Italy

In this paper, we study how a co-located robot affects human motivation and emotion. In particular, we ex- amine the role of the co-located robot’s anthropomorphism, as well as the effects of the human’s personality and gender. To study this, we conducted an online experiment, where 182 participants completed a repetitive task, either easy or hard, in one of the four conditions: in the presence of a non-anthropomorphic robot, an anthropomorphic robot, another human, or alone. For each condition, we analyzed the number of repetitions and the total time users spent, which we treated as the proxy of their motivation, as well as their self-reported emotional states. The study results suggest that the presence of a non-anthropomorphic robot has the potential to lead to a higher level of motivation and a more desirable affective state for users than the presence of an anthropomorphic robot or another human, especially for introverts and female users during difficult tasks.

IEEE RO-MAN 2022

Norm Learning with Reward Models from Instructive and Evaluative Feedback

**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

The impact of COVID-19 has highlighted the
importance of sports and physical activity for maintaining
people’s mental well-being. In this domain, promising use cases
for social robots have been identified. However, current research
lacks a systematic approach for implementing, analyzing and
understanding the contribution of robotic embodiment and
proximity for persuasive and motivation technologies. In this
work, we investigate how social robots could be used, in realworld
training scenarios, to support trainers in conducting
exercises while positively motivating trainees. Two use cases
were designed to meet the needs of health and competitive
oriented trainees. Qualitative and quantitative results from a
field study show positive effects of the robot on trainees in terms
of acceptance and intrinsic motivation. In addition, our findings
highlight directions for further improvement of the design of
social robots in sports and physical scenarios.

Structural optimization of variable stiffness mechanism with particle jamming and core-frame

Accurate detection of the human body and its limbs in
real-time is one of the challenges toward human-robot
collaboration (HRC) technology in the factory of the
future. In this work, a new algorithm has been developed to
fuse thermal, depth, and color information. Starting with
the calibration of the sensors to each other, proceeding
with data fusion and detection of human body parts, and
ending with pose estimation. The proposed approach has been
tested in various HRC scenarios. It has shown a significant
decrease in errors and noise in industrial environments.
Furthermore, it produces not only a higher positioning
accuracy of the head and hands compared to the state of art
algorithms (e.g., OpenPose), but it is also faster. Hence,
such an algorithm could be joined with digital twins for
safe and efficient human-robot collaboration

Use of a Human-Centered Manual Interaction Patterns Analysis Methodology for the Specification of Dexterous Robotic Grippers

The paper addresses aspects of cooperative and uncooperative behaviour in natural language dialogue between humans and social robots. The principles of cooperation in human-human dialogues are taken as the basis for cooperative behaviour in human-robot dialogues. Several approaches are
described that can improve human-robot cooperation. These include more flexible recognition of user intents, more flexible searches using knowledge graphs, generating more cooperative
responses using semantic metadata, and generating more human-friendly responses using Wikipedia. These approaches are demonstrated in a series of videos.

Android Robots Vs Virtual Agents: which system differently aged users prefer?

People are increasingly interacting with artificial agents in social settings, and as these agents become more sophisticated, people will have to teach them social norms. Two prominent teaching methods include instructing the learner how to act, and giving evaluative feedback on the learner’s actions. Our empirical findings indicate that people naturally adopt both methods when teaching norms to a simulated robot, and they use the methods selectively as a function of the robot’s perceived expertise and learning progress. In our algorithmic work, we conceptualize a set of context-specific norms as a reward function and integrate learning from the two teaching methods under a single likelihood-based algorithm, which estimates a reward function that induces policies maximally likely to satisfy the teacher’s intended norms. We compare robot learning under various teacher models and demonstrate that a robot responsive to both teaching methods can learn to reach its goal and minimize norm violations in a navigation task for a grid world. We improve the robot’s learning speed and performance by enabling teachers to give feedback at an abstract level (which rooms are acceptable to navigate) rather than at a low level (how to navigate any particular room).

Public Perception, Privacy, Safety, and Ethical Considerations of Communication Robots in Law Enforcement

Self-disclosures can be valuable and sensitive parts of the human-robot interaction. This paper investigates how far human's tendency to self-disclose depends on the topic of interaction, individual's personality and perceived robot identity (i.e., human-, robot- or animal-like). Robot's (Pepper) identity was shown in its self-disclosure, interaction behaviors (gestures, sound and voice), and "clothing". In an "in-the-wild" study at a science festival, 80 visitors interacted with one of these robot identities. When questioned by the robot, they disclosed more about their attitudes and opinions than about other categories. Significant correlations appeared between personality characteristics and the degree of self-disclosure, as well as differences in self-disclosure categories. The different robot identities showed no effects on disclosures.

An Initial Investigation into the Use of Social Robots within an Existing Educational Program for Students with Learning Disabilities

Humans naturally produce nonverbal signals such as facial expressions, body movements, hand gestures, and tone of voice, along with words, to communicate their messages, opinions, and feelings. Considering robots are progressively moving out from research laboratories into human environ- ments, it is increasingly desirable that they develop a similar social intelligence. Therefore, equipping social robots with non- verbal communication skills has been an active research area for decades, where data-driven, end-to-end learning approaches have become predominant in recent years, offering scalabil- ity and generalisability. However, most of these approaches consider a single character, modelling intrapersonal dynamics only. In this paper, we propose a method based on condi- tional Generative Adversarial Networks, intending to generate behaviours for a robot in affective dyadic interactions. Our method takes as an input the audio of a target person together with the nonverbal signals of their interacting partner, modelled by a novel Context Encoder, to generate appropriate body gestures. We evaluate our method on the multimodal JESTKOD dataset that comprises dyadic interactions under agreement and disagreement scenarios. The experimental results show that Context Encoder can better contribute to the prediction of co- speech gestures in agreement situations.

Agree or Disagree? Generating Body Gestures from Affective Contextual Cues during Dyadic Interactions

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 414 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.

A Review and Recommendations on Reporting Recruitment and Compensation Information in HRI Research Papers

Population around Europe is getting older; many older adults would like to live independently in their homes as long as possible. Personalized integrated care plan strategies envisage
the possibility of home care services for those people at risk of frailty. In this context, the robot could empower people to live independently. In the presented work, 20 frail older adults were asked to live with a telepresence robot (i.e., Double Robot), through
which the formal caregiver could remotely visit them. A total of 284
days of field-test trial was evaluated with a proposed framework
that explores several domains, including system reliability, maintainability,
system acceptance, user attitude, and user experiences. At the end of the experimentation, the system was used for 4241 mins accomplishing 151 telepresence services, and the results underline optimal system reliability and high user experience. Based on the collected feedback, the proposed telepresence robot can be exploited into integrated care services. The evaluation of a system should envisage a complex, multidisciplinary, and holistic approach
that may influence the success or failure of the robot’s purpose if not properly analyzed during the evaluation and design phase.

A Sample Efficiency Improved Method via Hierarchical Reinforcement Learning Networks

We propose a novel framework for real-time communication-aware coverage control in networked robot swarms. Our framework unifies the robot dynamics with network-level message-routing to reach consensus on swarm formations in the presence of communication uncertainties by leveraging local information. Specifically, we formulate the communication-aware coverage as a cooperative graphical game, and use variational inference to reach mixed strategy Nash equilibria of the stage games. We experimentally validate the proposed approach in a mobile ad-hoc wireless network scenario using teams of aerial vehicles and terrestrial user equipment (UE) operating over a large geographic region of interest. We show that our approach can provide wireless coverage to stationary and mobile UEs under realistic network conditions.

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Norm Learning with Reward Models from Instructive and Evaluative Feedback

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