Italy

Trust is vital for effective human-robot teams. Trust is unstable, however, and it changes over time, with decreases in trust occurring when robots make mistakes. In such cases, certain strategies identified in the human–human literature can be deployed to repair trust, including apologies, denials, explanations, and promises. Whether these strategies work in the human robot domain, however, remains largely unknown. This is primarily because of the fragmented and dispersed state of the current literature on trust repair in HRI. As a result, this paper brings together studies on trust repair in HRI and presents a more cohesive view of when apologies, denials, explanations, and promises have been seen to repair trust. In doing so, this paper also highlights possible gaps and proposes future work. This contributes to the literature in several ways but primarily provides a starting point for future research and recommendations for studies seeking to determine how trust can be repaired in HRI.

IEEE RO-MAN 2022

A mixed capability-based and optimization methodology for human-robot task allocation and scheduling

**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

Laboratories are being increasingly automated. In
small laboratories individual processes can be fully automated,
but this is usually not economically viable. Nevertheless, in-
dividual process steps can be performed by flexible, mobile
robots to relieve the laboratory staff. As a contribution to the
requirements in a life science laboratory the mobile, dextrous
robot Kevin was designed by the Fraunhofer IPA research
institute in Stuttgart, Germany. Kevin is a mobile service robot
which is able to fulfill non-value adding activities such as
transportation of labware. This paper gives an overview of
Kevin’s functionalities, its development process, and presents a
preliminary study on how its lights and sounds improve user
interaction.

 SM-EXO: Shape Memory alloy-based Hand EXOskeleton for Cobotic Application in Cognitive Development Investigation

Learning from demonstration (LfD) approaches
have garnered significant interest for teaching social robots a
variety of tasks in healthcare, educational, and service domains
after they have been deployed. These LfD approaches often
require a significant number of demonstrations for a robot to
learn a performant model from task demonstrations. However,
requiring non-experts to provide numerous demonstrations for
a social robot to learn a task is impractical in real-world
applications. In this paper, we propose a method to improve
the sample efficiency of existing learning from demonstration
approaches via data augmentation, dynamic experience replay
sizes, and hierarchical Deep Q-Networks (DQN). After validating
our methods on two different datasets, results suggest that our
proposed hierarchical DQN is effective for improving sample
efficiency when learning tasks from demonstration. In the future,
such a sample-efficient approach has the potential to improve
our ability to apply LfD approaches for social robots to learn
tasks in domains where demonstration data is limited, sparse,
and imbalanced.

Efficacy of a Human-Robot Collaborative Problem Solving Activity to Improve Computational Thinking Skills

Social robotics recommendations have been studied for a long time, and many existing studies have addressed in-store recommendations using robots. However, it has been pointed out that many studies conducted in “in-the-wild” field environments have only focused on the initial stages of customer purchase behavior, such as stopping and engaging in a conversation, and few have been able to induce an interest in the product and even purchase. One of the causes is that the robot itself attracts most of the customer’s attention, making it difficult for customers to be interested in the robot’s recommendations. To solve this problem, this study examines the inclusion of clear and specific instructions to customers in interactions in which the robot recommends services and products. We conducted a field experiment to confirm the effectiveness of such instructive interactions, and found that customers are more likely to be interested in the content recommended by the robot, rather than in the robot itself, through the instructive interaction.

TODS: Thermal Outdoor image Dataset for Spoofing detection

The usage of social robots in psychotherapy has gained interest in various applications. In the context of therapy for children with socio-emotional impairments, for example autism spectrum conditions, the first approaches have already been successfully evaluated in research. In this context, the robot can be seen as a tool for therapists to foster interaction with the children. To ensure a successful integration of social robots into therapy sessions, an intuitive and comprehensive interface for the therapist is needed to guarantee save and appropriate human-robot interaction. This publication addresses the development of a graphical user interface for robot-assisted therapy to train socio-emotional skills in children on the autism spectrum. The software follows a generic and modular approach. Furthermore, a robotic middleware is used to control the robot and the user interface is based on a local web application. During therapy sessions, the therapist interface is used to control the robot’s reactions and provides additional information from emotion and arousal recognition software. The approach is implemented with the humanoid robot Pepper (Softbank Robotics). A feasibility study is carried out with experts from a child and youth psychiatry to evaluate the user experience of the therapist interface. In sum, the user experience and usefulness can be rated positively.

Benchmarking deep neural networks for gesture recognition on embedded devices

Mixed human-robot teams (HRTs) have the potential to perform complex tasks by leveraging diverse and complementary capabilities within the team. However, assigning humans to operator roles in HRTs is challenging due to the significant variation in user capabilities. While much of prior work in role assignment treats humans as interchangeable (either generally or within a category), we investigate the utility of personalized models of operator capabilities based in relevant human factors in an effort to improve overall team performance. We call this approach individualized role assignment (IRA) and provide a formal definition. A key challenge for IRA is associated with the fact that factors that affect human performance are not static (e.g., one's ability to track multiple objects can change during or between tasks). Instead of relying on time-consuming and highly-intrusive measurements taken during the execution of tasks, we propose the use of short cognitive tests, taken before engaging in human-robot tasks, and predictive models of individual performance to perform IRA. Results from a comprehensive user study conclusively demonstrate that IRA leads to significantly better team performance than a baseline method that assumes human operators are interchangeable, even when we control for the influence of the robots' performance. Further, our results point to the possibility that such relative benefits of IRA will increase as the number of operators (i.e., choices) increase for a fixed number of tasks.

The Robot Olympics: Estimating and Influencing Beliefs about a Robot's Perceptual Capabilities

This study explores the effects of the positioning strategy of domestic social robots on the purchase intention of consumers. Specifically, the authors investigate the effects of robot positioning as companions with as assistants and as appliances. The study results showed that the participants preferred the domestic social robots positioned as assistants rather than as companions. Moreover, for male participants, the positioning of domestic social robots as appliances was also preferred over robots positioned as companions. The study results also showed that the effects of positioning on the purchase intention were mediated by the participants’ perception of usefulness regarding the robot.

Moving Away from 'robotic' Interactions: Evaluation of Empathy, Emotion and Sentiment Expressed and Detected by Computer Systems

This paper presents a complete design and
validation of a specific case of a Human-Robot collaborative
workspace consisting of a robot arm and a human operator
working in a pick & place task in close proximity. The human
motion monitoring is made with IMU-based wearables and optical
markers, and the robot motion planning method considers safety
distance criteria as prescribed in ISO/TS 15066. This case study is
meant to illustrate the design of collaborative environments by
using the Model-based Design approach. The goal is to
systematically address the problems of safety assurance and
performance optimization, allowing the use of optimization and
machine learning approaches.

Model-Based Design of a Collaborative Human-Robot Workspace

With becoming an aging society rapidly, the digital divide among the elderly is expected to emerge as a social problem. In order to bridge the digital gap between the elderly and the general public, it is necessary to find out what difficulties the elderly have in using information and communication technology (ICT) and what technologies and services they need. In order to figure out those issues, we conducted three phases of study: ICT literacy survey, interview, and design workshop (N=10). This study has three major findings. First, from the physical view, the elderly found ICT that reduces their movement distance attractive. Second, from the cognitive perspective, services which reduce the gap between the real physical ability of the elderly and the perceived ability of them by themselves are needed. Lastly, from the psychological view, an intuitive and natural interface is needed for the elderly who are afraid of learning new technology.

Exploring ICT for the Elderly: By Analyzing the Elders’ Needs for Information Acquisition and Delivery

Social robots have been shown effective in pedagogical settings due to their embodiment and social behavior that can improve a learner’s motivation and engagement. In this study, the impact of a social robot’s motivational gestures in robot-assisted language learning (RALL) was investigated. Twenty-five university students participated in a language learning task tutored by a NAO robot under two conditions (within-subjects design); in one condition the robot provided positive and negative feedback on participant’s performance using both verbal and non-verbal behavior (Gesture condition), in another condition the robot only employed verbal feedback (No-Gesture condition). To assess cognitive engagement and learning in each condition, we collected EEG brain activity from the participants during the interaction and evaluated their word knowledge during an immediate and delayed post-test. No significant difference was found with respect to cognitive engagement as quantified by the EEG Engagement Index during the practice phase. Similarly, the word test results indicated an overall high performance in both conditions, suggesting similar learning gain regardless of the robot’s gestures. These findings do not provide evidence in favor of robot’s motivational gestures during language learning tasks but at the same time indicate challenges with respect to the design of effective social behavior for pedagogical robots.

Motivational Gestures in Robot-Assisted Language Learning: A Study of Cognitive Engagement using EEG Brain Activity

Possible applications for Social Robots in healthcare settings, that could have a tremendous social impact in helping alleviating staff workload, are those of a patient-facing role such as robot receptionist, providing assistance to patients and visitors. 
Examples of functions that such robots would need to be able to execute are greeting visitors, reception check-in/out of patients, answering common questions they may have, showing them where to sit, helping them locate missing objects, providing directions to facilities, guiding them to different locations, etc. 
In this paper we describe current progress towards developing a multimodal conversational AI system integrated in a Social Conversational Robot (an ARI robot) that will act as a receptionist in a hospital waiting room.
We present the developed architecture of the system and report on an initial experimental validation study carried out in laboratory conditions with the ARI robot. 


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A mixed capability-based and optimization methodology for human-robot task allocation and scheduling

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SM-EXO: Shape Memory alloy-based Hand EXOskeleton for Cobotic Application in Cognitive Development Investigation

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