Overtrust in AI Recommendations About Whether or Not to Kill: Evidence from Two Human-Robot Interaction Studies.

This research explores prospective determinants of trust in the recommendations of artificial agents regarding decisions to kill, using a novel visual challenge paradigm simulating threat-identification (enemy combatants vs. civilians) under uncertainty. In Experiment 1, we compared trust in the advice of a physically embodied versus screen-mediated anthropomorphic robot, observing no effects of embodiment; in Experiment 2, we manipulated the relative anthropomorphism of virtual robots, observing modestly greater trust in the most anthropomorphic agent relative to the least. Across studies, when any version of the agent randomly disagreed, participants reversed their threat-identifications and decisions to kill in the majority of cases, substantially degrading their initial performance. Participants' subjective confidence in their decisions tracked whether the agent (dis)agreed, while both decision-reversals and confidence were moderated by appraisals of the agent's intelligence. The overall findings indicate a strong propensity to overtrust unreliable AI in life-or-death decisions made under uncertainty.

Social Type-Aware Navigation Framework for Mobile Robots in Human-Shared Environments.

As robots become increasingly common in human-populated environments, they must be perceived as social beings and behave socially. People try to preserve their own space during social interactions with others, and this space depends on a variety of factors, such as individual characteristics or their age. In real-world social spaces, there are many different types of people, and robots need to be more sensitive, especially when interacting with vulnerable subjects such as children. However, the current navigation methods do not consider these differences and apply the same avoidance strategies to everyone. Thus, we propose a new navigation framework that considers different social types and defines appropriate personal spaces for each, allowing robots to respect them. To this end, the robot needs to classify people in a real environment into social types and define the personal space for each type as a Gaussian asymmetric function to respect them. The proposed framework is validated through simulations and real-world experiments, demonstrating that the robot can improve the quality of interactions with people by providing each individual with an adaptive personal space. The proposed costmap layer is available on GitHub.

The League: A person-centred approach to the development of social robotics for paediatric anxiety.

BACKGROUND: Social robots are promising tools to improve the quality of life of children and youth living with anxiety and should be developed based on the priorities of end users. However, pathways to include young people in patient-oriented research, particularly in the overlap between technology and mental health, have been historically limited. OBJECTIVE: In this work, we describe engagement with experts with lived experiences of paediatric anxiety in a social robotics research programme. We report the experiences of patient advisors in a co-creation process and identify considerations for other research groups looking to involve end users in technology development in the field of youth mental health. DESIGN: We engaged individuals with a lived experience of paediatric anxiety (current, recent past, or from a parent perspective) using three different models over the course of three years. Two initial patient partners were involved during project development, eight were engaged as part of an advisory panel ('the League') during study development and data analysis and four contributed as ongoing collaborators in an advisory role. League members completed a preparticipation expectation survey and a postparticipation experience survey. FINDINGS: Eight individuals from a range of anxiety-related diagnostic groups participated in the League as patient partners. Members were teenagers (n = 3), young adults aged 22-26 years who had connected with a youth mental health service as children within the past eight years (n = 3) or parents of children presently living with anxiety (n = 2). Preferred methods of communication, expectations and reasons for participating were collected. The League provided specific and actionable feedback on the design of workshops on the topic of social robotics, which was implemented. They reported that their experiences were positive and fairly compensated, but communication and sustained engagement over time were challenges. Issues of ethics and language related to patient-centred brain health technology research are discussed. CONCLUSIONS: There is an ethical imperative to meaningfully incorporate the voices of youth and young adults with psychiatric conditions in the development of devices intended to support their mental health and quality of life. PATIENT OR PUBLIC CONTRIBUTION: Six young people and two parents with lived experiences of paediatric anxiety participated in all stages of developing a research programme on social robotics to support paediatric mental health in a community context. They also provided input during the preparation of this manuscript.

People follow motivation-structural rules when they react to synthetised sounds.

Emotionally expressive vocalizations can elicit approach-avoidance responses in humans and non-human animals. We investigated whether artificially generated sounds have similar effects on humans. We assessed whether subjects' reactions were linked to acoustic properties, and associated valence and intensity. We generated 343 artificial sounds with differing call lengths, fundamental frequencies and added acoustic features across 7 categories and 3 levels of biological complexity. We assessed the hypothetical behavioural response using an online questionnaire with a manikin task, in which 172 participants indicated whether they would approach or withdraw from an object emitting the sound. (1) Quieter sounds elicited approach, while loud sounds were associated with avoidance. (2) The effect of pitch was modulated by category, call length and loudness. (2a) Low-pitched sounds in complex sound categories prompted avoidance, while in other categories they elicited approach. (2b) Higher pitch in loud sounds had a distancing effect, while higher pitch in quieter sounds prompted approach. (2c) Longer sounds promoted avoidance, especially at high frequencies. (3) Sounds with higher intensity and negative valence elicited avoidance. We conclude that biologically based acoustic signals can be used to regulate the distance between social robots and humans, which can provide an advantage in interactive scenarios.

Socially adaptive cognitive architecture for human-robot collaboration in industrial settings.

This paper introduces DAC-HRC, a novel cognitive architecture designed to optimize human-robot collaboration (HRC) in industrial settings, particularly within the context of Industry 4.0. The architecture is grounded in the Distributed Adaptive Control theory and the principles of joint intentionality and interdependence, which are key to effective HRC. Joint intentionality refers to the shared goals and mutual understanding between a human and a robot, while interdependence emphasizes the reliance on each other's capabilities to complete tasks. DAC-HRC is applied to a hybrid recycling plant for the disassembly and recycling of Waste Electrical and Electronic Equipment (WEEE) devices. The architecture incorporates several cognitive modules operating at different timescales and abstraction levels, fostering adaptive collaboration that is personalized to each human user. The effectiveness of DAC-HRC is demonstrated through several pilot studies, showcasing functionalities such as turn-taking interaction, personalized error-handling mechanisms, adaptive safety measures, and gesture-based communication. These features enhance human-robot collaboration in the recycling plant by promoting real-time robot adaptation to human needs and preferences. The DAC-HRC architecture aims to contribute to the development of a new HRC paradigm by paving the way for more seamless and efficient collaboration in Industry 4.0 by relying on socially adept cognitive architectures.

Artificial cognition vs. artificial intelligence for next-generation autonomous robotic agents.

The trend in industrial/service robotics is to develop robots that can cooperate with people, interacting with them in an autonomous, safe and purposive way. These are the fundamental elements characterizing the fourth and the fifth industrial revolutions (4IR, 5IR): the crucial innovation is the adoption of intelligent technologies that can allow the development of cyber-physical systems, similar if not superior to humans. The common wisdom is that intelligence might be provided by AI (Artificial Intelligence), a claim that is supported more by media coverage and commercial interests than by solid scientific evidence. AI is currently conceived in a quite broad sense, encompassing LLMs and a lot of other things, without any unifying principle, but self-motivating for the success in various areas. The current view of AI robotics mostly follows a purely disembodied approach that is consistent with the old-fashioned, Cartesian mind-body dualism, reflected in the software-hardware distinction inherent to the von Neumann computing architecture. The working hypothesis of this position paper is that the road to the next generation of autonomous robotic agents with cognitive capabilities requires a fully brain-inspired, embodied cognitive approach that avoids the trap of mind-body dualism and aims at the full integration of Bodyware and Cogniware. We name this approach Artificial Cognition (ACo) and ground it in Cognitive Neuroscience. It is specifically focused on proactive knowledge acquisition based on bidirectional human-robot interaction: the practical advantage is to enhance generalization and explainability. Moreover, we believe that a brain-inspired network of interactions is necessary for allowing humans to cooperate with artificial cognitive agents, building a growing level of personal trust and reciprocal accountability: this is clearly missing, although actively sought, in current AI. The ACo approach is a work in progress that can take advantage of a number of research threads, some of them antecedent the early attempts to define AI concepts and methods. In the rest of the paper we will consider some of the building blocks that need to be re-visited in a unitary framework: the principles of developmental robotics, the methods of action representation with prospection capabilities, and the crucial role of social interaction.

Assessing Feasibility of Cognitive Impairment Testing Using Social Robotic Technology Augmented with Affective Computing and Emotional State Detection Systems.

Social robots represent a valid opportunity to manage the diagnosis, treatment, care, and support of older people with dementia. The aim of this study is to validate the Mini-Mental State Examination (MMSE) test administered by the Pepper robot equipped with systems to detect psychophysical and emotional states in older patients. Our main result is that the Pepper robot is capable of administering the MMSE and that cognitive status is not a determinant in the effective use of a social robot. People with mild cognitive impairment appreciate the robot, as it interacts with them. Acceptability does not relate strictly to the user experience, but the willingness to interact with the robot is an important variable for engagement. We demonstrate the feasibility of a novel approach that, in the future, could lead to more natural human-machine interaction when delivering cognitive tests with the aid of a social robot and a Computational Psychophysiology Module (CPM).

A social robot connected with chatGPT to improve cognitive functioning in ASD subjects.

Neurodevelopmental Disorders (NDDs) represent a significant healthcare and economic burden for families and society. Technology, including AI and digital technologies, offers potential solutions for the assessment, monitoring, and treatment of NDDs. However, further research is needed to determine the effectiveness, feasibility, and acceptability of these technologies in NDDs, and to address the challenges associated with their implementation. In this work, we present the application of social robotics using a Pepper robot connected to the OpenAI system (Chat-GPT) for real-time dialogue initiation with the robot. After describing the general architecture of the system, we present two possible simulated interaction scenarios of a subject with Autism Spectrum Disorder in two different situations. Limitations and future implementations are also provided to provide an overview of the potential developments of interconnected systems that could greatly contribute to technological advancements for Neurodevelopmental Disorders (NDD).

Artificial Emotional Intelligence in Socially Assistive Robots for Older Adults: A Pilot Study.

This paper presents our recent research on integrating artificial emotional intelligence in a social robot (Ryan) and studies the robot's effectiveness in engaging older adults. Ryan is a socially assistive robot designed to provide companionship for older adults with depression and dementia through conversation. We used two versions of Ryan for our study, empathic and non-empathic. The empathic Ryan utilizes a multimodal emotion recognition algorithm and a multimodal emotion expression system. Using different input modalities for emotion, i.e. facial expression and speech sentiment, the empathic Ryan detects users emotional state and utilizes an affective dialogue manager to generate a response. On the other hand, the non-empathic Ryan lacks facial expression and uses scripted dialogues that do not factor in the users emotional state. We studied these two versions of Ryan with 10 older adults living in a senior care facility. The statistically significant improvement in the users' reported face-scale mood measurement indicates an overall positive effect from the interaction with both the empathic and non-empathic versions of Ryan. However, the number of spoken words measurement and the exit survey analysis suggest that the users perceive the empathic Ryan as more engaging and likable.

Perspectives of Healthcare Providers to Inform the Design of an AI-Enhanced Social Robot in the Pediatric Emergency Department.

Children commonly experience pain and distress in healthcare settings related to medical procedures such as blood tests and intravenous insertions (IVIs). Inadequately addressed pain and distress can result in both short- and long-term negative consequences. The use of socially assistive robotics (SARs) to reduce procedure-related distress and pain in children's healthcare settings has shown promise; however, the current options lack autonomous adaptability. This study presents a descriptive qualitative needs assessment of healthcare providers (HCPs) in two Canadian pediatric emergency departments (ED) to inform the design an artificial intelligence (AI)-enhanced social robot to be used as a distraction tool in the ED to facilitate IVIs. Semi-structured virtual individual and focus group interviews were conducted with eleven HCPs. Four main themes were identified: (1) common challenges during IVIs (i.e., child distress and resource limitations), (2) available tools for pain and distress management during IVIs (i.e., pharmacological and non-pharmacological), (3) response to SAR appearance and functionality (i.e., personalized emotional support, adaptive distraction based on child's preferences, and positive reinforcement), and (4) anticipated benefits and challenges of SAR in the ED (i.e., ensuring developmentally appropriate interactions and space limitations). HCPs perceive AI-enhanced social robots as a promising tool for distraction during IVIs in the ED.

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.

Exploring the needs of children and caregivers to inform design of an artificial intelligence-enhanced social robot in the pediatric emergency department.

Background & Objective: Socially assistive robots (SARs) are a promising tool to manage children's pain and distress related to medical procedures, but current options lack autonomous adaptability. The aim of this study was to understand children's and caregivers' perceptions surrounding the use of an artificial intelligence (AI)-enhanced SAR to provide personalized procedural support to children during intravenous insertion (IVI) to inform the design of such a system following a user-centric approach. Methods: This study presents a descriptive qualitative needs assessment of children and caregivers. Data were collected via semi-structured individual interviews and focus groups. Participants were recruited from two Canadian pediatric emergency departments (EDs) between April 2021 and January 2022. Results: Eleven caregivers and 19 children completed 27 individual interviews and one focus group. Three main themes were identified: A. Experience in the clinical setting, B. Acceptance of and concerns surrounding SARs, and C. Features that support child engagement with SARs. Most participants expressed comfort with robot technology, however, concerns were raised about sharing personal information, photographing/videotaping, and the possibility of technical failure. Suggestions for feature enhancements included increasing movement to engage a child's attention and tailoring language to developmental age. To enhance the overall ED experience, participants also identified a role for the SAR in the waiting room. Conclusion: Artificial intelligence-enhanced SARs were perceived by children and caregivers as a promising tool for distraction during IVIs and to enhance the overall ED experience. Insights collected will be used to inform the design of an AI-enhanced SAR.

Seeing eye to eye: trustworthy embodiment for task-based conversational agents.

Smart speakers and conversational agents have been accepted into our homes for a number of tasks such as playing music, interfacing with the internet of things, and more recently, general chit-chat. However, they have been less readily accepted in our workplaces. This may be due to data privacy and security concerns that exist with commercially available smart speakers. However, one of the reasons for this may be that a smart speaker is simply too abstract and does not portray the social cues associated with a trustworthy work colleague. Here, we present an in-depth mixed method study, in which we investigate this question of embodiment in a serious task-based work scenario of a first responder team. We explore the concepts of trust, engagement, cognitive load, and human performance using a humanoid head style robot, a commercially available smart speaker, and a specially developed dialogue manager. Studying the effect of embodiment on trust, being a highly subjective and multi-faceted phenomena, is clearly challenging, and our results indicate that potentially, the robot, with its anthropomorphic facial features, expressions, and eye gaze, was trusted more than the smart speaker. In addition, we found that embodying a conversational agent helped increase task engagement and performance compared to the smart speaker. This study indicates that embodiment could potentially be useful for transitioning conversational agents into the workplace, and further in situ, "in the wild" experiments with domain workers could be conducted to confirm this.

Social robotics to support older people with dementia: a study protocol with Paro seal robot in an Italian Alzheimer's day center.

Introduction: The aging of the population and the high incidence of those over 80 lead to an inevitable increase in chronic degenerative diseases, such as dementia, resulting in increased morbidity and disability. Treatment of people with dementia involves both pharmacological and non-pharmacological interventions. In particular, robot-assisted therapy is a potentially useful treatment for dementia as it has the advantage of improving mood, encouraging social interaction and communication. The overall objective of the study is to evaluate the improvement in patient-perceived quality of life following the use of the Paro robot integrated with usual care in the older people with dementia. Methods and analysis: For this study, 20 patients with dementia are recruited and divided into Experimental Group (EG) and Control Group (CG). Twenty-four session of intervention are conducted, divided into 2 sessions per week, for 12 weeks. The therapy sessions last 20 min. The Experimental Group will receive a social robotic intervention with Paro combined with usual care; the Control Group will receive only the traditional therapy, consisting of cognitive stimulation (reality orientation therapy, cognitive training) and occupational activities (painting workshops, cooking workshops, garden therapy, music therapy, etc.). Paro is a seal-shaped robot designed to have a calming effect and elicit emotional responses in patients in hospitals, nursing homes, and retirement homes. Assessment will be performed at the baseline, at the end of intervention and 3 months after the end of intervention. During these phases, several scales will be administered to the patients, such as Quality of Life-Alzheimer's Disease, Addenbrooke's Cognitive Examination, the Rating Anxiety In Dementia scale and the Cornell Scale for Depression in Dementia, Quebec User Evaluation of Satisfaction, Neuropsychiatric Inventory, the Technology Acceptance Model. Discussions: The final goals of the present study are to evaluate the improvement in patient-perceived quality of life following the use of the Paro robot integrated with usual care in the older people with dementia. Ethics and dissemination: The study was approved by the Ethic Committee of the Istituto Nazionale Ricovero e Cura Anziani, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS INRCA) during the session of 12 April 2022. It was recorded in ClinicalTrials.gov on 23 November 2022 on the number NCT05626205. The study findings will be used for publication in peer-reviewed scientific journals and presentations in scientific meetings.

A Role for Bottom-Up Synthetic Cells in the Internet of Bio-Nano Things?

The potential role of bottom-up Synthetic Cells (SCs) in the Internet of Bio-Nano Things (IoBNT) is discussed. In particular, this perspective paper focuses on the growing interest in networks of biological and/or artificial objects at the micro- and nanoscale (cells and subcellular parts, microelectrodes, microvessels, etc.), whereby communication takes place in an unconventional manner, i.e., via chemical signaling. The resulting "molecular communication" (MC) scenario paves the way to the development of innovative technologies that have the potential to impact biotechnology, nanomedicine, and related fields. The scenario that relies on the interconnection of natural and artificial entities is briefly introduced, highlighting how Synthetic Biology (SB) plays a central role. SB allows the construction of various types of SCs that can be designed, tailored, and programmed according to specific predefined requirements. In particular, "bottom-up" SCs are briefly described by commenting on the principles of their design and fabrication and their features (in particular, the capacity to exchange chemicals with other SCs or with natural biological cells). Although bottom-up SCs still have low complexity and thus basic functionalities, here, we introduce their potential role in the IoBNT. This perspective paper aims to stimulate interest in and discussion on the presented topics. The article also includes commentaries on MC, semantic information, minimal cognition, wetware neuromorphic engineering, and chemical social robotics, with the specific potential they can bring to the IoBNT.

Evaluation of the Toyota Human Support Robot (HSR) for Social Interaction and Learning.

Tele-operated social robots (telerobots) offer an innovative means of allowing children who are medically restricted to their homes (MRH) to return to their local schools and physical communities. Most commercially available telerobots have three foundational features that facilitate child-robot interaction: remote mobility, synchronous two-way vision capabilities, and synchronous two-way audio capabilities. We conducted a comparative analysis between the Toyota Human Support Robot (HSR) and commercially available telerobots, focusing on these foundational features. Children who used these robots and these features on a daily basis to attend school were asked to pilot the HSR in a simulated classroom for learning activities. As the HSR has three additional features that are not available on commercial telerobots: (1) pan-tilt camera, (2) mapping and autonomous navigation, and (3) robot arm and gripper for children to "reach" into remote environments, participants were also asked to evaluate the use of these features for learning experiences. To expand on earlier work on the use of telerobots by remote children, this study provides novel empirical findings on (1) the capabilities of the Toyota HSR for robot-mediated learning similar to commercially available telerobots and (2) the efficacy of novel HSR features (i.e., pan-tilt camera, autonomous navigation, robot arm/hand hardware) for future learning experiences. We found that among our participants, autonomous navigation and arm/gripper hardware were rated as highly valuable for social and learning activities.

Say what you want, I'm not listening!: A conversational self-reflection robot that does not parse user speech.

We present a conversational social robot behaviour design that draws from psychotherapy research to support individual self-reflection and wellbeing, without requiring the robot to parse or otherwise understand what the user is saying. This simplicity focused approached enabled us to intersect the well-being aims with privacy and simplicity, while achieving high robustness. We implemented a fully autonomous and standalone (not network enabled) prototype and conducted a proof-of-concept study as an initial step to test the feasibility of our behaviour design: whether people would successfully engage with our simple behaviour and could interact meaningfully with it. We deployed our robot unsupervised for 48 h into the homes of 14 participants. All participants engaged with self-reflection with the robot without reporting any interaction challenges or technical issues. This supports the feasibility of our specific behaviour design, as well as the general viability of our non-parsing simplicity approach to conversation, which we believe to be an exciting avenue for further exploration. Our results thus pave the way for further exploring how conversational behaviour designs like ours may support people living with loneliness.

Artificial scaffolding: Augmenting social cognition by means of robot technology.

The concept of scaffolding refers to the support that the environment provides in the acquisition and consolidation of new abilities. Technological advancements allow for support in the acquisition of cognitive capabilities, such as second language acquisition using simple smartphone applications There is, however, one domain of cognition that has been scarcely addressed in the context of technologically assisted scaffolding: social cognition. We explored the possibility of supporting the acquisition of social competencies of a group of children with autism spectrum disorder engaged in a rehabilitation program (age = 5.8 ± 1.14, 10 females, 33 males) by designing two robot-assisted training protocols tailored to Theory of Mind competencies. One protocol was performed with a humanoid robot and the other (control) with a non-anthropomorphic robot. We analyzed changes in NEPSY-II scores before and after the training using mixed effects models. Our results showed that activities with the humanoid significantly improved NEPSY-II scores on the ToM scale. We claim that the motor repertoire of humanoids makes them ideal platforms for artificial scaffolding of social skills in individuals with autism, as they can evoke similar social mechanisms to those elicited in human-human interaction, without providing the same social pressure that another human might exert.