Human-scale inflatable robots that respond to users’ movements offer a unique opportunity to explore embodied and relational interaction. Yet, little is known about how their form and interactive behaviour shape engagement and perception. This study presents three inflatable robots designed to explore different levels of user control: Barrel affords high user freedom, allowing approach or retreat; Chair introduces partial constraint through a semi-enclosing structure; Jacket envelops the user, with movement largely determined by the robot. Across all systems, robot motion responds to user behaviour in semi-random ways, contributing to a sense of agency. Participants engaged through a think-aloud protocol, followed by semi-structured interviews and a drawing activity. Findings show that, in the absence of clear prior models, participants actively constructed interpretations of behaviour, attributing agency and intention, while variations in control shaped trust and relational dynamics. These results offer insights for designing human-scale soft robots that support rich, embodied interaction.

As robots are increasingly designed to take on relational qualities, new methods are needed to explore how their form, movement, and presence shape connection. We introduce puppetry as an embodied design practice in which designers and users inhabit and control physical prototypes to examine relational qualities before technical implementation. We ground this in equine-assisted interventions, where horses act as relational partners: their unpredictability, reciprocity, and embodied risk require humans to attune and regulate emotions. We utilised puppeteering to re-enact these dynamics safely through movement, materiality, and interaction, surfacing how such qualities may inform relational robots. Our findings show that puppeteering functions as a generative practice: movement becomes inquiry, playful performance reveals affective states, and low-fidelity prototypes enable safe exploration of relational complexity. Puppetry thus offers a pathway for developing robots as autonomous, equal partners; revealing insights into agency, co-embodiment, and materiality, often overlooked in traditional prototyping.

Initiating joint attention (JA) is a fundamental first step in social interactions. In sighted individuals, it relies predominantly on visual cues, such as gaze and hand gestures. These features can reduce opportunities for blind and visually impaired (BVI) and sighted people to interact. Understanding the strategies to navigate these challenges is necessary to develop technology that can facilitate case study of five children with mixed visual abilities engaging in activities rich with JA opportunities. In a teacher-led classroom, the children experimented with the use of an AI-powered headset designed to support BVI people in social situations. Interaction analysis established that situational complexity affects the children’s responses to initiation attempts. Furthermore, the headset adds to this complexity, affecting the frequency and reactions to attempts to initiate JA. The findings informed the creation of a JA initiation framework and suggestions for future design.

Equine-Assisted Interventions (EAIs) aim to improve participant health and well-being through the development of a therapeutic relationship with a trained horse. These interventions leverage the horse’s ability to provide emotional feedback, as it responds to negative non-verbal cues with reciprocal negativity, thereby encouraging participants to regulate their emotions and achieve attunement with the horse. Despite their benefits, EAIs face significant challenges, including logistical, financial, and resource constraints, which hinder their widespread adoption and accessibility. To address these issues, we conducted an autoethnographic study of the lead researcher’s engagement in an EAI to investigate the underlying mechanisms and explore potential technological alternatives. Our findings suggest that the reciprocal and responsive non-verbal communication, combined with the horse’s considerable physical presence, supports the potential of an embodied robotic system as a viable alternative. Such a system could offer a scalable and sustainable solution to the current limitations of EAIs.

Joint attention (JA) is a crucial component of social interaction, relying heavily on visual cues like eye gaze and pointing. This creates barriers for blind and visually impaired people (BVI) to engage in JA with sighted peers. Yet, little research has characterised these barriers or the strategies BVI people employ to overcome them. We interviewed ten BVI adults to understand JA experiences and analysed videos of four BVI children with eight sighted partners engaging in activities conducive to JA. Interviews revealed that lack of JA feedback is perceived as voids that block engagement, exacerbated in group settings, with an emphasis on oneself to fill those voids. Video analysis anchored the absence of the person element within typical JA triads, suggesting a potential for technology to foster alternative dynamics between BVI and sighted people. We argue these findings could inform technology design that supports more inclusive JA interactions.

In this paper we apply the recent concept of robot Ethical Risk Assessment to an exemplar Socially Assistive Robot (SAR); specifically considering ethical risks posed by anthropomorphism in this context. We draw on two complimentary studies to demonstrate that anthropomorphism is important to overall SAR function and overall relatively low ethical risk. As such, rather than avoiding anthropomoprhism all together (as suggested in a recently published standard on robot ethics), we suggest anthropomorphism in SARs should be a customisable trait that can be adapted to the user.

There will always be occasions where robots require assistance from humans. Understanding what motivates people to help a robot, and what effect this interaction has on an individual will be essential in successfully integrating robots into our society. Emotions are important in motivating prosocial behavior between people, and therefore may also play a large role in human-robot interaction. This research explores the role of emotion in motivating people to help a robot and some of the ethical issues that arise as a result, with the ultimate aim of developing suitable methods for robots to interact with humans to acquire assistance.

In this paper we present the results of an experimental study investigating the application of human persuasive strategies to a social robot. We demonstrate that robot displays of goodwill and similarity to the participant significantly increased robot persuasiveness, as measured objectively by participant behaviour. However, such strategies had no impact on subjective measures concerning perception of the robot, and perception of the robot did not correlate with participant behaviour. We hypothesise that this is due to difficulty in accurately measuring perception of a robot using subjective measures. We suggest our results are particularly relevant for the design and development of socially assistive robots.

Machines with high levels of autonomy such as robots and our growing need to interact with them creates challenges to ensure safe operation. The recent interest to create autonomous vehicles through the integration of control and decision-making systems makes such vehicles robots too. We therefore applied estimation and decision-making mechanisms currently investigated for human-robot interaction to human-vehicle interaction. In other words, we define the vehicle as an autonomous agent with which the human driver interacts, and focus on understanding the human intentions and decision-making processes. These are then integrated into the ro-bot`s/vehicle's own control and decision-making system not only to understand human behaviour while it occurs but to predict the next actions. To obtain knowledge about the human's intentions, this work relies heavily on the use of motion tracking data (i.e. skeletal tracking, body posture)gathered from drivers whilst driving. We use a data-driven approach to both classify current driving manoeuvres and predict future manoeuvres, by using a fixed prediction window and augmenting a standard set of manoeuvres. Results are validated against drivers of different sizes, seat preferences and levels of driving expertise to evaluate the robustness of the methods; precision and recall metrics higher than 95% for manoeuvre classification and 90% for manoeuvre prediction with time-windows of up to 1.3 seconds are obtained. The idea of prediction adds a highly novel aspect to human-robot/human-vehicle interaction, allowing for decision and control at a later point.

Emphasis, by means of either pitch accents or beat gestures (rhythmic co-verbal gestures with no semantic meaning), has been shown to serve two main purposes in human communication: syntactic disambiguation and salience. To use beat gestures in this role, interlocutors must be able to integrate them with the speech they accompany. Whether such integration is possible when the multi-modal communication information is produced by a humanoid robot, and whether it is as efficient as for human communicators, are questions that need to be answered to further understanding of the efficacy of humanoid robots for naturalistic human-like communication.

Co-verbal gestures are an important part of human communication, improving its efficiency for information conveyance. A key component of such improvement is the observer's ability to integrate information from the two communication channels, speech and gesture. Whether such integration also occurs when the multi-modal communication information is produced by a humanoid robot, and whether it is as efficient as for a human communicator, is an open question. Here, we present an experiment which, using a fully within subjects design, shows that for a range of iconic gestures, speech and gesture integration occurs with similar efficiency for human and for robot communicators. The gestures for this study were produced on an Aldebaran Robotics NAO robot platform with a Kinect based tele-operation system. We also show that our system is able to produce a range of iconic gestures that are understood by participants in unimodal (gesture only) communication, as well as being efficiently integrated with speech. Hence, we demonstrate the utility of iconic gestures for robotic communicators.

The success of the human-robot co-worker team in a flexible manufacturing environment where robots learn from demonstration heavily relies on the correct and safe operation of the robot. How this can be achieved is a challenge that requires addressing both technical as well as human-centric research questions. In this paper we discuss the state of the art in safety assurance, existing as well as emerging standards in this area, and the need for new approaches to safety assurance in the context of learning machines. We then focus on robotic learning from demonstration, the challenges these techniques pose to safety assurance and outline opportunities to integrate safety considerations into algorithms “by design”. Finally, from a human-centric perspective, we stipulate that, to achieve high levels of safety and ultimately trust, the robotic co-worker must meet the innate expectations of the humans it works with. It is our aim to stimulate a discussion focused on the safety aspects of human-in-the-loop robotics, and to foster multidisciplinary collaboration to address the research challenges identified.

The development of trustworthy human-assistive robots is a challenge that goes beyond the traditional boundaries of engineering. Essential components of trustworthiness are safety, predictability and usefulness. In this paper we demonstrate that the integration of joint action understanding from human-human interaction into the human-robot context can significantly improve the success rate of robot-to-human object handover tasks. We take a two layer approach. The first layer handles the physical aspects of the handover. The robot's decision to release the object is informed by a Hidden Markov Model that estimates the state of the handover. Inspired by human-human handover observations, we then introduce a higher-level cognitive layer that models behaviour characteristic for a human user in a handover situation. In particular, we focus on the inclusion of eye gaze / head orientation into the robot's decision making. Our results demonstrate that by integrating these non-verbal cues the success rate of robot-to-human handovers can be significantly improved, resulting in a more robust and therefore safer system.