Italy

Children with Autism Spectrum Disorder (ASD) often struggle with visual perspective taking (VPT) skills and the understanding that others might have viewpoints and perspectives that are different from their own; i.e., the ability to understand that two or more people looking at the same object from different positions might not see the same thing. The understanding of VPT can be improved by introducing explicit causal explanations in the interactions involving autistic children. Moreover, the use of social robots can help autistic children improve their social skills. We present a retrospective study with Kaspar, a humanoid social robot specifically designed to interact with children with ASD, which aims to define the initial protocol for a study on the effect of causal explanation in VPT provided by Kaspar. To this end, we investigate in which scenarios causal explanations, provided either by researchers or by Kaspar, contribute substantially to the child’s understanding of VPT. The results have helped us identify multiple interaction categories that benefit from causal explanation. We have used these results in order to define new interaction games that benefit from causal explanations. These are now progressing through usability assessment experiments.

IEEE RO-MAN 2022

Listen and tell me who the user is talking to: Automatic detection of the interlocutor’s type during a conversation

**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

Thailand, as well as many other countries worldwide, is facing a shortage of medical staff. We propose a solution to improve medical services in health centres: a robot teleoperation
system to allow patients to consult with doctors from public hospitals, and for doctors to examine and make decisions about their required care. To develop such a system, a user-centred design (UCD) process is followed. Here we present an important first step in this process to establish user requirements for such a system. Hence, we have conducted a focus group with Thai
medical staff from Banphaeo General Hospital and an online survey with potential patients. An online collaborative board has been setup to facilitate running the focus group virtually and provide an effective tool to gather data. A qualitative data is then analysed using a framework analysis. Based on this work, we present a list of user requirements for doctors, patients
and assistants and discuss how the collected requirements can be transferred into technical specifications of the system. Our study found that communication among different user groups is the most important requirement.

Drowsiness prevention using a social robot

Humans are highly skilled in communicating their intent for when and where a handover would occur. However, even the state-of-the-art robotic implementations for handovers typically lack of such communication skills. This study investigates visualization of the robot's internal state and intent for Human-to-Robot Handovers using Augmented Reality. Specifically, we explore the use of visualized 3D models of the object and the robotic gripper to communicate the robot's estimation of where the object is and the pose in which the robot intends to grasp the object. We tested this design via a user study with 16 participants, in which each participant handed over a cube-shaped object to the robot 12 times. Results show communicating robot intent via augmented reality substantially improves the perceived experience of the users for handovers. Results also indicate that the effectiveness of augmented reality is even more pronounced for the perceived safety and fluency of the interaction when the robot makes errors in localizing the object.

Impacts of Teaching towards Training Gesture Recognizers for Human-Robot Interaction

The clarity of the position of the eyes and pupils in canid
species varies depending on their sociality. Existing
social robots, on the other hand, have a wide range of
applications, including education, medical care, and
customer service, while most of the robots have had a
simple design of eyes with a white base and a black iris.
However, as in canid species, it is conceivable that the
level of appropriate communication intensity varies
depending on the purpose of a robot, and that there is a
suitable eye design depending on its use. In this study, we
propose the appropriate gaze clarity of a robot for three
situations where the avatar speaks to the human, the avatar
listens to the human's talk, and the avatar is nearby the
human during the task requiring concentration.

Living one week with an autonomous Pepper in a rehabilitation center: lessons from the field

The growing presence of robots in our daily life brings out the need to develop systems that are ever more user-friendly, considering users’ needs and preferences. This is necessary in particular when robots are developed to be introduced into welfare settings. For this reason, a study is proposed with the aim to investigate differently aged (young, middle-aged, and seniors) potential users’ assessment of male android robots as opposed to male virtual agents, in order to compare interactive systems characterized by different levels of embodiment. 180 participants joined the experiment, which consisted of watching video clips depicting android robots and virtual agents, and subsequently fulfilling the RAQ (Robot Acceptance Questionnaire) and the VAAQ (Virtual Agent Acceptance Questionnaire). Results highlighted substantial differences in robots and agents’ assessment, differences which seem to be affected by participants' age, as well.

Assist Effectiveness Study based on Viscosity -Comparison of Assumed Command Signal and Actual Command Signal-

Despite already being commonplace, delegation to robotic
virtual agents (VAs) is often considered challenging and
error-prone in critical situations by the general public.
Theory of mind, the human capacity to take another
person's perspective, is deemed an important enabler for
human-human cooperation. This study explores the effect of
a robotic VA's ability to use theory of mind on users'
delegation behavior. To this end, we conducted a
between-subjects experiment with participants playing the
Colored Trails game with robotic VAs of varying levels of
theory of mind. The results invalidate our hypothesis that
the ToM level is a reliable indicator of delegation
choices. Instead, we found that the participants'
performance strongly correlates with their delegatory
intentions. Therefore, to facilitate delegation, designers
of robots and robotic agents may consider refraining from
using ToM-resemblance features and focusing on balancing
user performance perception instead to induce the desired
delegation behaviors.

Humans and Robotic Arm: Laban Movement Theory to create  Emotional Connection

Social robots found their way into several economic fields of our daily life, e.g., as info points in shopping malls or hotels. However, research on robots in the tourism sector is still in its infancy. Conducting three online studies, we examined the design of robotic speech for a robotic concierge. Providing self-disclosure via speech or using local dialect did not affect robot acceptance and attitudes. In contrast, informal speech led to higher likeability and perceived warmth of the robot concierge. Overall, our results reveal positive evaluations of our robotic concierge regardless of its manipulation.

Push and Pull Feedback in Mobile Robotic Telepresence - A Telecare Case Study

Robot-mediated learning activities are often designed as collaborative exercises where children work together to achieve the activity objectives. Although miscommunications and misunderstandings occur frequently, humans, unlike robots, are very good at overcoming them and converging to a shared solution. With the aim of equipping a robot with these abilities and exploring its effects, in this article we investigate how a humanoid robot can collaborate with a human learner to construct a shared solution to a problem via suggesting actions and (dis)agreeing with each other. Concretely, we designed a learning activity aiming to improve the computational thinking skills of children, in which the robot makes suggestions on what to do, that may be in line with what the human thinks or not. Furthermore, the robot may suggest wrong actions that could essentially prevent them from finding a correct solution. Via a pilot study conducted remotely with 9 school children, we investigate whether the interaction results in positive learning outcomes, how the collaboration evolves, and how these relate to each other. The results show positive learning outcomes for the participants in terms of finding better solutions, suggesting that the collaboration with the robot might have helped trigger the learning mechanisms.

Comparison of a VR-Based and a Rule-Based Robot Control Method for Assistance in a Physical Human-Robot Collaboration Scenario

Supernumerary robotic limbs can act as intelligent prostheses or augment the motion of healthy people to achieve actions which are not possible with only two natural hands. However, as trimanual control is not typical in everyday activities, it is still unknown how different training could influence its acquisition. We conducted an experimental study to evaluate the impact of different forms of trimanual action on training. Two groups of twelve subjects were each trained in virtual reality for five weeks using either a three independent goals task or one dependent goal task. The success of their training was then evaluated by comparing their task performance and motion characteristics between sessions. The results show that subjects dramatically improved their trimanual task performance as a result of training. However, while they showed improved motion efficiency and reduced workload for tasks with multiple independent goals with practice, no such improvement was observed when they trained with the one coordinated goal task.

Instructive Interaction for Redirection of Customer Attention from Robot to Service

Collaborative robots that physically interact with humans
in an ergonomic and safe manner are essential to the future
of industry. A common task across many industrial
applications is robot-to-human handover, in which the
location of object exchange is vital in cultivating a
seamless interaction. Most prior work on computing these
exchange locations aims to adjust human posture towards a
better ergonomic state during a single handover. This
procedure typically involves the robot estimating the
human's biomechanical properties, e.g. center of mass and
base of support, before determining an optimal handover
location according to some ergonomics assessment scale. In
a similar vein, we compare two methodologies for object
handover, whereby the handover location is computed to
either "assist" or "stimulate" the human receiver. Unlike
existing approaches, we posit that improvements in human
posture can be derived by stimulating the receiver's
movement dynamics to facilitate posture variability, rather
than constrain or stabilize it. To compare methodologies,
we conduct a within-subjects study where participants
perform 78 object handovers with a collaborative robot
architecture. Our findings indicate an improvement in
ergonomics scores for the "stimulating" approach, hinting
at the importance of productive inconvenience in long-term
robot-to-human handover.

Head Pose for Object Deixis in VR-Based Human-Robot Interaction

For the most comfortable, human-aware robot navigation, subjective user preferences need to be taken into account. This paper presents a novel reinforcement learning framework to train a personalized navigation controller along with an intuitive virtual reality demonstration interface. The conducted user study provides evidence that our personalized approach significantly outperforms classical approaches with more comfortable human-robot experiences. We achieve these results using only a few demonstration trajectories from non-expert users, who predominantly appreciate the intuitive demonstration setup. As we show in the experiments, the learned controller generalizes well to states not covered in the demonstration data, while still reflecting user preferences during navigation. Finally, we transfer the navigation controller without loss in performance to a real robot.

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