Italy

Social isolation is an issue that effects many people, especially those from ethic minorities, LGBTQIA+ communities, the elderly, those in long-term healthcare, and those living with life-limiting illnesses. It has become increasingly evident during the pandemic, when mental health issues have soared, and the importance of interacting with loved ones has been highlighted. While telecommunication software helped a great deal in these unprecedented circumstances, it does not allow for navigation in remote environments, and lacks high level interactions found in face-to-face communication. Therefore, this system has been developed to address these issues, and this study was being conducted to test the technical usability of the system when being used by healthy participants. It was found that 720p is the highest resolution that can be applied before the camera delay becomes unusable; though participants suggested that they would like the option to switch to 2k resolution should they be looking close up without moving. In addition, it became apparent that overall the participants were positive about the system, but would prefer a less bulky head-mounted display, and that the choice of which robot to use with the system (Nao or Pepper) was entirely down to individual preference based on the task being completed.

IEEE RO-MAN 2022

Effects of a Co-Located Robot and Anthropomorphism on Human Motivation and Emotion across Personality and Gender

**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

Human-robot interaction (HRI) requires quantitative models of proxemics and trust for robots to use in negotiating with people for space. Hall’s theory of proxemics has been used for decades to describe social interaction distances but has lacked detailed quantitative models and generative explanations to apply to these cases. In the limited case of 
autonomous vehicle interactions with pedestrians crossing a road, a recent model has explained the quantitative sizes of Hall’s distances to 4% error and their links to the concept of trust in human interactions. The present study extends this model by generalising several of its assumptions to cover further cases including human-human and human-robot interactions. It tightens the explanations of Hall zones from 4% to 1% error and fits several more recent empirical HRI results. This may help to further unify these disparate fields and quantify them to a level which enables real-world perational HRI applications.

Quantum RoboSound: Auditory Feedback of a Quantum-Driven Robotic Swarm

In music education, staying motivated has always been a crucial goal, especially for children. Earlier research has demonstrated how social robots could help improve motivation and increase the performance of children practicing music pieces. As important stakeholders, the attitudes and expectations of parents and music teachers are important factors that affect children’s and their own acceptance of applying social robots in music education. In this study, a survey was created and used to assess parents’ and music teachers’ attitudes, expectations, and intentions related to using social robots in music education. The survey results suggested that parents and music teachers expect robots to act as assistive and motivating agents, give positive feedback, and schedule and structure practice sessions. Moreover, parents expect a social robot to evaluate student performance, while music teachers focus more on positive encouragement. The biggest concerns of these stakeholders are whether the robots can convey the emotional aspects of music and demonstrate how to play the instrument. The intention of using robots is positively correlated with positive attitudes toward robots in general and the positive attitude toward robots specifically in the context of music education. The current study contributes insights into parents’ and music teachers’ attitudes and expectations as determinants of their acceptance of social robots in music education and provides a method for measuring these attitudes and expectations.

Activity Recognition in Restaurants to Address Underlying Needs: A Case Study

In Minimal Invasive Surgical procedures, flexible bevel tip needles are widely used for percutaneous intervention
due to the advantage of enhancing the target reaching accuracy. Here, the target reaching accuracy suffers due to tissue in-homogeneity, deformation in tissue domain and improper steering techniques. The main objective of the percutaneous intervention procedure is ensuring patient safety and reaching
desired target position accurately. Several researchers have already developed many approaches to control the needle steering for precise target reaching. To overcome complex
approaches in existing controllers, we have proposed a fuzzy based controller to regulate the needle in a specified plane. Our designed method involves the needle non-holonomic constraints
based kinematics inside tissue domain and Lyapunov analysis based fuzzy rule base for fuzzy inference system which ensures the closed loop stability of needling system for percutaneous
intervention procedure. We have also validated our designed control scheme through extensive simulations and experimentations in biological tissue.


Using the Price Sensitivity Meter to Measure the Value of Transformative Robot Sound

When navigating in a shared environment, the extent to which robots are able to effectively use signals for coordinating with human behaviors can ameliorate dissatisfaction and increase acceptance. In this paper, we present an online video study to investigate whether familiar acoustic signals can improve the legibility of a robot's navigation behavior. We collected the responses of 120 participants to evaluate their perceptions of a robot that communicates with one of the three used non-verbal navigational cues (an acoustic signal, an acoustic in pair with a visual signal, and a dissimilar frequency acoustic signal). Our results showed a significant legibility improvement when the robot used both light and acoustic signals to communicate its intentions compared to using only the same acoustic sound. Additionally, our findings highlighted that people also perceived differently the robot's intentions when they were expressed through two frequencies of the mere sound. The results of this work suggest a paradigm that can help the development of mobile service robots in public spaces.

Two dimensions of the intersemiotic legal translation in digital environments. Programming legal acts as a resemiotization process.

Robotic comedians (and social robots generally) need to recognize and adapt to human responses during playful
dialog. To support this ability, we determined design guidelines
via a survey of 20 human comedians and developed a machine learning pipeline to support comedian-like behaviors by our robotic system. Based on comedian input, we identified that discerning laughter vs. no laughter during a joke setup and big laugh vs. so-so response vs. no laugh after a punchline were important skills for a comedian. To enable these abilities in a robotic system, we used an existing dataset of robot comedy performance audio to train classifiers for audience
responses during the setup and after the punchline of jokes. Top-performing models for the above types of discernment performed similarly to human raters who completed the same
classification task. Comparison of the current results to our past efforts of a similar nature reveal repeatability of top-performing approaches and generalizability of the approaches to new parts of robot comedy routines. The social intelligence supported by this work can promote the likability and acceptance of robots.

How long does it take to learn trimanipulation?

Face spoofing detection could be used in conjunction with face recognition or facial expression recognition in social robots for better interaction with the users. Recent works have employed thermal image-based spoofing detection due to its competitive spoofing detection performance. However, existing methods utilized indoor thermal images which put a limit on using thermal image-based spoofing detection in outdoor environments. Moreover, several thermal image-based spoofing detection datasets consist of facial images at a frontal viewpoint, which would be put a limit on the usage in social robots with short heights. In this paper, we present our novel thermal image dataset for spoofing detection in outdoor environments. Unlike most existing datasets, our dataset is collected in outdoor environments with the camera facing the upper view to simulate the viewpoint of a social robot. To cover diverse outdoor environments, the dataset was collected at varying locations, times, and weather. Furthermore, we trained an event recognition-based spoofing detection on our collected dataset and also have provided benchmarking results for several baselines. Our spoofing detection method based on MobileNetV2 yielded a spoofing detection accuracy of 96.04\% and GFLOPs of 0.88. Our dataset will be open-sourced for users pursuing academic research and will be available upon request to the authors.

Towards evaluating the impact of swarm robotic control strategy on operators' cognitive load

Robots are becoming increasingly prominent in the entertainment sphere, where they interact with guests in themed environments to tell stories, often in place of human characters. To evaluate the potential benefits of robots in these contexts compared to humans, we created an interactive puzzle game where either a robot or a human actor serves as a diegetic "game guide" character that is both a cooperative partner and an omniscient game master. In the game, participants solve a crime mystery by asking the game guide for information to complete tasks and for hints to solve puzzles. We conducted a between-subjects study (n = 42) to investigate how players' game experiences differed when the game guide was a human compared to an embodied robot. Our results show that participants playing with a robot had more fun, felt less judged, and felt more connected with the robot while solving tasks compared to those playing with a human. These results suggest that robots can be effective alternatives to human actors in broader immersive entertainment contexts such as escape rooms to provide greater enjoyment and promote more social interaction with in-game characters.

Talk to Kotaro: a web crowdsourcing study on the impact of phoneme and prosody choice for synthesized speech on human impression

The use of multi-robot systems is increasing in
disaster response, industry, transport, and logistics. Humans
will remain indispensable to control and manage these fleets
of robots, particularly in safety-critical applications. However,
a human operator’s cognitive capacities can be challenged and
exceeded as the sizes of autonomous fleets grow, and more
sophisticated AI techniques can lead to opaque robot control
programs. In a user study (n = 40), we explore how autonomous
swarm intelligence algorithms and novel tangible interaction
modalities relate to subjective and physiological indices of
operator cognitive load (i.e., NASA Task Load Index, heart
rate variability). Our findings suggest that there are differences
in workload across conditions; however, subjective and cardiac
measures appear to be sensitive to different aspects of cognitive
state. The results hint at the potential of both tangible interfaces
and automation to engage operators and reduce cognitive load,
yet show the need for further validation of workload measures
for use in studying and optimizing human-swarm interactions.

Examining Audio Communication Mechanisms for Supervising Fleets of Agricultural Robots

During the COVID-19 pandemic, many research areas that require in person experiments with human volunteers have been impacted due to lockdowns and other activity-restricting policies. The field of robotics is no exception, and specially human-robot interaction research has been severely impacted. In order to circumvent the difficulty of gathering volunteers in person to interact with a robot, we have decided to build a novel crowdsourcing web platform for hosting our "Talk to Kotaro" experiment. The experiment consists of volunteers talking to a robot avatar and reacting to its semantic-free utterances. The developed web platform, which was built using the Python Flask framework, allows for such interactions while recording audio and video and other relevant data, which will be used for studying human impression estimation on gibberish speech. This paper describes not only the experiment and its preliminary results, but the developed platform itself; such tool is essential during pandemics and very useful for regular times, because it enables crowdsourcing data from all over the world.

How a Social Robot's Vocalization Affects Children's Speech, Learning, and Interaction

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 Human-Robot 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.

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Quantum RoboSound: Auditory Feedback of a Quantum-Driven Robotic Swarm

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