multimodal model trained and validated on healthy-person data sets, using transformer architecture in a semisupervised manner, and later retrained on the labeled data set of patients experiencing moderate to severe dementia. In April 2024, empathy alignment of large language models was initiated using prompt engineering and reinforcement learning. This innovative caregiving approach is designed to recognize the signs of agitation and, upon recognition, intervene with empathetic verbal communication. This proposal has the potential to have a significant impact on an emerging field of computational dementia science by reducing unnecessary agitation and falls of persons experiencing moderate to severe dementia, while reducing caregiver burden. PRR1-10.2196/55761.

Exploring the relationship between solitude capacity, technology readiness, and the efficacy of robotic companions in emotional relief for young adults living alone

Exploring the relationship between solitude capacity, technology readiness, and the efficacy of robotic companions in emotional relief for young adults living alone

A Companion Robots AAL Service Network based on Message Server

A Companion Robots AAL Service Network based on Message Server

Ethical implications in using robots among older adults living with dementia.

The aging of the world's population due to accelerating demographic shift on all continents is causing increasing pressure worldwide, giving rise to a "crisis of care" or "care wave." The increase in longevity is resulting in an increase in chronic diseases (such as dementia), an increase in care needs to perform the activities of daily living, and situations of isolation and profound loneliness among older adults. These circumstances are opening the debate on the need to use technology, such as robots, to improve the wellbeing of older adults and their caregivers. The aim of this paper is to address the ethical questions in using social and companion robots for people with dementia, such as concerning consent, the replacement of human care, the potential for increased dependency, and the burden on caregivers. Involving older adults and other stakeholders offers the potential to pursue robotics to support older people while also ensuring a strong ethical commitment. The study is a review of high-impact articles on the topic of the use of social and companion robots with older people with dementia.

Human-Robot Intimacy: Acceptance of Robots as Intimate Companions.

Depictions of robots as romantic partners for humans are frequent in popular culture. As robots become part of human society, they will gradually assume the role of partners for humans whenever necessary, as assistants, collaborators, or companions. Companion robots are supposed to provide social contact to those who would not have it otherwise. These companion robots are usually not designed to fulfill one of the most important human needs: the one for romantic and intimate contact. Human-robot intimacy remains a vastly unexplored territory. In this article, we review the state-of-the-art research in intimate robotics. We discuss major issues limiting the acceptance of robots as intimate partners, the public perception of robots in intimate roles, and the possible influence of cross-cultural differences in these domains. We also discuss the possible negative effects human-robot intimacy may have on human-human contact. Most importantly, we propose a new term "intimate companion robots" to reduce the negative connotations of the other terms that have been used so far and improve the social perception of research in this domain. With this article, we provide an outlook on prospects for the development of intimate companion robots, considering the specific context of their use.

Influence of a robotic companion on women's food choices: Evidence from an imaginary task.

Previous research has demonstrated the influence of commensal dining between humans on food choices, whereas we conducted two studies to examine how the presence of a robot might influence people's choices between meat-heavy and vegetable-forward meals in imaginary scenarios. In Study 1, participants were instructed to choose three desirable dishes from a set of two meat and two vegetable dishes while they imagined eating alone, with a human, or with a robot. Although the meat dishes were rated as more palatable and pleasant, the female participants chose fewer meat-heavy meals when eating alone or with a robot than when eating with a human, whereas no such effect was observed for the male participants. We also replicated these patterns in Study 2, as the female participants chose fewer meat-heavy meals when eating with a robot and a human than when eating with two humans. Collectively, these findings provide empirical evidence regarding how the presence of a certain robot can influence female consumers' food choices in imaginary scenarios, which has direct implications for the practice of promoting sustainable food choices.

Reconfigurable Robotic Exercising Companion

Regular exercise plays a crucial role in promoting overall well-being in today’s lifestyle. However, individuals often find it challenging to properly execute exercises, including maintaining correct postures and appropriate movement speeds. Robotic companions have emerged as potential solutions to assist and motivate users during exercise sessions. This research paper proposes a novel robot companion designed for exercise scenarios using a reconfigurable robot. In contrast to existing non-reconfigurable robotic companions, the use of a reconfigurable robot provides added flexibility in generating emotions. The system incorporates a module that utilizes fuzzy logic to evaluate the correctness of exercise performance based on posture variations and movement speeds. The robot generates emotions and provides feedback to users based on the exercise correctness score. The robot expresses emotions through reconfigurations, motion patterns, and variations in robot speed. This emotion-based feedback could be helpful for creating engaging and interactive exercise experiences. Apart from emotion generation, the robot utilizes vocal cues as feedback. Experimental results validate the effectiveness of the proposed system in evaluating exercise correctness and demonstrating meaningful emotion transitions. The findings of this work contribute to the development of innovative robotic companions for improving exercise adherence and overall well-being.

Robotic companions and healthy aging: A mixed-methods exploration of older adults' perspectives and insights

This study investigates older adults' perceptions and opinions regarding companion robots, contributing to the understanding of the benefits and risks associated with technology designed for senior users. Employing a mixed-methods approach, we first conducted qualitative research to explore key concepts and variables, followed by quantitative testing of the proposed model. The qualitative phase involved online interviews with 182 participants aged 55 or older. Data were analyzed using NVivo software, employing thematic analysis to identify conceptually similar themes. Six primary themes emerged, categorized into perceived benefits (health and wellness, companionship and support, and technology design advantages) and perceived risks (digital dependency and social disconnection, information integrity and online resilience, and implementation costs). Based on the qualitative findings, we developed a research model comprising six hypotheses. The model was subsequently tested quantitatively using structural equation modeling via AMOS with data from 313 adults aged 55 or older. All hypotheses were supported, with the model demonstrating an explanatory power of 56 %. Results indicate that while companion robots offer potential social and emotional support for older adults, they also present significant challenges. The study highlights the importance of balancing the advantages of companion robots with their potential drawbacks. These findings have implications for the design and implementation of smart healthcare devices for older populations, suggesting that maximizing benefits while mitigating risks could enhance adoption and utilization. This research contributes to the growing body of literature on Age technology, offering insights for researchers, technology designers, and healthcare providers working to improve the quality of life for older adults through technological innovations.

Analysis of the Pet Companion Robot Market

Pet robot is a kind of intelligent device with artificial intelligence and mechanical technology. These robots provide a solution to the challenges of balancing work responsibilities and caring for pets, while also enhancing the interaction between owners and pets to ensure the health of pets. The pet robot market is a new technology market, through the in-depth study of the pet robot market, it can fill the research gap in this field and provide new research opportunities for the academic circle. Through the research methods of questionnaire survey and literature review, we compared the global market strategies of large, small and medium-sized technology pet robot companies, combined with the quantitative literature review and questionnaire quantitative analysis, and drew the conclusion that the pet robot market has great potential, but the entry barrier is high, and the differentiation strategy is needed.

Analysis of the Pet Companion Robot Market

Pet robot is a kind of intelligent device with artificial intelligence and mechanical technology. These robots provide a solution to the challenges of balancing work responsibilities and caring for pets, while also enhancing the interaction between owners and pets to ensure the health of pets. The pet robot market is a new technology market, through the in-depth study of the pet robot market, it can fill the research gap in this field and provide new research opportunities for the academic circle. Through the research methods of questionnaire survey and literature review, we compared the global market strategies of large, small and medium-sized technology pet robot companies, combined with the quantitative literature review and questionnaire quantitative analysis, and drew the conclusion that the pet robot market has great potential, but the entry barrier is high, and the differentiation strategy is needed.

Can gestures communicate the time of described events? The perception of temporal gestures executed by a companion robot

In this paper, we investigate whether temporal gestures indicating different events in time can convey information about the relative timing of these events. We depart from the assertion, that in many cultures time is metaphorically represented as an axis, oriented from left to right, i.e. recent or future events have rightmost position, as compared to the events of the past. We test the hypothesis that temporal gestures, referring to the events on the axis, may communicate to the addressee the temporal reference of the described events. The hypothesis was tested in an experiment (N = 22 people, average age 21.6 years, 17 female) where two companion robots told stories, accompanied by left-oriented, symmetric or right oriented gestures. Each story included events from two different cases – recent and past, and each statement was accompanied by (a) left-oriented or symmetric gesture (first condition), or (b) right-oriented or symmetric gesture (second condition). The task of the subjects was to correlate the described events with time, assigning each statement with recent or past event. As a result of this study, the hypothesis that the orientation of temporal gestures can communicate the time of the designated events was partially confirmed. The results correspond to the concept of time oriented for the narrator from left to right, where the events of the past are in the center, and later events (recent events, or ‘today’) are located on the right. This conceptual orientation in the experiment was interpreted by the human listeners, although from their point of view, “recent events” are located on the left side of the metaphoric axis.

Prototyping a Zoomorphic Interactive Robot Companion with Emotion Recognition and Affective Voice Interaction for Elderly People

An aging society paired with a skilled labor shortage, particularly in European countries, requires a rethinking of deprecated structures. Intelligent assistive technologies, specifically socially assistive robots, addressing the gap between caretakers and elderly people in need of care have moved into the focus of debate due to their potentials to reduce costs, improve independence, and eventually raise quality of life. In this work, we outline the potentials of zoomorphic robot companions combining intelligent conversational abilities and emotion recognition. We then describe the prototyping of an emotion-sensing zoomorphic interactive robot companion including the development and implementation of a multimodal emotion recognition framework. This framework uses speech emotion recognition, sentiment analysis, and affective voice interaction based on a large language model. The prototyping has been accompanied by two studies on elderly peoples' design preferences regarding the proposed feature set as well as different embodiments to find the appropriate casing for the robot companion. This work provides valuable insights into the prototyping and can thus support future research endeavors in this area.

Recommendations for designing conversational companion robots with older adults through foundation models.

Companion robots are aimed to mitigate loneliness and social isolation among older adults by providing social and emotional support in their everyday lives. However, older adults' expectations of conversational companionship might substantially differ from what current technologies can achieve, as well as from other age groups like young adults. Thus, it is crucial to involve older adults in the development of conversational companion robots to ensure that these devices align with their unique expectations and experiences. The recent advancement in foundation models, such as large language models, has taken a significant stride toward fulfilling those expectations, in contrast to the prior literature that relied on humans controlling robots (i.e., Wizard of Oz) or limited rule-based architectures that are not feasible to apply in the daily lives of older adults. Consequently, we conducted a participatory design (co-design) study with 28 older adults, demonstrating a companion robot using a large language model (LLM), and design scenarios that represent situations from everyday life. The thematic analysis of the discussions around these scenarios shows that older adults expect a conversational companion robot to engage in conversation actively in isolation and passively in social settings, remember previous conversations and personalize, protect privacy and provide control over learned data, give information and daily reminders, foster social skills and connections, and express empathy and emotions. Based on these findings, this article provides actionable recommendations for designing conversational companion robots for older adults with foundation models, such as LLMs and vision-language models, which can also be applied to conversational robots in other domains.

Robot-based solution for helping Alzheimer patients

Alzheimer's is a progressive and debilitating neurological disorder characterized by cognitive decline, memory loss, and impaired daily functioning. It is an irreversible brain disease that destroys memory, thinking, and the ability to carry out daily activities. It poses significant challenges for patients and healthcare providers. Modern societies are trying to enhance the quality of people's lives, including Alzheimer's patients. In this study, we explored the potential of social robots to provide emotional support, improve cognitive function, and facilitate communication among Alzheimer's patients. This was achieved by initiating conversations on various topics such as family, relationships, and daily activities. This paper contributes to the literature by introducing a novel and well-organized framework for building an Alzheimer's care robot. Further, this study enriches the literature by introducing the Alzheimer Care Companion Robot (ACCR), designed to identify Alzheimer's patients. The ACCR initiates conversations in the native Arab-Kuwaiti dialect, displaying relevant memories through images and videos on its screen to assist in memory recall based on the individuals' life experiences. The proposed ACCR consists of 271 conversations belonging to three main categories: active, proactive, and graphical user interface (GUI) dialogs comprising 112 dialogs, 109 dialogs, and 50 dialogs for active, proactive, and GUI, respectively. The experimental result illustrated the success of the proposed solution.