Italy

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.

IEEE RO-MAN 2022

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

**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.

technical paper


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

Laboratories are being increasingly automated. In
small laboratories individual processes can be fully automated,
but this is usually not economically viable. Nevertheless, in-
dividual process steps can be performed by flexible, mobile
robots to relieve the laboratory staff. As a contribution to the
requirements in a life science laboratory the mobile, dextrous
robot Kevin was designed by the Fraunhofer IPA research
institute in Stuttgart, Germany. Kevin is a mobile service robot
which is able to fulfill non-value adding activities such as
transportation of labware. This paper gives an overview of
Kevin’s functionalities, its development process, and presents a
preliminary study on how its lights and sounds improve user
interaction.

 SM-EXO: Shape Memory alloy-based Hand EXOskeleton for Cobotic Application in Cognitive Development Investigation

Learning from demonstration (LfD) approaches
have garnered significant interest for teaching social robots a
variety of tasks in healthcare, educational, and service domains
after they have been deployed. These LfD approaches often
require a significant number of demonstrations for a robot to
learn a performant model from task demonstrations. However,
requiring non-experts to provide numerous demonstrations for
a social robot to learn a task is impractical in real-world
applications. In this paper, we propose a method to improve
the sample efficiency of existing learning from demonstration
approaches via data augmentation, dynamic experience replay
sizes, and hierarchical Deep Q-Networks (DQN). After validating
our methods on two different datasets, results suggest that our
proposed hierarchical DQN is effective for improving sample
efficiency when learning tasks from demonstration. In the future,
such a sample-efficient approach has the potential to improve
our ability to apply LfD approaches for social robots to learn
tasks in domains where demonstration data is limited, sparse,
and imbalanced.

Efficacy of a Human-Robot Collaborative Problem Solving Activity to Improve Computational Thinking Skills

Social robotics recommendations have been studied for a long time, and many existing studies have addressed in-store recommendations using robots. However, it has been pointed out that many studies conducted in “in-the-wild” field environments have only focused on the initial stages of customer purchase behavior, such as stopping and engaging in a conversation, and few have been able to induce an interest in the product and even purchase. One of the causes is that the robot itself attracts most of the customer’s attention, making it difficult for customers to be interested in the robot’s recommendations. To solve this problem, this study examines the inclusion of clear and specific instructions to customers in interactions in which the robot recommends services and products. We conducted a field experiment to confirm the effectiveness of such instructive interactions, and found that customers are more likely to be interested in the content recommended by the robot, rather than in the robot itself, through the instructive interaction.

TODS: Thermal Outdoor image Dataset for Spoofing detection

The usage of social robots in psychotherapy has gained interest in various applications. In the context of therapy for children with socio-emotional impairments, for example autism spectrum conditions, the first approaches have already been successfully evaluated in research. In this context, the robot can be seen as a tool for therapists to foster interaction with the children. To ensure a successful integration of social robots into therapy sessions, an intuitive and comprehensive interface for the therapist is needed to guarantee save and appropriate human-robot interaction. This publication addresses the development of a graphical user interface for robot-assisted therapy to train socio-emotional skills in children on the autism spectrum. The software follows a generic and modular approach. Furthermore, a robotic middleware is used to control the robot and the user interface is based on a local web application. During therapy sessions, the therapist interface is used to control the robot’s reactions and provides additional information from emotion and arousal recognition software. The approach is implemented with the humanoid robot Pepper (Softbank Robotics). A feasibility study is carried out with experts from a child and youth psychiatry to evaluate the user experience of the therapist interface. In sum, the user experience and usefulness can be rated positively.

Benchmarking deep neural networks for gesture recognition on embedded devices

Mixed human-robot teams (HRTs) have the potential to perform complex tasks by leveraging diverse and complementary capabilities within the team. However, assigning humans to operator roles in HRTs is challenging due to the significant variation in user capabilities. While much of prior work in role assignment treats humans as interchangeable (either generally or within a category), we investigate the utility of personalized models of operator capabilities based in relevant human factors in an effort to improve overall team performance. We call this approach individualized role assignment (IRA) and provide a formal definition. A key challenge for IRA is associated with the fact that factors that affect human performance are not static (e.g., one's ability to track multiple objects can change during or between tasks). Instead of relying on time-consuming and highly-intrusive measurements taken during the execution of tasks, we propose the use of short cognitive tests, taken before engaging in human-robot tasks, and predictive models of individual performance to perform IRA. Results from a comprehensive user study conclusively demonstrate that IRA leads to significantly better team performance than a baseline method that assumes human operators are interchangeable, even when we control for the influence of the robots' performance. Further, our results point to the possibility that such relative benefits of IRA will increase as the number of operators (i.e., choices) increase for a fixed number of tasks.

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