Italy

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&#39;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&#39;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.

IEEE RO-MAN 2022

Impacts of Teaching towards Training Gesture Recognizers for 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)

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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.

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.

technical paper

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.

Simple User Assistant Driving Algorithm for Cost Effective Smart Electronic Wheelchair

Wheelchairs are among the most important equipment in terms of facilitating the lives of people with walking disabilities. In particular, the use of electric wheelchairs allows people with walking disabilities to move comfortably without excessively straining their bodies. However, in some cases, a person with a walking disability who uses an electric wheelchair may have difficulties in driving the wheelchair or may experience unintended collisions due to obstacles that are not within the line of sight. In order to prevent such negative events, it is necessary to develop a wheelchair with electric wheels that is produced at a minimum cost, can be easily sold to all kinds of people with disabilities, and will not be difficult to use. In addition, this equipment must include a system designed to provide wheelchair control and driving comfort with maximum efficiency for areas outside of the line of sight, which is one of the features required by all people with walking disabilities. This article explains the design of a prototype that can detect blind spots thanks to sensors and provide driving comfort while a wheelchair driver with walking disabilities uses it. In this paper, it is aimed to create a smart electric wheelchair that can be used by anyone with a walking disability while keeping the costs to a minimum.

On-The-Go Robot-to-Human Handovers with a Mobile Manipulator

A wider incorporation of robots into classrooms is hampered by current technological limitations on full autonomy in social robots. Automated speech recognition, for example, a key enabler for vocal communication, is still unable to perform with sufficient accuracy. Past studies have shown that humans adjust their speech patterns to accommodate less skilled interlocutors. If such a response holds in human-robot interactions as well, we may be able to exploit it to lessen the burden on social robots and enable rich, autonomous vocal communication. 
In this paper we explore whether a robot's speaking ability could have an impact on children's speech patterns, learning, and engagement by designing an interaction where a child and a robot collaborate on a Tower of Hanoi puzzle. Sixteen children aged 7-14 completed this collaborative task partnered with a social robot that communicated with either high verbal (full sentences), low verbal (short phrases or single words), or non-verbal (sound-based utterances) vocalization. While we found no significant impact on children's speech patterns or learning due to the robot's method of vocalization, children in the non-verbal condition had a significantly lower perception of the robot's intelligence along with higher rates of providing feedback and more instances of undoing its moves. This suggests that a link may exist between a robot's perceived speaking ability and children's confidence in that robot's overall intelligence and capability in a collaborative task, as well as their empathy towards a peer they perceive as less skilled in the task.

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

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