Real-world human-robot interactions often encompass uncertainty. This uncertainty can be handled in different ways, for example by designing robot planners to be more or less risk-tolerant. However, how users actually perceive different risk-taking behaviours in robots has yet to be described. Additionally, in the absence of guarantees on optimal robot performance, the interaction between risk and performance on user perceptions is also unclear. To address this gap, we conducted a user study with 84 participants investigating how robot performance and risk behaviour affects users' trust and risk-taking decisions. Participants collaborated with a Franka robot arm to perform a block-stacking task. We compared a robot which displays consistent but sub-optimal behaviours to a robot displaying risky but occasionally optimal behaviour. Risky robot behaviour led to higher trust than consistent behaviour when the robot was on average good at stacking blocks (high expectation), but lower trust when the robot was on average bad at stacking blocks (low expectation). Individual risk-willingness also predicted likelihood of selecting the risky robot over the consistent robot for future interactions, but only when the average expectation was low. These findings have implications for risk-aware planning and decision-making in mixed human-robot systems.

So far, research on drone failures has been mostly limited to understanding the technical causes of failures and recovery strategies. In contrast, there is little work looking at how failures of drones are perceived by users. To address this gap, we conduct a real-world study where participants experience drone failures leading to monetary loss whilst navigating a drone over an obstacle course. We tested 46 participants where they experienced both a failure and failure-free (control) interaction. Participants' trust in the drone, their enjoyment of the interaction, perceived control, and future use intentions were all negatively impacted by drone failures. However, risk-taking decisions during the interaction were not affected. These findings suggest that experiencing a failure whilst operating a drone in real-time is detrimental to participants' subjective experience of the interaction.

In recent years, the awareness of the academy around responsible research has notably increased. For instance, with advances in machine learning and artificial intelligence, recent efforts have been made to promote ethical, fair, and inclusive AI and robotics. To better understand if and to what extent HRI is incentivizing researchers to engage in responsible research, we conducted an exploratory review of the publishing guidelines for the most popular HRI conference venues. We identified 18 conferences which published at least 7 HRI papers in 2022. From these, we discuss four themes relevant to conducting responsible HRI research in line with the Responsible Research and Innovation framework: ethical and human participant considerations, transparency and reproducibility, accessibility and inclusion, and plagiarism and LLM use. We identify several gaps and room for improvement within HRI regarding responsible research. Finally, we establish a call to action to provoke conversations among HRI researchers about the importance of conducting responsible research within emerging fields like HRI.

In real-life scenarios, robots will have to make decisions that involve multiple users. The current literature does not consider scenarios where a robot interacts with users who have conflicting preferences. To address this issue, this paper proposes using the robot as a mediator. Different possible conflict resolution actions for the robot are presented, as well as the challenges and open questions arising from this proposal.

Little is known about how children perceive, trust and learn from social robots compared to humans. The goal of this study was to compare a robot and a human agent in a selective trust task across different combinations of reliability (both reliable, only human reliable, or only robot reliable). 111 children, aged 3 to 6 years, participated in an online study where they viewed videos of a human and a robot labelling both familiar and novel objects. We found that, although children preferred to endorse a novel object label from the agent who previously labelled familiar objects correctly, when both the human and the robot were reliable they were biased more towards the robot. Their social evaluations also tended much more strongly towards a general robot preference. Children's conceptualisations of the agents making a mistake also differed, such that an unreliable human was selected as doing things on purpose, but not an unreliable robot. These findings suggest that children's perceptions of a robot's reliability are separate from their evaluation of its desirability as a social interaction partner and its perceived agency. Further, they indicate that a robot making a mistake does not necessarily reduce children's desire to interact with it as a social agent.

Mentalizing, where humans infer the mental states of others, facilitates understanding and interaction in social situations. Humans also tend to adopt mentalizing strategies when interacting with robotic agents. There is an ongoing debate about how inferred mental states affect gaze following, a key component of joint attention. Although the gaze from a robot induces gaze following, the impact of mental state attribution on robotic gaze following remains unclear. To address this question, we asked forty-nine young adults to perform a gaze cueing task during which mental state attribution was manipulated as follows. Participants sat facing a robot that turned its head to the screen at its left or right. Their task was to respond to targets that appeared either at the screen the robot gazed at or at the other screen. At the baseline, the robot was positioned so that participants would perceive it as being able to see the screens. We expected faster response times to targets at the screen the robot gazed at than targets at the non-gazed screen (i.e., gaze cueing effect). In the experimental condition, the robot's line of sight was occluded by a physical barrier such that participants would perceive it as unable to see the screens. Our results revealed gaze cueing effects in both conditions although the effect was reduced in the occluded condition compared to the baseline. These results add to the expanding fields of social cognition and human-robot interaction by suggesting that mentalizing has an impact on robotic gaze following.

Several recent works in human-robot-interaction (HRI) have begun to highlight the importance of the replication crisis and open science practices for our field. Yet, suggestions and recommendations tailored to child-robot-interaction (CRI) research, which poses its own additional set of challenges, remain limited. There is also an increased need within both HRI and CRI for inter and crossdisciplinary collaborations, where input from multiple different domains can contribute to better research outcomes. Consequently, this workshop aims to facilitate discussions between researchers from diverse disciplines within CRI. The workshop will open with a panel discussion between CRI researchers from different disciplines, followed by 3-minute flash talks of the accepted submissions. The second half of the workshop will consist of breakout group discussions, where both senior and junior academics from different disciplines can share their experiences of conducting CRI research. Through this workshop, we hope to create a common ground for addressing shared challenges in CRI, as well as identify a set of possible solutions going forward.

The very first moments of co-presence, during which a robot appears to a participant for the first time, are often "off-the-record" in the data collected from human-robot experiments (video recordings, motion tracking, methodology sections, etc.). Yet, this "pre-beginning" phase, well documented in the case of human-human interactions, is not an interactional vacuum: It is where interactional work from participants can take place so the production of a first speaking turn (like greeting the robot) becomes relevant and expected. We base our analysis on an experiment that replicated the interaction opening delays sometimes observed in laboratory or "in-the-wild" human-robot interaction studies-where robots can require time before springing to life after they are in co-presence with a human. Using an ethnomethodological and multimodal conversation analytic methodology (EMCA), we identify which properties of the robot's behavior were oriented to by participants as creating the adequate conditions to produce a first greeting. Our findings highlight the importance of the state in which the robot originally appears to participants: as an immobile object or, instead, as an entity already involved in preexisting activity. Participants' orientations to the very first behaviors manifested by the robot during this "pre-beginning" phase produced a priori unpredictable sequential trajectories, which configured the timing and the manner in which the robot emerged as a social agent. We suggest that these first instants of co-presence are not peripheral issues with respect to human-robot experiments but should be thought about and designed as an integral part of those.

While we can see robots in more areas of our lives, they still make errors. One common cause of failure stems from the robot perception module when detecting objects. Allowing users to correct such errors can help improve the interaction and prevent the same errors in the future. Consequently, we investigate the effectiveness of a virtual reality (VR) framework for correcting perception errors of a Franka Panda robot. We conducted a user study with 56 participants who interacted with the robot using both VR and screen interfaces. Participants learned to collaborate with the robot faster in the VR interface compared to the screen interface. Additionally, participants found the VR interface more immersive, enjoyable, and expressed a preference for using it again. These findings suggest that VR interfaces may offer advantages over screen interfaces for human-robot interaction in erroneous environments.

Many solutions tailored for intuitive visualization or teleoperation of virtual, augmented and mixed (VAM) reality systems are not robust to robot failures, such as the inability to detect and recognize objects in the environment or planning unsafe trajectories. In this paper, we present a novel virtual reality (VR) framework where users can (i) recognize when the robot has failed to detect a realworld object, (ii) correct the error in VR, (iii) modify proposed object trajectories and, (iv) implement behaviors on a real-world robot. Finally, we propose a user study aimed at testing the efficacy of our framework. Project materials can be found in the OSF repository1.