Italy

The development of artificial intelligence has led to significant progress in activity detection; however, a proper evaluation of the activity detection performance is not guaranteed and is still lacking in real-life environments. In this study, to verify the stability and usefulness of activity detection in a real-world setting, we analyzed the activity detection performance for 40 elderly people using a human care robot.

IEEE RO-MAN 2022

Knowing Where to Look: A Planning-based Architecture to Automate the Gaze Behavior of Social Robots

**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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[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

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.

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

Learning in universities challenges students to engage in self-directed learning, which requires a high degree of self-motivation while individual support by teachers is limited. Research on social robots has already demonstrated their potential to support students in their learning process. In this paper, we focus on the benefits of adaptivity of a robotic tutor in a higher education scenario. To this end, we conducted a field study over three sessions over the course of a semester and implemented two conditions (adaptive and non-adaptive) of a robotic tutor to support students with exam preparation. After participant learned with both version in random order in the first two sessions, their preferred condition was used in the third session. Our results show that significantly more students preferred to learn with the adaptive robotic tutor. Additionally, participation resulted in significantly better exam performance compared to the average of the course. However, there was no significant difference in the learning experience such as motivation or need satisfaction between conditions.

Towards an automatic generation of natural gestures for a storyteller robot

Current research on the impact of gender appear-
ance in virtual agents and social robots highlights the danger
of transmitting and solidifying existing gender stereotypes. To
investigate gender-stereotyped interaction at public spaces in
dependency of virtual agents gender, we varied the gender of a
virtual agent at a metro station. We used an ethnographic study
approach, combining a two-day behavior observation with semi-
structured interviews with descriptive and qualitative system
log analysis of four weeks. Our results show that topics of
conversation differ in dependency of the virtual agents gender:
the male virtual agent was asked about topics such as brothels,
drugs and alcohol and insulted frequently, while the female one
was asked for relationship status or about flirting.

Familiar Acoustic Cues for Legible Service Robots

When service robots will be employed in private and public spaces, conflicts in human-robot interaction (HRI) might arise. To gain priority and continue its tasks, the robot would benefit from conflict resolution strategies (CRS) that are acceptable and effective. Previous studies have mainly investigated verbal or text-based CRS. As verbal interaction might not suit every application context or robot type, movement-based CRS were investigated. First, four possible implementations were pre-tested in an online study (N = 101). Then two CRS varying in modality (verbal vs. motoric) and assertiveness (submissive vs. dominant) were tested for acceptance and compliance in a lab study (N = 31) and compared for three robot types (humanoid, zoomorphic, and mechanoid). The verbal appeal was the most effective strategy to achieve user compliance. The motoric dominant strategy (moving back and forth) was perceived as most assertive and least polite if applied by the mechanoid cleaning robot but was not more effective than the verbal strategy. These studies provide insights into the influence of robot type on the acceptability and effectiveness of robot conflict resolution behavior depending on the modality.

Continuous and Incremental Learning in physical Human-Robot Cooperation using Probabilistic Movement Primitives (ProMPs)

Increasing demand for automation and recent intact trends have accelerated the employment of robots in manufacturing and service areas. Thus, there is a growing demand for easier and safer operation of robots, both by experts and non-professionals. This paper proposes a detachable smart
robot teaching device with collision prediction to meet such demands. The proposed device is composed of two modules. One is for easy teaching based on a 6D mouse, and the other is for safe operation by measuring the distance to the surrounding obstacles and predicting the collision situation. The teaching module is used only in a teaching phase and can be separated
afterward. A prototype is implemented, and the performance is verified through experiments with a collaborative robot.

Detachable smart teaching device for the easy and safe operation of robot manipulator

With the increasing use of social robots and automated machines in our daily lives, roboticists need to design robots that are suitable for human-robot collaboration. Prior work suggests that robots that are perceived to be intentional (i.e., are able to experience mental life capacities), can, in most cases, positively affect human-robot collaboration. With studies highlighting the importance of individual differences and how they drive our perception. We aimed to investigate how individual differences in gender moderate the relationship between subjective perceptions of robots and behavioral performance in a human-robot collaborative task. Participants rated a humanoid robot (i.e., iCub) on whether it can experience mental life capacities and completed a collaborative task with it. We correlated their subjective ratings with the completion time of the collaborative task and found a positive correlation between perceiving iCub to experience basic and social emotion with their performance (i.e., movement times). This relationship, however, was evident for males but not females. The results of this study suggest that perceiving humanoid robots as capable of experiencing mental states influences collaborative performance differently depending on gender. These findings can be relevant for the field of social robotics and to successfully design robot interaction partners for workplaces.

Expectations about mental states of a robot influence performance in a collaborative task with a robot for males but not females

Students with a learning disability (LD) generally require supplementary one-to-one instruction and support to acquire the foundational academic skills learned at school. Because learning is more difficult for students with LD, students can frequently display off-task behaviours to avoid attempting or completing challenging learning tasks. Re-directing students back to their learning task is a frequent strategy used by educators to support students. However, there have been limited studies investigating the use of assistive technology to support student re-direction, specifically in a ``real-world'' educational setting. In this in situ study, we investigate the impact of integrating socially assistive robot to provide re-direction strategies to students. A social robot, QT, was employed within the existing learning program during one-to-one remedial instruction sessions. The study comprised two phases, ``Instruction as usual" (IAU) and ``Robot-mediated instructions" (RMI). Both followed the students' one-to-one instructional program where students get personalised learning support from their instructors, except for the RMI phase which included a social robot as a tool. We investigated the impact of the robot on students' on-task behaviours and progress towards learning goals. The results of our mixed method analysis suggest that the robotic intervention supported students in staying on-task and completing their learning goal.

Reimagining RViz: Multidimensional Augmented Reality Robot Signal Design

Bidirectional object handover between a human and a robot enables an important functionality skill in robotic human-centered manufacturing or services. The problem in achieving this skill lies in the capacity of any solution to deal with three important aspects: (i) synchronized timing for the handing over phases; (ii) the handling of object pose constraints; and (iii) understanding the haptic exchanging to seamlessly achieve some steps of the (i). We propose a new approach for (i) and (ii) consisting in explicitly formulating the handover process as constraints in a task-space quadratic programming control framework to achieve implicit time and trajectory encounters. Our method is implemented on Panda robotic arm taking objects from a human operator.

Towards Transferring Human Preferences from Canonical to Actual Assembly Tasks

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 HumanRobot 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.

Task Selection and Planning in Human-Robot Collaborative Processes: To be a Leader or a Follower?

We are approaching a future where social robots will progressively become widespread in many aspects of our daily lives, including education, healthcare, work, and personal use. All such practical applications require that humans and robots collaborate in human environments, where social interaction is unavoidable. Along with verbal communication, successful social interaction is closely coupled with nonverbal communication. Humans perform nonverbal communication in an instinctive and adaptive way, with no effort. For robots to be successful in our social landscape, they should therefore engage in social interactions in a human-like manner, with increasing levels of autonomy. Understanding human-human interaction is therefore the most natural guide to designing human-robot interaction interfaces that can be usable and understandable by everyone. The key aim of this special session is to bring forth efforts to understand mechanisms underlying human-human nonverbal communication and introduce novel approaches to the design, development, and evaluation of robotic platforms inspired and driven by those mechanisms. This special session will bring together researchers with diverse backgrounds, ranging from social psychology, cognitive science, machine learning, computer vision, and human-robot interaction, to share innovative ideas, findings, and challenges related to nonverbal communication skills in humans and robots. The special session will foster multidisciplinary discussions among academic and industrial researchers towards improving robots’ nonverbal communication skills and enhancing human trust and acceptance of robots.

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Knowing Where to Look: A Planning-based Architecture to Automate the Gaze Behavior of Social Robots

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