Italy

This study explored the culture and gender differences among older adults interacting with polite non-humanoid robots. We have used Lakoff theory for the polite conversational maxims. A within-subjects experiment was designed with polite and non-polite, correct and erroneous behaving robots. The polite robot employs three sub-rules (“don’t impose,” “give options,” and “be friendly”). A user study was conducted with older adults from two cultural backgrounds: Israeli Jewish and Arab participants. The study revealed that the participants could not differentiate between the polite behaviors when the robot was correct. They were more annoyed with a polite robot making an error as compared to a non-polite erroneous robot. Whereas gender had no impact on participants’ evaluations, there were significant cultural differences between the groups: Although the Arab participants were primarily more skeptical, they enjoyed, were satisfied, and trusted the robot more than the Jewish participants. In addition, they preferred the direct non-polite behavior, whereas the Jewish participants liked the polite behavior more.

IEEE RO-MAN 2022

A Non-Humanoid Robotic Object for Providing a Sense Of Security

**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

Having a sense of security is considered a basic
human emotional need. It increases confidence, encourages exploration,
and enhances relationships with others. In this study
we tested the possibility of leveraging the interaction with a
simple non-humanoid robot for increasing participants’ sense of
security. The robotic behavior was designed with a psychology
expert in attachment theory and was translated into the robot’s
morphology by an animator. Specifically, the robot was designed
to be attentive and responsive using lean, gaze and nodding
gestures. We compared participants’ experience in the secure
condition to the experience of participants who interacted with
a non-responsive robot. We further compared the participants’
implicit sense of security between the robotic conditions and
an additional baseline condition in which participants did not
interact with the robot. Our findings indicate the potential in
leveraging a simple non-humanoid robot for enhancing humans’
sense of security.

Robot Self-defense: Robots Can Use Force on Human Attackers to Defend Victims

Gaze cues play an important role in human communication and are used to coordinate turn-taking and joint attention, as well as to regulate intimacy. In order to have fluent conversations with people, social robots need to exhibit humanlike gaze behavior. Previous Gaze Control Systems (GCS) in HRI have automated robot gaze using data-driven or heuristic approaches. However, these systems tend to be mainly reactive in nature. Planning the robot gaze ahead of time could help in achieving more realistic gaze behavior and better eye-head coordination. In this paper, we propose and implement a novel planning-based GCS. We evaluate our system in a comparative within-subjects user study (N=26) between a reactive system and our proposed system. The results show that the users preferred the proposed system and that it was significantly more interpretable and better at regulating intimacy.

Eye Design of Social Robots Inspired by the Difference of Gaze Clarity in Canid Species

The supervision component is the binder of a robotic architecture. Without it, there is no task, no interaction happening, it conducts the other components of the
architecture towards the achievement of a goal, which means, in the context of a collaboration with a human, to bring changes in the physical environment and to update the human partner mental state. However, not so much work focus
on this component in charge of the robot decision-making and control, whereas this is the robot puppeteer. Most often, either tasks are simply scripted, or the supervisor
is built for a specific task. Thus, we propose JAHRVIS, a Joint Action-based Human-aware supeRVISor. It aims at being
task-independent while implementing a set of key joint action and collaboration mechanisms. With this contribution, we intend to move the deployment of autonomous collaborative robots forward, accompanying this paper with our open-source code.

Nothing About Us Without Us: a participatory design for an Inclusive Signing Tiago Robot

The advent of collaborative robots has revolutionised the work concept for Small Medium Enterprises (SME), introducing quick line configuration changes and allowing a human operator to work together with a robot. Several problems are still present regarding the need to reprogram the robotic collaborator. This paper exploits the concept of Virtual Reality to instruct the robotic co-worker, allowing a non-skilled operator to teach a task effortlessly and straightforwardly. Each
Skill composing the task is classified and characterised through its pre and postconditions to let the robot stop and notify the operator in case of inability to complete the scheduled action.
Moreover, the system allows the operator to interact with the robot and be proactive during the task execution.

A Literature Review of Trust Repair in HRI

In this work, we address two crucial issues that arise in the design of a human-robot collaborative station for the assembly of products: the optimal task allocation and the scheduling problem. We propose an offline method to solve in series the two mentioned issues, considering a static allocation and taking into account several features such as the minimization of postural discomfort, operation processing times, idle times and hence the total cycle time. Our methodology consists of a mixed approach that combines a capability-based method, where the agents’ capabilities are tested against a list of predefined criteria, with optimization. In particular, we formulate a modified version of the Hungarian Algorithm to solve also unbalanced assignment problems, where the number of tasks is different from the number of agents. The scheduling policy is obtained by means of a Mixed Integer Linear Programming (MILP) formulation, with a multi-objective optimization. Moreover, the concepts of operation, assembly tree and precedence graph are formalized, since they represent the inputs to our method, together with the information on the workstation layout and on the selected kind of robot. Finally, the proposed solution is applied to a case study to define the optimal task allocation and scheduling for two different workstation layouts: the results are compared and the best layout is accordingly selected.

Investigating gender-stereotyped interactions with virtual agents in public spaces

Regular exercise provides many mental and physical health benefits. However, when exercises are done incorrectly, it can lead to injuries. Because the COVID-19 pandemic made it challenging to exercise in communal spaces, the growth of virtual fitness programs was accelerated, putting people at risk of sustaining exercise-related injuries as they received little to no feedback on their exercising techniques. Co-located robots could be one potential enhancement to virtual training programs as they can cause higher learning gains, more compliance, and more enjoyment than non-co-located robots. In this study, we compare the effects of a physically present robot by having a person exercise either with a robot (robot condition) or a video of a robot displayed on a tablet (tablet condition). Participants (N=25) had an exercise system in their homes for two weeks. Participants who exercised with the co-located robot made fewer mistakes than those who exercised with the video-displayed robot. Furthermore, participants in the robot condition reported a higher fitness increase and more motivation to exercise than participants in the tablet condition.

Privacy Expectations for Human-Autonomous Vehicle Interactions

Robots operating in public spaces, such as autonomous vehicles, will necessarily collect images and other data concerning the people and vehicles in their vicinity, raising privacy concerns. Common conceptions of privacy in robotics do not include the challenges of many-to-many surveillance where fleets of many individual robots collect data on many people during operation. Technologists, legal scholars, and privacy researchers recommend such technologies fulfill the reasonable privacy expectations of society, but there is no standard method for measuring privacy expectations. We propose a method informed by Contextual Integrity Theory for identifying societal privacy expectations for autonomous vehicle-collected data and codifying the contextual expectations as norms. We present a study (n=600) that identifies twelve distinct norms, which are made up of contextual factors such as the subject of data collection and the data use. In a model for tolerance of autonomous vehicle data collection, we find that both contextual factors related to the data processing and factors related to the individual are significant predictors.

Physical Touch from a Robot Caregiver: Examining Factors that Shape Patient Experience

Collaborative storytelling has long been a goal of social robotics, however, much of this research is limited in interactivity or assumes that story content is curated. In this paper, we present a working fully-automatic collaborative storytelling robot, which can collaborate with a person to create a unique, improvised story by using a large-scale neural language model to dynamically generate continuations to a story. Because effective storytelling requires engaging the emotions of participants, we explore several modalities of procedurally-generated expressivity: 1. an expressive text-to-speech voice with several delivery styles, 2. physical and verbal reactions performed by the robot, and 3. an external display used to show instructions and graphics during storytelling.

To understand the issues associated with improvised collaborative storytelling with a social robot, we conduct an online survey and elicitation study with a group of online observers of collaborative storytelling gameplay, comparing several expressivity strategies in terms of storytelling-related characteristics, expressivity characteristics, and personality traits as measured by RoSAS. This evaluation showed that expressivity strategies using both emotive voice and performed reactions were perceived to be more competent storytellers and more strongly associated with positive personality traits.

Robot-mediated Group Instruction for Children with ASD: A Pilot Study

People often hold inaccurate mental models of robots. When such misconceptions regard a robot's perceptual capabilities, they can lead to issues with safety, privacy, and interaction efficiency. This work is the first attempt to model users' beliefs about a robot's perceptual capabilities and make plans to improve their accuracy---i.e., to perform belief repair. We designed a new domain called the Robot Olympics, implemented it as a web-based game platform for collecting data about users' beliefs, and developed an approach to estimating and influencing users' beliefs about a virtual robot in that domain. We then conducted a study that collected user behavior and belief data from 240 online participants who played the game. Results revealed shortcomings in modeling the participant's interpretations of the robot's actions, as well as the decision making process behind their own actions. The insights from this work provide recommendations for designing further studies and improving user models to support belief repair in human-robot interaction.

Influence of Animallike Affective Non-verbal Behavior on Children’s Perceptions of a Zoomorphic Robot

For a successful deployment of physical Human-Robot Cooperation (pHRC), humans need to be able to teach robots new motor skills quickly. Probabilistic movement primitives (ProMPs) are a promising method to encode a robot’s motor skills learned from human demonstrations in pHRC settings. However, most algorithms to learn ProMPs from human demonstrations operate in batch mode, which is not ideal in pHRC when we want humans and robots to work together from even the first demonstration. In this paper, we propose a new learning algorithm to learn ProMPs incrementally and continuously in pHRC settings. Our algorithm incorporates new demonstrations sequentially as they arrive, allowing humans to observe the robot’s learning progress and incrementally shape the robot’s motor skill. A built-in forgetting factor allows for corrective demonstrations resulting from the human’s learning curve or changes in task constraints. We compare the performance of our algorithm to existing batch ProMP algorithms on reference data generated from a pick-and-place task at our lab. Furthermore, we demonstrate how the forgetting factor allows us to adapt to changes in the task. The incremental learning algorithm presented in this paper has the potential to lead to a more intuitive learning progress and to establish a successful cooperation between human and robot faster than training in batch mode.

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