The law draws a sharp distinction between objects and persons, and between two kinds of persons, the ''fictional'' kind (i.e. corporations), and the ''non-fictional'' kind (individual or ''natural'' persons). This paper will assess whether we maximize overall long-term legal coherence by (A) maintaining an object classification for all future AI systems, (B) creating fictional legal persons associated with suitably advanced, individuated AI systems (giving these fictional legal persons derogable rights and duties associated with certified groups of existing persons, potentially including free speech, contract rights, and standing to sue ''on behalf of'' the AI system), or (C) recognizing non-fictional legal personhood through legal identity for suitably advanced, individuated AI systems (recognizing them as entities meriting legal standing with non-derogable rights which for the human case include life, due process, habeas corpus, freedom from slavery, and freedom of conscience). We will clarify the meaning and implications of each option along the way, considering liability, copyright, family law, fundamental rights, civil rights, citizenship, and AI safety regulation. We will tentatively find that the non-fictional personhood approach may be best from a coherence perspective, for at least some advanced AI systems. An object approach may prove untenable for sufficiently humanoid advanced systems, though we suggest that it is adequate for currently existing systems as of 2025. While fictional personhood would resolve some coherence issues for future systems, it would create others and provide solutions that are neither durable nor fit for purpose. Finally, our review will suggest that ''hybrid'' approaches are likely to fail and lead to further incoherence: the choice between object, fictional person and non-fictional person is unavoidable.

I wouldn't go as far as to say it's been dethroned, but Mirumi -- the clingy fluffball with a staring problem -- now has some serious competition for the title of cutest robot at CES 2025. I just met Ropet, a wide-eyed companion robot with warm, soft fur, little flapping arms and big feelings. And damn is that thing adorable. Ropet's sole mission is to love and be loved. Think of it like a living plushie; it has a personality, will listen to your deepest darkest secrets without judgement, and will reach out to hug you when you're sad.

This paper presents a collaborative fall detection and response system integrating Wi-Fi sensing with robotic assistance. The proposed system leverages channel state information (CSI) disruptions caused by movements to detect falls in non-line-of-sight (NLOS) scenarios, offering non-intrusive monitoring. Besides, a companion robot is utilized to provide assistance capabilities to navigate and respond to incidents autonomously, improving efficiency in providing assistance in various environments. The experimental results demonstrate the effectiveness of the proposed system in detecting falls and responding effectively.

Understanding user enjoyment is crucial in human-robot interaction (HRI), as it can impact interaction quality and influence user acceptance and long-term engagement with robots, particularly in the context of conversations with social robots. However, current assessment methods rely solely on self-reported questionnaires, failing to capture interaction dynamics. This work introduces the Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES), a novel scale for assessing user enjoyment from an external perspective during conversations with a robot. Developed through rigorous evaluations and discussions of three annotators with relevant expertise, the scale provides a structured framework for assessing enjoyment in each conversation exchange (turn) alongside overall interaction levels. It aims to complement self-reported enjoyment from users and holds the potential for autonomously identifying user enjoyment in real-time HRI. The scale was validated on 25 older adults' open-domain dialogue with a companion robot that was powered by a large language model for conversations, corresponding to 174 minutes of data, showing moderate to good alignment. The dataset is available online. Additionally, the study offers insights into understanding the nuances and challenges of assessing user enjoyment in robot interactions, and provides guidelines on applying the scale to other domains.

Learning companion robots for young children are increasingly adopted in informal learning environments. Although parents play a pivotal role in their children's learning, very little is known about how parents prefer to incorporate robots into their children's learning activities. We developed prototype capabilities for a learning companion robot to deliver educational prompts and responses to parent-child pairs during reading sessions and conducted in-home user studies involving 10 families with children aged 3-5. Our data indicates that parents want to work with robots as collaborators to augment parental activities to foster children's learning, introducing the notion of parent-robot collaboration. Our findings offer an empirical understanding of the needs and challenges of parent-child interaction in informal learning scenarios and design opportunities for integrating a companion robot into these interactions. We offer insights into how robots might be designed to facilitate parent-robot collaboration, including parenting policies, collaboration patterns, and interaction paradigms.

We aim to design robotic educational support systems that can promote socially and intellectually meaningful learning experiences for students while they complete school work outside of class. To pursue this goal, we conducted participatory design studies with 10 children (aged 10--12) to explore their design needs for robot-assisted homework. We investigated children's current ways of doing homework, the type of support they receive while doing homework, and co-designed the speech and expressiveness of a homework companion robot. Children and parents attending our design sessions explained that an emotionally expressive social robot as a homework aid can support students' motivation and engagement, as well as their affective state. Children primarily perceived the robot as a dedicated assistant at home, capable of forming meaningful friendships, or a shared classroom learning resource. We present key design recommendations to support students' homework experiences with a learning companion robot.

The social separation imposed by the pandemic led us to rely on technology to an extent we might never have imagined – from Teams and Zoom to online banking and vaccine status apps. Now, society faces an increasing number of decisions about our relationship with technology. For example, do we want our workforce needs fulfilled by automation, migrant workers, or an increased birth rate? In the coming years, we will also need to balance technological innovation with people's wellbeing – both in terms of the work they do and the social support they receive. And there is the question of trust. When humans should trust robots, and vice versa, is a question our Trust Node team is researching as part of the UKRI Trustworthy Autonomous Systems hub.

Intuition Robotics announced today that it is expanding its mission of improving the lives of older adults to include enhancing their interactions with their physicians. The Israeli startup has developed the AI-based, award-winning proactive social robot ElliQ which has spent over 30,000 days in older adults' homes over the past two years. Now ElliQ will help increase patient engagement while offering primary care providers continuous actionable data and insights for early detection and intervention. The very big challenge Intuition Robotics set up to solve was to "understand how to create a relationship between a human and a machine," says co-founder and CEO Dor Skuler. Unlike a number of unsuccessful high-profile social robots (e.g., Pepper) that tried to perform multiple functions in multiple settings, ElliQ has focused exclusively on older adults living alone.

Humans are social creatures, but what happens when their social networks break down? Companion robots may be able to provide emotional support increasingly missing from people's lives. While such a scenario sounds like a sci-fi pipe dream, 21st century Japan is developing the technology to make it a reality. There is a growing demand. Here, there is a clear distinction between uchi and soto, one's in-group and out-group. Most people aren't likely to strike it up with members of the latter.

Amazon Alexa: "Sorry to hear that. Talking to a friend, listening to music or taking a walk might help. I hope you feel better soon." Alexa's artificial intelligence-infused heart may be in the right place, but there's only so far it or any AI can go to comfort someone who is alone. All the same, Alexa's response raises questions about just what kind of role an AI can play to "cure" loneliness, especially among the elderly.