Italy

When acting together with another agent, humans form shared representations with their partner, in order to predict and adjust to their partner’s behaviors and ensure a smooth and efficient joint action. Previous work has shown that humans do not form shared representations when acting with partners that are not perceived as intentional, such as computers or robots. In the current study, we investigated whether engaging in a complementary joint action task with the humanoid robot iCub could influence the perceived intentionality of the robot. Participants completed a target tracking and detection task with iCub, in which they had to act together with the robot to achieve a shared goal. Participants’ likelihood of perceiving the robot as intentional was assessed before and after the interaction. Results showed that participants were more likely to perceive iCub as an intentional agent after they shared a task with it, suggesting that interacting with a robot to achieve a common goal promotes intentionality attribution toward the robot.

IEEE RO-MAN 2022

A Benchmark Toolkit for Collaborative Human-Robot Interaction

**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)

A registration is required to access this event page. If you have not already done so, please use the below link to register for the event. 

[Register Here](http://www.smile.unina.it/ro-man2022/welcome-to-ieee-ro-man-2022/)

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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

Autonomous vehicles have an existential communication challenge due to the lack of need for a human driver who can signal to vulnerable road users nearby about the intentions of the vehicle. This presents an opportunity for a vehicle to vulnerable road user communication system, such as for bicyclists. Enabling communication between bicyclists and autonomous vehicles will lead to an improvement of the bicyclists' safety in autonomous driving. If a bicyclist wants to pass the autonomous vehicle, the autonomous vehicle should provide feedback to the human about what it is about to do and what it would like the person to do. The user study presented in this paper investigated several possible options for an external display for effective nonverbal communication between an autonomous vehicle and a bicyclist. 
The results were compared to our recent study concerning vehicle-to-pedestrian communication. In total 208 participants were recruited for the vehicle-to-walker and vehicle-to-bicyclist feedback module studies.
The results did not show significant differences between the communication modalities presented. This paper shows and discusses differences between vehicle-to-walker and vehicle-to-bicyclist feedback modules. It is plausible to use the same combination of interaction modes, symbols and text, as for the vehicle-to-pedestrian communication feedback module due to economic reasons.
This study shows the necessity for more immersive environments to study vehicle to bicyclist communication needs in more detail.

Measuring Visual Social Engagement from Proxemics and Gaze

Drowsiness is one of the major causes of accidents in
driving and other tasks. Therefore, finding effective ways
to prevent drowsiness and to maintain their alert state is
an important subject of study in human-machine systems such
as autonomous driving. 
In this paper, we show the potential to use a social robot
to prevent participants from becoming drowsy and to keep
them alert. Twenty-five participants were asked to perform
Sustained Attention to Response Tasks (SART) and report
their subjective drowsiness levels. When the system
detected drowsiness from the task reaction time or the
self-reports, one of the following awakening alarms was
triggered: (1) sound and the robot's movement (SRM), (2)
sound from (motionless) robot (SR), (3) sound only (no
robot present) (SO), or (4) no stimulus (NS), as a control.
The participants' task performance and self-reported
drowsiness were continuously recorded to evaluate the
effectiveness of each alarm condition. The experimental
results showed a significant difference in the
self-reported scores of drowsiness between the SRM and SR
conditions and the NS condition, while significance was not
found between the SO and NS conditions. In addition, the
response time was shorter for SRM. The difference between
SR and SO is only the presence of the social robot, so
these results indicate that the presence of the social
robot increases the participants’ alertness level.

AugRE: Augmented Robot Environment to Facilitate Human-Robot Teaming and Communication

Socially assistive robots (SAR) are conceived to improve the quality of life by better assisting people in need, when
it comes to rehabilitation, convalescence, learning etc. In this
paper we explore how patients could cohabit with a socially assistive robot in rehabilitation centers. We presented a qualitative analysis of eight patients living with Pepper robot
during a week’s time. The data come from videos of daily interactions between the patients and Pepper, patients’ journal diaries and daily reports written by the medical staff. Our analysis proposes an understanding on what worked in terms of a potential relationship between patients and Pepper, while the robot was still under development. Our study shows that notions like patience, politeness, and a general positive appreciation of
the design of the robot create a social presence for the residents of the reeducation center

JAHRVIS, a Supervision System for Human-Robot Collaboration

In the context of an aging population, technological tools such as socially assistive robots (SARs) have made their appearance as tools to assist the work practices of care teams in contact with the elderly. We want to show the contribution of user tests in a Living Lab in the process of co-designing such tools. These tests allow both technical problems to be highlighted and Human-Robot Interactions (HRI) to be studied in an iterative way. We conducted tests with elderly users as well as teenagers, to analyze the strategies used by users of the system. This also allowed us to observe the first emotional reaction and the first interaction modality (tactile or vocal) provoked by robot. Based on these observations, we propose perspectives for a better HRI.

Cooperative and Uncooperative Behaviour in Task-oriented Dialogues with Social Robots

The success of the interaction between the robotics community and the users of these services is an aspect of considerable importance in the drafting of the development plan of any technology. This aspect becomes even more relevant when dealing with sensitive services and issues such as those related to interaction with specific subgroups of any population. Over the years, there have been few successes in integrating and proposing technologies related to deafness and sign language. Instead, in this paper, we propose an account of successful interaction between a signatory robot and the Italian deaf community, which occurred during the Smart City Robotics Challenge (SciRoc) 2021 competition \footnote{https://sciroc.org/}. Thanks to the use of a participatory design and the involvement of experts belonging to the deaf community from the early stages of the project, it was possible to create a technology that has achieved significant results in terms of acceptance by the community itself and could lead to significant results in the technology development as well.

An Autonomous Conversational Android that Acquires Human-Item Co-Occurrence in the Real World

While user-centered design philosophy and corresponding design recommendations are central pillars of human-robot interaction (HRI) research, the process how to move from such abstract and generalized design recommendations to concrete, context-specific design implementations remains under-researched and vague in the literature. The goal of this paper is therefore to show an approach for moving from abstract design recommendations to a concrete interface, and thus illustrates a design process that is rarely illustrated in concrete terms in HRI. This is done using a real-world use case of designing a possible user-centered interface for mobile cooperative manufacturing robots for assembly work in a medium-sized company. A study is presented to conceptualize and test a Research-through-Design approach, which combines transdisciplinary methods to determine relevant information which should be displayed on a graphical user interface (GUI) for HRI. Based on the use case, a Goal-Directed Task Analysis (GDTA) was conducted, consisting of a participatory observation and interviews with subject matter experts to analyze an assembly task from the work objective to the information units. The acquired information has been transferred to a physical model. A wireframe has been created to show how the results of the GDTA and the physical model can be applied to a GUI. The wireframe design has been evaluated through qualitative interviews with end users (n = 12) to get first estimates about its relevance. In order to validate the applied methods, design and engineering students (n = 10) repeated the process in stages followed by interviews. The results indicate that the method mix shows potential and leads to supportive user interfaces.

Learning Human Actions Semantics in Virtual Reality for a Better Human-Robot Collaboration

In this paper, we introduce Ommie, a novel robot that supports deep breathing practices for the purposes of anxiety reduction. The robot’s primary function is to guide users through a series of extended inhales, exhales, and holds by way of haptic interactions and audio cues. We present core design decisions during development, such as robot morphology and tactility, as well as the results of a usability study in collaboration with a local wellness center. Interacting with Ommie resulted in a significant reduction in STAI-6 anxiety measures, and participants found the robot intuitive, approachable, and engaging. Participants also reported feelings of focus and companionship when using the robot, often elicited by the haptic interaction. These results show promise in the robot’s capacity for supporting mental health.

Leveraging Cognitive States in Human-Robot Teaming

Human gaits constitute significant health information because they indicate body function conditions, such as sensory, motor, and cognitive functions. Monitoring gait patterns helps to find weakened body parts and to prevent diseases before they become serious. In this paper, we propose a novel method to monitor human gaits in indoor environments by using a mobile robot and artificial intelligence. A mobile robot periodically collects 3D skeleton data by using an installed depth camera, and the collected data are used to extract gait parameters and to classify normal, antalgic, stiff-legged, steppage, lurching, and Trendelenburg gaits via the proposed system. Ultrawideband (UWB)-based localization with Kalman filtering and odometry-based state estimation is adopted to allow a mobile robot to rapidly and accurately reach the target human. We use both 3D skeletons and joint-based angle data to achieve improved pathological gait classification performance. A bidirectional gated recurrent unit (Bi-GRU) architecture is adopted for fusion at the feature level. An accuracy of 97.64% is achieved by using the proposed multiple-input model, whereas 96.63% and 94.17% accuracies are achieved by the skeleton- and joint-based angle input models, respectively. The proposed automatic gait analysis method can contribute to improving untact smart home care and quality of life.

Domestic Social Robots as Companions or Assistants? The Effects of the Robot Positioning on the Consumer Purchase Intentions

The rapid spread of COVID-19 around the globe has increased the need to adopt autonomous social robots within our healthcare systems. In particular, socially assistive robots can help to improve the day-to-day functioning of our healthcare facilities including long-term care, while keeping residents and staff safe by performing repetitive tasks such as health screening. In this paper, we present the first human-robot interaction study with an autonomous multi-task socially assistive robot used for non-contact screening in long-term care homes. The robot monitors temperature, checks for face masks, and asks screening questions to minimize human-to-human contact. We investigated staff perceptions of 7 attributes: screening experience without and with the robot, efficiency, cognitive attitude, freeing up staff, safety, affective attitude, and intent to use the robot. Furthermore, we investigated the influence of demographics on these attributes. Study results show that, overall, staff rated these attributes high for the screening robot, with a statistically significant increase in cognitive attitude and safety after interacting with the robot. Differences between gender and occupation were also determined. Our study highlights the potential application of an autonomous screening robot for long-term care homes.

Tolerating Untrustworthy Robots: Studying Human Vulnerability Experience within a Privacy Scenario for Trust in Robots

Understanding the decisions of AI-driven systems and the rationale behind such decisions is key to the success of the human-robot team. However, the complexity and the ``black-box'' nature of many AI algorithms create a barrier for establishing such understanding within their human counterparts. Reinforcement Learning (RL), a machine-learning algorithm based on the simple idea of action-reward mappings, has a rich quantitative representation and a complex iterative reasoning process that present a significant obstacle to human understanding of, for example, how value functions are constructed, how the algorithms update the value functions, and how such updates impact the action/policy chosen by the robot. In this paper, we discuss our work to address this challenge by developing a decision-tree based explainable model for RL to make a robot's decision-making process more transparent. Set in a human-robot virtual teaming testbed, we conducted a study to assess the impact of the explanations, generated using decision trees, on building transparency, calibrating trust, and improving the overall human-robot team's performance. We discuss the design of the explainable model and the positive impact of the explanations on outcome measures.

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A Benchmark Toolkit for Collaborative Human-Robot Interaction

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