Italy

IEEE RO-MAN 2022

Robots as Mirrors of Human Nature: How to Create Human-Robot Interactions That Bring Out Our Best, Not Our Worst

**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.

keynote

The increasing growth of robots and other technological devices in homes makes it critical to understand child-device interactions within the home, especially given the real possibility of child aggression towards these devices. 
To explore factors that currently and will, in the future, shape child-robot interaction in the home related to children's aggressive behavior, we conducted a 2 x 3 x 3 between-subjects crowdsourced study (N = 332) that examined how parents would respond and perceive their child interacting with different technological devices. Participants were shown a video clip of a person interacting with a technological device (robot, smart speaker, or tablet), exhibiting either aggressive or neutral behavior, and interacting with the device in one of three interaction modalities (audio, physical, or audio+physical). Imagining that the person in the video was their child, parents who observed aggressive behavior compared with neutral behavior indicated greater concern, a higher likelihood to intervene, distinct intervention methods, a higher perception of device mistreatment, and greater sympathy for the device. Despite hypothesizing that the robot would be seen as the most anthropomorphic, animate and, warm device, participant ratings of the robot were no different than the smart speaker, however, both devices were rated more highly on those dimensions than the tablet.

The Effects of Interaction Strategy and Robot Intent on Shopping Behavior

Human-Robot Interfaces (HRIs) represent a crucial
component in telerobotic systems. Body-Machine Interfaces
(BoMIs) based on body motion can feel more intuitive than
standard HRIs for naive users as they leverage humans’ natural
control capability over their movements. Among the different
methods used to map human gestures into robot commands,
data-driven approaches select a set of body segments and
transform their motion into commands for the robot based
on the users’ spontaneous motion patterns. Despite being a
versatile and generic method, there is no scientific evidence
that implementing an interface based on spontaneous motion
maximizes its effectiveness. In this study, we compare a set
of BoMIs based on different body segments to investigate this
aspect. We evaluate the interfaces in a teleoperation task of a
fixed-wing drone and observe users’ performance and feedback.
To this aim, we use a framework that allows a user to control
the drone with a single Inertial Measurement Unit (IMU) and
without prior instructions. All the interfaces are entirely data-driven
and depend on the user’s spontaneous motion. We show
through a user study that selecting the body segment for a
BoMI based on spontaneous motion can lead to sub-optimal
performance. Based on our findings, we suggest additional
metrics based on biomechanical and behavioral factors that
might improve data-driven methods for the design of HRIs.

Parental Attitudes, Trust, and Comfort with Using Robots for Providing Care to Children with Developmental Disabilities

Using robot manipulators in contexts where it is undesirable or impractical for humans to physically intervene is crucial for several applications, from manufacturing to extreme environments. However, robots require a high degree of intelligence to operate in those environments, especially if they are not fully structured. 
Teleoperation compensates for this limitation by connecting the human operator to the robot using human-robot interfaces. The remotely operated sessions can also be used as demonstrations to program more powerful autonomous agents.
In this article, we report a thorough user study to characterise the effect of simple vibrotactile feedback on the performance and cognitive load of the human user in performing teleoperated. grasping and manipulation tasks. The experiments are performed using a portable and affordable bilateral teleoperation system that we designed, composed of a Leap Motion sensor and a custom-designed vibrotactile haptic glove to operate a 4-fingered robot hand equipped with 3-axis force sensors on the fingertips; the software packages we developed are open-source and
publicly available. Our results show that vibrotactile feedback improves teleoperation and reduces cognitive load, especially for complex in-hand manipulation tasks.

Creating Informative and Successful Human Robot Interaction in a Real-World Service Environment

In order to test how explanations affect a user working together with a collaborative robot, we created a test scenario where a user sorts trash together with a collaborative robot. Sometimes the robot is not able to fulfill its part of the task. Different modalities (textual, graphical, both) for explaining this error to the user are tested in a between subjects design and the effects on task performance, cognitive load and trust are analyzed.

Giving Social Robots a Conversational Memory for Motivational Experience Sharing

In health care, social care robots might approach humans more closely and sensitively than ever before. It is, therefore, essential to ensure that patients trust and accept them. Since hospital stays often offer only little time to gain experience interacting with a robot, it is important to identify strategies to strengthen the building of trust right from the first contact. One construct promoting trust in human-human interaction is self-disclosure. The aim of this study is to examine whether it also has a trust-promoting effect in human-robot interaction. Therefore, a study was conducted where subjects experienced a hospital scenario: A social care robot introduced itself, either just listing its tasks or additionally sharing personal information about itself. It was investigated whether a self-disclosing care robot is perceived as more trustworthy, anthropomorphic, likeable, and perceived as mindful, as well as more likely to be accepted than a robot that did not do so. A content analysis of the reasons for acceptance was conducted, as well as a semantic analysis of the adjectives used to describe the robot. 
The quantitative analysis revealed no significant difference between the introductions for the variables trust, acceptance, anthropomorphism, likeability, and mind perception. Descriptive data indicate a possible positive impact of self-disclosure on acceptance and likeability. In the self-disclosure condition, trust in technology was most often cited as a reason for accepting the robot. In the low self-disclosure condition, concerns about possible errors of the robot were mentioned most often. The most frequent reason for acceptance was a potential relief for nursing staff.

Exploring the influence of culture and gender on older adults’ perception of polite robots

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.

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

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.

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Robots as Mirrors of Human Nature: How to Create Human-Robot Interactions That Bring Out Our Best, Not Our Worst

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