Italy

This article analyzes various kinds of interactional trouble occurring in task-oriented human-robot interaction. We focus on users&#39; repair strategies, typically used for maintaining communication in social interaction. We use an existing dataset consisting of 36 video-recordings of elderly people completing a lengthy health survey with a Pepper robot. Miscommunication was abundant in the data, with some types of repair being more common than others. These were mainly related to “hearing trouble” on the side of the robot, leading to participants’ repeats and rephrasings. Interactional trouble was also found to escalate, with multiple repair initiations, sometimes leading to “second best answers”. This appeared to be a strategy for participants to avoid repair in the service of progressivity. Overall, miscommunication and repair in these task-oriented human-robot interactions require substantial adaptation from the human.

IEEE RO-MAN 2022

Perception of Physical and Virtual Agents: Exploration of Factors Influencing the Acceptance of Intrusive Domestic Agents

**31st IEEE International Conference on Robot & Human Interactive Communication**
 This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.
 
 The conference covers a wide range of topics related to Robot and Human Interactive Communication, involving theories, methodologies, technologies, empirical and experimental studies. Papers related to the study of robotic technology, psychology, cognitive science, artificial intelligence, human factors, ethics and policies, interaction-based robot design and other topics related to human-robot interaction are welcome.
 
 **CONFERENCE THEME:**
 Social, asocial, and antisocial robots
 
 Robots are starting to populate our lives: at work, in our homes, hotels, shops. However, not everybody is comfortable with robots finding a place in society. For example, people may be afraid that robots will replace humans in their jobs. This belief is exacerbated by the media that, on one side, raise expectations of robots’ capabilities, while amplifying fears and worries on the other side.
 
 In the next years, due to the increasing request for interaction with people in social environments, academic research and market analyses predict that robots will become more and more “social”. But, at the same time, robots may incur the risk of being perceived as “asocial” as their pervasiveness in everyday environments may become an obstacle to social interaction between humans. Or they may even be rejected as “antisocial” if researchers do not take care to develop robots that behave according to the customs and habits of the communities where they are expected to operate.
 
 To prevent this from happening, robotic researchers can no longer take the luxury of being mere technicians. They must be capable of addressing all the challenges raised by future robot-populated communities in a multidisciplinary way. They need to share their knowledge with the public to make people aware of what robots really are, what expectations we can have from them, the risks we may incur, and the possible mitigation plans.
 
 While being open to any kind of contribution related to Human-Robot Interactive Communication, the RO-MAN 2022 conference will emphasize these aspects of robotic research. How can we create robots capable of being welcomed as our assistants and companions in society while, at the same time, not being perceived as “asocial” or “antisocial?” How can we create robots perceived as facilitators of the natural development of human beings and communities, rather than obstacles?
 
 Contributions from different disciplines, not exclusively related to technological development, will be welcomed as crucial to pave the way to global acceptance of future robots with an informed attitude.
 
 [![](https://assets.underline.io/uploads/markdown_image/1/image/af259abe105b3791c498ae1552d75710.png)](https://drive.google.com/file/d/18w73BZPB_JVOgcrcdZDqYpEupwVt4CnU/view?usp=sharing)

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31st IEEE International Conference on Robot & Human Interactive Communication
This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.

This article analyzes various kinds of interactional trouble occurring in task-oriented human-robot interaction. We focus on users' repair strategies, typically used for maintaining communication in social interaction. We use an existing dataset consisting of 36 video-recordings of elderly people completing a lengthy health survey with a Pepper robot. Miscommunication was abundant in the data, with some types of repair being more common than others. These were mainly related to “hearing trouble” on the side of the robot, leading to participants’ repeats and rephrasings. Interactional trouble was also found to escalate, with multiple repair initiations, sometimes leading to “second best answers”. This appeared to be a strategy for participants to avoid repair in the service of progressivity. Overall, miscommunication and repair in these task-oriented human-robot interactions require substantial adaptation from the human.

technical paper

We designed an observer-aware method for creating
navigation paths that simultaneously indicate a robot’s goal
while attempting to remain in view for a particular observer.
Prior art in legible motion does not account for the limited field
of view of observers, which can lead to wasted communication
efforts that are unobserved by the intended audience. Our
observer-aware legibility algorithm directly models the locations
and perspectives of observers, and places legible movements
where they can be easily seen. To explore the effectiveness of
this technique, we performed a 300-person online user study.
Users viewed first-person videos of restaurant scenes with robot
waiters moving along paths optimized for different observer
perspectives, along with a baseline path that did not take into
account any observer’s field of view. Participants were asked to
report their estimate of how likely it was the robot was heading
to their table versus the other goal table as it moved along each
path. We found that for observers with incomplete views of the
restaurant, observer-aware legibility is effective at increasing
the period of time for which observers correctly infer the goal
of the robot. Non-targeted observers have lower performance
on paths created for other observers than themselves, which
is the natural drawback of personalizing legible motion to a
particular observer. We also find that an observer’s relationship
to the environment (e.g. what is in their field of view) has
more influence on their inferences than the observer’s relative
position to the targeted observer, and discuss how this implies
knowledge of the environment is required in order to effectively
plan for multiple observers at once.

Observer-Aware Legibility for Social Navigation

One way to improve the relationship between humans and anthropomorphic agents is to have humans empathize with the agents. In this study, we focused on a task between agents and humans. We experimentally investigated hypotheses stating that task difficulty and task content facilitate human empathy. The experiment was a two-way analysis of variance (ANOVA) with four conditions: task difficulty (high, low) and task content (competitive, cooperative). The results showed no main effect for the task content factor and a significant main effect for the task difficulty factor. In addition, pre-task empathy toward the agent decreased after the task. The ANOVA showed that one category of empathy toward the agent increased when the task difficulty was higher than when it was lower. This indicated that this category of empathy was more likely to be affected by the task. The task itself used can be an important factor when manipulating each category of empathy.

"This Bot Knows What I'm Talking About!" Human-Inspired Laughter Classification Methods for Adaptive Robotic Comedians

If a robot tells you it can lie for your benefit, how
would that change how you perceive it? This paper presents a
mixed-methods empirical study that investigates how disclosure
of deceptive or honest capabilities influences the perceived social
intelligence and construal level of a robot. We first conduct
a study with 198 Mechanical Turk participants, and then a
replication of it with 15 undergraduate students in order to gain
qualitative data. Our results show that how a robot introduces
itself can have noticeable effects on how it is perceived–even
from just one exposure. In particular, when revealing having
ability to lie when it believes it is in the best interest of a
human, people noticeably find the robot to be less trustworthy
than a robot that conceals any honesty aspects or reveals total
truthfulness. Moreover, robots that are forthcoming with their
truthful abilities are seen in a lower construal than one that
is transparent about its deceptive abilities. These results add
much needed knowledge to the understudied area of robot
deception and could inform designers and policy makers of
future practices when considering deploying robots that deceive

Verbal and Non-Verbal Conflict Resolution Strategies for Service Robots

There is a growing interest in the use of robotics and AI in the nuclear industry, however it is important to ensure these systems are ethically grounded, trustworthy and safe. An emerging technique to address these concerns is the use of explainability. In this paper we present the results of an interview study with nuclear industry experts to explore the use of explainable intelligent systems within the field. We interviewed 16 participants with varying backgrounds of expertise, and presented two potential use cases for evaluation; a navigation scenario and a task scheduling scenario. Through an inductive thematic analysis we identified the aspects of a deployment that experts want to know from explainable systems and we outline how these associate with the folk conceptual theory of explanation, a framework in which people explain behaviours. We established that an intelligent system should explain its reasons for an action, its expectations of itself, changes in the environment that impact decision making, probabilities and the elements within them, safety implications and mitigation strategies, robot health and component failures during decision making in nuclear deployments. We determine that these factors could be explained with cause, reason, and enabling factor explanations.

Should AI Systems in Nuclear Facilities Explain Decisions the Way Humans Do? An Interview Study

People’s willingness to help robots has been explored in the lab and in the wild in various settings. While previous studies relied on robotic prototypes, service robots are now already deployed in public spaces. This presents a novel and exciting opportunity for human-robot interaction (HRI) scholars to study robotic technologies in the context of their deployment. In this paper, we present the qualitative methodology and the outcomes of an exploratory mixedmethods (observations, autoethnography, online content analysis) study of people voluntarily helping commercially deployed delivery robots in Tallinn, Estonia. Based on the cumulative
findings of the three methods, we discuss how spontaneous help towards robots manifested, the situational factors that may have contributed to the observed helping behaviors, and the role that the perceptions of the robots as cute and helpful may have played in these interactions. While our findings support the assumption that human help is a reasonable mitigation strategy to overcoming the challenges service robots may face in uncontrolled environments, we also emphasize the importance of considering the ethical implications when commercial technology relies in part on passersby to succeed in its tasks.

With a Little Help of Humans. An Exploratory Study of Delivery Robots Stuck in Snow

As social robots rapidly become mainstream tech- nologies, it is critical for HRI researchers and practitioners to consider their societal and ethical impacts as well as their ability to perpetuate or mitigate intersectional social inequities and hierarchies relating to race, class, gender, disability, and other social axes. Through an equity, ethics, and justice- centered audit of human-robot interaction (HRI) scholarship, we reveal how the HRI community has engaged with these topics over the past two decades. We use the five senses ethical framework that has been proposed specifically for use in HRI contexts to perform the review paired with an analysis of equity and justice. We then expand the Design Justice framework (a framework for analyzing how design impacts society and distributes benefits and burdens to society through the lenses of equity, values, scope, ownership, and accountability) to HRI contexts through the inclusion of HRI-specific topics such as autonomy, transparency, deception, and policies. We invite researchers and practitioners to explore the HRI Equitable Design framework to work towards designing equitable and inclusive HRI research studies and technologies.

Ethics, Equity, & Justice in Human-Robot Interaction: A Review and Future Directions

Socially Assistive Robots (SARs) have shown promise, but there are still practical challenges to their widespread adoption. Recent research has demonstrated the advantages of teleoperated systems in this space and called for better guidelines for teleoperation interfaces. We ran group usability tests with therapists with no experience with robots to learn more about the challenges they face. We found that robot-novice therapists understand how robots can be effective in therapy. However, learning to use a robot interface can be challenging for new users. These challenges include the unfamiliar metaphors used for robot connection and the need to create, acquire, or share robot interaction content. We also identify users’ needs that are perhaps non-obvious in a research context, such as privacy of client health information and professional boundaries with client families when using electronic tools. We make several recommendations based on analysis of our group usability tests: (1) developing dedicated interfaces for content authoring that account for caregiver technical expertise, (2) implementing content organization and sharing tools, (3) using connection metaphors that non-technical users may be more familiar with such as phone calls or web URLs, (4) considering user privacy in connection methods chosen, especially within telehealth. Most importantly, we encourage further research in SAR teleoperation that focuses on caregivers as teleoperators.

Practical Considerations for Deploying Robot Teleoperation in Therapy and Telehealth

Deploying robots with the ability to autonomously
navigate in the environment of a clinic necessitates carefully de-
signing their verbal and non-verbal behaviour in order to avoid
irritating, worrying or even endangering users, bystanders
and passersby. We describe a user-centred approach used to
design, implement and evaluate an interaction concept for
mobile robotic assistants for a clinic environment, resulting in
two robotic assistant prototypes and a generalized multimodal
interaction framework applicable to a variety of autonomously
navigating robotic agents. We present the steps of the design
process and intermediate findings, the final system components
and experiment results showing good acceptance of the inter-
action concept. A clip of the two robots in action can be found
under https://short.dfki.de/ro-man

Iterative User-Centric Development of Mobile Robotic Systems with Intuitive Multimodal Human-Robot Interaction in a Clinic Environment

Theory of Mind (ToM) is a psychological construct that captures the ability to ascribe mental states to others and then use those representations for explaining and predicting behavior. We review recent progress in endowing artificially intelligent robots with ToM. A broad array of modeling, experimental, and benchmarking approaches and methods are present in the extant literature. Unlike other domains of human cognition for which research has achieved super-human capabilities, ToM for robots lacks a unified construct and is not consistently benchmarked or validated---realities which possibly hinder progress in this domain. We argue that this is, at least in part, due to inconsistent defining of ToM, no presence of a unifying modeling construct, and the absence of a shared data resource. We believe these would improve the ability of the research community to compare the ToM abilities of different systems. We suggest that establishing a shared definition of ToM, creating a shared data resource that supports consistent benchmarking & validation, and developing a generalized modeling tool are critical steps towards giving robots ToM capabilities that lay observers will recognize as such.

Robots with Theory of Mind for Humans: A Survey

Recent advances in collaborative robots have provided an
opportunity for the close collaboration of humans and
robots in a shared workspace. To exploit this
collaboration, robots need to plan for optimal team
performance while considering human presence and
preference. This paper studies the problem of task
selection and planning in a collaborative, simulated
scenario. In contrast to existing approaches, which mainly
involve assigning tasks to agents by a task allocation unit
and informing them through a communication interface, we
give the human and robot the agency to be the leader or
follower. This allows them to select their own tasks or
even assign tasks to each other. We propose a task
selection and planning algorithm that enables the robot to
consider the human's preference to lead, as well as the
team and the human's performance, and adapts itself
accordingly by taking or giving the lead. The effectiveness
of this algorithm has been validated through a simulation
study with different combinations of human accuracy levels
and preferences for leading.

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Perception of Physical and Virtual Agents: Exploration of Factors Influencing the Acceptance of Intrusive Domestic Agents

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Observer-Aware Legibility for Social Navigation

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