Italy

With the increasing use of social robots and automated machines in our daily lives, roboticists need to design robots that are suitable for human-robot collaboration. Prior work suggests that robots that are perceived to be intentional (i.e., are able to experience mental life capacities), can, in most cases, positively affect human-robot collaboration. With studies highlighting the importance of individual differences and how they drive our perception. We aimed to investigate how individual differences in gender moderate the relationship between subjective perceptions of robots and behavioral performance in a human-robot collaborative task. Participants rated a humanoid robot (i.e., iCub) on whether it can experience mental life capacities and completed a collaborative task with it. We correlated their subjective ratings with the completion time of the collaborative task and found a positive correlation between perceiving iCub to experience basic and social emotion with their performance (i.e., movement times). This relationship, however, was evident for males but not females. The results of this study suggest that perceiving humanoid robots as capable of experiencing mental states influences collaborative performance differently depending on gender. These findings can be relevant for the field of social robotics and to successfully design robot interaction partners for workplaces.

IEEE RO-MAN 2022

Expectations about mental states of a robot influence performance in a collaborative task with a robot for males but not females

**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.
 
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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.

technical paper

Recent developments in explainable artificial intelligence promise the potential to transform human-robot interaction: Explanations of robot decisions could affect user perceptions, justify their reliability, and increase trust. However, the effects on human perceptions of robots that explain their decisions have not been studied thoroughly. To analyze the effect of explainable robots, we conduct a study in which two simulated robots play a competitive board game. While one robot explains its moves, the other robot only announces them. Providing explanations for its actions was not sufficient to change the perceived competence, intelligence, likeability or safety ratings of the robot. However, the results show that the robot that explains its moves is perceived as more lively and human-like. This study demonstrates the need for and potential of explainable human-robot interaction and the wider assessment of its effects as a novel research direction.

Task-Oriented Robot-to-Human Handovers in Collaborative Tool-Use Tasks


This paper presents the design, implementation, and evaluation of a novel collaborative educational game titled “Land of Hands”, involving children and a customized social robot that we designed (HakshE). Through this gaming platform, we aim to teach proper hand hygiene practices to children and explore the extent of interactions that take place between a pro-social robot and children in such a setting. We blended gamification with Computers are Social Actors (CASA) paradigm to model the robot as a social actor or a fellow player in the game. The game was developed using Godot’s 2D engine and Alice 3. In this study, 32 participants played the game online through a video teleconferencing platform Zoom. To understand the influence a pro-social robot’s nudges have on children’s interactions, we split our study into two conditions: With-Nudges and Without-Nudges. Detailed analysis of rubrics and video analyses of children’s interactions show that our platform helped children learn good hand hygiene practices. We also found that using a pro-social robot creates enjoyable interactions and greater social engagement between the children and the robot although learning itself wasn’t influenced by the pro-sociality of the robot.

An AI-powered Hierarchical Communication Framework for Robust Human-Robot Collaboration in Industrial Settings

With recent advancements in AI, there are growing concerns about human biases implemented in AI decisions. Threats posed by AI bias may be even more drastic when applied to robots that are perceived as independent entities and are not mediated by humans. Furthermore, technology is typically perceived as objective and there is a risk that people will embrace its decisions without considering possible biases. In order to understand the extent of threats brought about by such biases, we evaluated participants' responses to a gender-biased robot mediating a debate between two participants (male and female). The vast majority of participants did not associate the robot's behavior with a bias, despite being informed that the robot's algorithm is based on human examples. Participants attributed the robot's decisions to their own performance and used explanations involving gender stereotypes. Our findings suggest that robots' biased behaviors can serve as validation for common human stereotypes. 


Questioning Wizard of Oz: Effects of Revealing the Wizard behind the Robot

When we approach a group, there is an exchange
of a multitude of verbal or non-verbal social signals to indicate
that we are looking to interact. We continue to share these
signals throughout the interaction to portray our thoughts and
motivations. We define an interaction by the signals we send;
sending different signals evokes a different response. Giving
social robots the knowledge of group social interaction, they
will have the ability to more effectively participate in these
interactions in the real world. In this paper, we present the
results from an online data collection study looking at social
group dynamics. We collected a dataset of social behaviours in
a group using a socially interactive game played online by 88
participants. We also introduce a novel visual social engagement
metric, which is derived from two social signals: proxemics
(distance between interaction participants) and mutual gaze.
We propose a mathematical formula of both mutual gaze as
the product of the mutual distances to the optical axis, and the
visual social engagement as mutual gaze divided by distance
between participants. Additionally, we investigate the influence
of personality traits on the resulting interaction patterns. Using
the metric, we create unique interaction profiles which suggest
that participants have an interaction ’style’. No clear correlation
between personality and interaction patterns was found.

Let Me Introduce Myself – Using Self-Disclosure as a Social Cue for Health Care Robots

While existing approaches to human-robot collaboration typically focus on how to build robots that can work safely and fluently with humans on collaborative tasks, our research focuses on how people experience interaction with a robot and interpret its behaviour as cooperative or uncooperative. A microsociological theory was used to analyse the process of interaction as it unfolds, aiming to examine human perception of the cooperativeness and uncooperativeness of a robot and identify the boundary between them in the context of human-robot collaboration. Our hypothesis was that an unexpected robot movement during human-robot interaction will cause a negative perception of uncooperativeness. An experiment where the interaction was ‘disrupted’ by the robot’s movement during a collaborative task was conducted with 21 participants. Our findings, obtained through qualitative analysis based on semi-structured interviews and observations, show that the disruption leads certainly to a negative perception of the robot. The perception of robot cooperativeness or uncooperativeness, however, includes complex processes, and its boundary is not rigid, but flexible and nuanced.

Real-world validation study of daily activity detection for the elderly

Trust is vital for effective human-robot teams. Trust is unstable, however, and it changes over time, with decreases in trust occurring when robots make mistakes. In such cases, certain strategies identified in the human–human literature can be deployed to repair trust, including apologies, denials, explanations, and promises. Whether these strategies work in the human robot domain, however, remains largely unknown. This is primarily because of the fragmented and dispersed state of the current literature on trust repair in HRI. As a result, this paper brings together studies on trust repair in HRI and presents a more cohesive view of when apologies, denials, explanations, and promises have been seen to repair trust. In doing so, this paper also highlights possible gaps and proposes future work. This contributes to the literature in several ways but primarily provides a starting point for future research and recommendations for studies seeking to determine how trust can be repaired in HRI.

A mixed capability-based and optimization methodology for human-robot task allocation and scheduling

Students with a learning disability (LD) generally require supplementary one-to-one instruction and support to acquire the foundational academic skills learned at school. Because learning is more difficult for students with LD, students can frequently display off-task behaviours to avoid attempting or completing challenging learning tasks. Re-directing students back to their learning task is a frequent strategy used by educators to support students. However, there have been limited studies investigating the use of assistive technology to support student re-direction, specifically in a ``real-world'' educational setting. In this in situ study, we investigate the impact of integrating socially assistive robot to provide re-direction strategies to students. A social robot, QT, was employed within the existing learning program during one-to-one remedial instruction sessions. The study comprised two phases, ``Instruction as usual" (IAU) and ``Robot-mediated instructions" (RMI). Both followed the students' one-to-one instructional program where students get personalised learning support from their instructors, except for the RMI phase which included a social robot as a tool. We investigated the impact of the robot on students' on-task behaviours and progress towards learning goals. The results of our mixed method analysis suggest that the robotic intervention supported students in staying on-task and completing their learning goal.

Reimagining RViz: Multidimensional Augmented Reality Robot Signal Design

Bidirectional object handover between a human and a robot enables an important functionality skill in robotic human-centered manufacturing or services. The problem in achieving this skill lies in the capacity of any solution to deal with three important aspects: (i) synchronized timing for the handing over phases; (ii) the handling of object pose constraints; and (iii) understanding the haptic exchanging to seamlessly achieve some steps of the (i). We propose a new approach for (i) and (ii) consisting in explicitly formulating the handover process as constraints in a task-space quadratic programming control framework to achieve implicit time and trajectory encounters. Our method is implemented on Panda robotic arm taking objects from a human operator.

Towards Transferring Human Preferences from Canonical to Actual Assembly Tasks

Study reproducibility and generalizability of results to broadly inclusive populations is crucial in any research. Previous meta-analyses in HRI have focused on the consistency of reported information from papers in various categories. However, members of the HRI community have noted that much of the information needed for reproducible and generalizable studies is not found in published papers. We address this issue by surveying the reported study metadata over the main proceedings of the 2021 IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) and the past three years (2019 through 2021) of the main proceedings of the International Conference on HumanRobot Interaction (HRI) and alt.HRI. Based on the analysis results, we propose a set of recommendations for the HRI community that follow the longer-standing reporting guidelines from human-computer interaction (HCI), psychology, and other fields most related to HRI. Finally, we examine two key areas for user study reproducibility: recruitment details and participant compensation. We find a lack of reporting of both of these study metadata categories: of the 416 studies across both conferences and all years, 258 studies failed to report recruitment method and 255 studies failed to report compensation. This work provides guidance about specific types of needed reporting improvements for the field of HRI.

Task Selection and Planning in Human-Robot Collaborative Processes: To be a Leader or a Follower?

We are approaching a future where social robots will progressively become widespread in many aspects of our daily lives, including education, healthcare, work, and personal use. All such practical applications require that humans and robots collaborate in human environments, where social interaction is unavoidable. Along with verbal communication, successful social interaction is closely coupled with nonverbal communication. Humans perform nonverbal communication in an instinctive and adaptive way, with no effort. For robots to be successful in our social landscape, they should therefore engage in social interactions in a human-like manner, with increasing levels of autonomy. Understanding human-human interaction is therefore the most natural guide to designing human-robot interaction interfaces that can be usable and understandable by everyone. The key aim of this special session is to bring forth efforts to understand mechanisms underlying human-human nonverbal communication and introduce novel approaches to the design, development, and evaluation of robotic platforms inspired and driven by those mechanisms. This special session will bring together researchers with diverse backgrounds, ranging from social psychology, cognitive science, machine learning, computer vision, and human-robot interaction, to share innovative ideas, findings, and challenges related to nonverbal communication skills in humans and robots. The special session will foster multidisciplinary discussions among academic and industrial researchers towards improving robots’ nonverbal communication skills and enhancing human trust and acceptance of robots.

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Expectations about mental states of a robot influence performance in a collaborative task with a robot for males but not females

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