Italy

Mobile robots must navigate efficiently, reliably, and appropriately around people when acting in shared social environments. For robots to be accepted in such environments, we explore robot navigation for the social contexts of each setting. Navigating through dynamic environments solely considering a collision-free path has long been solved. In human-robot environments, the challenge is no longer about efficiently navigating from one point to another. Autonomously detecting the context and adapting to an appropriate social navigation strategy is vital for social robots&#39; long-term applicability in dense human environments. As complex social environments, museums are suitable for studying such behavior as they have many different navigation contexts in a small space.

Our prior Socially-Aware Navigation model considered context classification, object detection, and pre-defined rules to define navigation behavior in more specific contexts, such as a hallway or queue. This work uses environmental context, object information, and more realistic interaction rules for complex social spaces. In the first part of the project, we convert real-world interactions into algorithmic rules for use in a robot&#39;s navigation system. Moreover, we use context recognition, object detection, and scene data for context-appropriate rule selection. We introduce our methodology of studying social behaviors in complex contexts, different analyses of our text corpus for museums, and the presentation of extracted social norms. Finally, we demonstrate applying some of the rules in scenarios in the simulation environment.

IEEE RO-MAN 2022

Bilevel Optimization for Just-in-Time Robotic Kitting and Delivery via Adaptive Task Segmentation 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)

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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.

Mobile robots must navigate efficiently, reliably, and appropriately around people when acting in shared social environments. For robots to be accepted in such environments, we explore robot navigation for the social contexts of each setting. Navigating through dynamic environments solely considering a collision-free path has long been solved. In human-robot environments, the challenge is no longer about efficiently navigating from one point to another. Autonomously detecting the context and adapting to an appropriate social navigation strategy is vital for social robots' long-term applicability in dense human environments. As complex social environments, museums are suitable for studying such behavior as they have many different navigation contexts in a small space.

Our prior Socially-Aware Navigation model considered context classification, object detection, and pre-defined rules to define navigation behavior in more specific contexts, such as a hallway or queue. This work uses environmental context, object information, and more realistic interaction rules for complex social spaces. In the first part of the project, we convert real-world interactions into algorithmic rules for use in a robot's navigation system. Moreover, we use context recognition, object detection, and scene data for context-appropriate rule selection. We introduce our methodology of studying social behaviors in complex contexts, different analyses of our text corpus for museums, and the presentation of extracted social norms. Finally, we demonstrate applying some of the rules in scenarios in the simulation environment.

technical paper

Sex dolls are passively jointed human-like structures. Their limited but non negligible dissemination may suggest future new modes of sexual and affective relationships with sex robots. Understanding the quality of the relationship between a owner and his/her sex doll could help us to anticipate the social consequences of future sex robots. In this framework, we propose, from available testimonies reporting a large range of behaviors including sexual relationships, destructive behaviors, or installation of the doll in the daily life of the owner, or romance with the doll generally expressed by photographies, to interpret the sex doll as a new form of transitional object in the words of Winnicott. Moreover, the hyper-sexualized appearance of the doll makes easier its fetishization as a “labor of signification” according to Baudrillard’s semiotic theory. If to cherish a transitional object in adulthood can be an indicator of depressive syndromes or borderline personality disorder, no emotional disorder is necessary linked to its private use. Further studies about sex dolls and robots could so take advantage of work that was already done with other forms of transitional objects for adults.


We are all Individuals: The Role of Robot Personality and Human Traits in Trustworthy Interaction

Enabling robots to identify when humans need
assistance is key to being able to provide help that is both
proactive and efficient. This challenge is particularly difficult
for humans eating a meal in a restaurant, a context which is
dense with interlaced social elements such as conversation in
addition to functional tasks such as eating. We investigated the
challenge of identifying human dining activities from singleviewpoint
footage by collecting and annotating the individual
activities of five two-person meals. From this process, we found
that addressing the question of identifying meal phases and
overall neediness requires identifying an underlying group state
for the table as a whole. We report on the individual activities
and group states, as well as the interdependencies between these
factors that can be leveraged to both provide and measure
effective robotic restaurant service. In addition to the insights
revealed by this dataset, we describe preliminary attempts to
create an automated classification system for these activities.

How Users, Facility Managers, and Bystanders Perceive and Accept a Navigation Robot for Visually Impaired People in Public Buildings

his study explores how to best utilize service robots when deploying them in customer facing contexts for information requests. Many companies investing in such robots unfortunately fail to integrate them successfully in a way where they facilitate meaningful interactions with users. Copenhagen Visitor Service is one of those companies who have tried to integrate a Pepper robot into their service environment but failed due to several factors relating to both the environment, the robot and lack of a strategical implementation. Through an exploratory field study with in-situ feedback from the participants, it became evident that the robot in this specific setting needed a more focused purpose and an interaction flow that was not reliant on verbal communication. A prototype was developed consisting of an informative quiz targeting families. A subsequent field deployment demonstrated a better and more meaningful user experience, indicating that careful consideration of the use context is paramount for a successful integration as well as a strategical implementation.

Ankle Intention Detection Algorithm with HD-EMG Sensor

This paper reports on a field study of more than three hundred ‘incidental’ human-robot encounters in a public outdoor space. We have used an ethnomethodologically informed ethnographic method, employing breaching experiments and membership categorization analysis to reveal how Incidentally Co-present Persons (InCoPs) interact with and make sense of robots in an urban environment.

Proactive Robot Movements in a Crowd by Predicting and Considering the Social Influence

Children with Autism Spectrum Disorder (ASD) often struggle with visual perspective taking (VPT) skills and the understanding that others might have viewpoints and perspectives that are different from their own; i.e., the ability to understand that two or more people looking at the same object from different positions might not see the same thing. The understanding of VPT can be improved by introducing explicit causal explanations in the interactions involving autistic children. Moreover, the use of social robots can help autistic children improve their social skills. We present a retrospective study with Kaspar, a humanoid social robot specifically designed to interact with children with ASD, which aims to define the initial protocol for a study on the effect of causal explanation in VPT provided by Kaspar. To this end, we investigate in which scenarios causal explanations, provided either by researchers or by Kaspar, contribute substantially to the child’s understanding of VPT. The results have helped us identify multiple interaction categories that benefit from causal explanation. We have used these results in order to define new interaction games that benefit from causal explanations. These are now progressing through usability assessment experiments.

Listen and tell me who the user is talking to: Automatic detection of the interlocutor’s type during a conversation

Thailand, as well as many other countries worldwide, is facing a shortage of medical staff. We propose a solution to improve medical services in health centres: a robot teleoperation
system to allow patients to consult with doctors from public hospitals, and for doctors to examine and make decisions about their required care. To develop such a system, a user-centred design (UCD) process is followed. Here we present an important first step in this process to establish user requirements for such a system. Hence, we have conducted a focus group with Thai
medical staff from Banphaeo General Hospital and an online survey with potential patients. An online collaborative board has been setup to facilitate running the focus group virtually and provide an effective tool to gather data. A qualitative data is then analysed using a framework analysis. Based on this work, we present a list of user requirements for doctors, patients
and assistants and discuss how the collected requirements can be transferred into technical specifications of the system. Our study found that communication among different user groups is the most important requirement.

Drowsiness prevention using a social robot

Humans are highly skilled in communicating their intent for when and where a handover would occur. However, even the state-of-the-art robotic implementations for handovers typically lack of such communication skills. This study investigates visualization of the robot's internal state and intent for Human-to-Robot Handovers using Augmented Reality. Specifically, we explore the use of visualized 3D models of the object and the robotic gripper to communicate the robot's estimation of where the object is and the pose in which the robot intends to grasp the object. We tested this design via a user study with 16 participants, in which each participant handed over a cube-shaped object to the robot 12 times. Results show communicating robot intent via augmented reality substantially improves the perceived experience of the users for handovers. Results also indicate that the effectiveness of augmented reality is even more pronounced for the perceived safety and fluency of the interaction when the robot makes errors in localizing the object.

Impacts of Teaching towards Training Gesture Recognizers for Human-Robot Interaction

The clarity of the position of the eyes and pupils in canid
species varies depending on their sociality. Existing
social robots, on the other hand, have a wide range of
applications, including education, medical care, and
customer service, while most of the robots have had a
simple design of eyes with a white base and a black iris.
However, as in canid species, it is conceivable that the
level of appropriate communication intensity varies
depending on the purpose of a robot, and that there is a
suitable eye design depending on its use. In this study, we
propose the appropriate gaze clarity of a robot for three
situations where the avatar speaks to the human, the avatar
listens to the human's talk, and the avatar is nearby the
human during the task requiring concentration.

Living one week with an autonomous Pepper in a rehabilitation center: lessons from the field

The growing presence of robots in our daily life brings out the need to develop systems that are ever more user-friendly, considering users’ needs and preferences. This is necessary in particular when robots are developed to be introduced into welfare settings. For this reason, a study is proposed with the aim to investigate differently aged (young, middle-aged, and seniors) potential users’ assessment of male android robots as opposed to male virtual agents, in order to compare interactive systems characterized by different levels of embodiment. 180 participants joined the experiment, which consisted of watching video clips depicting android robots and virtual agents, and subsequently fulfilling the RAQ (Robot Acceptance Questionnaire) and the VAAQ (Virtual Agent Acceptance Questionnaire). Results highlighted substantial differences in robots and agents’ assessment, differences which seem to be affected by participants' age, as well.

Assist Effectiveness Study based on Viscosity -Comparison of Assumed Command Signal and Actual Command Signal-

Despite already being commonplace, delegation to robotic
virtual agents (VAs) is often considered challenging and
error-prone in critical situations by the general public.
Theory of mind, the human capacity to take another
person's perspective, is deemed an important enabler for
human-human cooperation. This study explores the effect of
a robotic VA's ability to use theory of mind on users'
delegation behavior. To this end, we conducted a
between-subjects experiment with participants playing the
Colored Trails game with robotic VAs of varying levels of
theory of mind. The results invalidate our hypothesis that
the ToM level is a reliable indicator of delegation
choices. Instead, we found that the participants'
performance strongly correlates with their delegatory
intentions. Therefore, to facilitate delegation, designers
of robots and robotic agents may consider refraining from
using ToM-resemblance features and focusing on balancing
user performance perception instead to induce the desired
delegation behaviors.

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Bilevel Optimization for Just-in-Time Robotic Kitting and Delivery via Adaptive Task Segmentation and Scheduling

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We are all Individuals: The Role of Robot Personality and Human Traits in Trustworthy Interaction

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